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Instituto Tecnológico y de Estudios Superiores de Monterrey

Campus Monterrey

School of Engineering and Sciences

Towards a Selective Laser Melting Process Parameters Optimization Approach using Regression Algorithms for Inconel 718 Manufactured Parts

A thesis presented by

José Alejandro Arias López

Submitted to the

School of Engineering and Sciences

in partial fulfilment of the requirements for the degree of

Master of Science In

Manufacturing Systems

Monterrey, Nuevo León, December 2019

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Instituto Tecnológico y de Estudios Superiores de Monterrey

Campus Monterrey

School of Engineering and Sciences

The committee members, hereby, certify that have read the thesis presented by José Alejandro Arias López and that it is fully adequate in scope and quality as a partial requirement for the degree of Master of Science in Manufacturing Systems.

_______________________

Prof. Dr. David C. Romero Díaz Tecnológico de Monterrey Principal Advisor _______________________

Prof. Dr. Leopoldo Ruiz Huerta Universidad Nacional Autónoma de México

Co-Advisor _______________________

Prof. Dr. Ciro A. Rodríguez González Tecnológico de Monterrey Committee Member _______________________

Dr. Elisa V. Vázquez Lepe Tecnológico de Monterrey Committee Member

_______________________

Dr. Rubén Morales Menéndez Dean of Graduate Studies School of Engineering and Sciences

Monterrey, Nuevo León, December 2019

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Declaration of Authorship

I, José Alejandro Arias López, declare that this thesis titled “Towards a Selective Laser Melting Process Parameters Optimization Approach using Regression Algorithms for Inconel 718 Manufactured Parts”, and the work presented in it are my own. I confirm that:

• This work was done wholly or mainly while in candidature for a research degree at this University.

• Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated.

Where I have consulted the published work of others, this is always clearly attributed.

• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.

I have acknowledged all of the main sources of help.

• Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.

___________________________

José Alejandro Arias López Monterrey, Nuevo León, December 2019

@2019 by José Alejandro Arias López All Rights Reserved

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Table of Contents

Declaration of Authorship ... iii

Table of Contents ... iv

Dedicatory ... vi

Acknowledgements ... vii

Abstract ... viii

List of Figures ... ix

List of Tables ... xii

Chapter 1: Introduction ... 1

1.1 Motivation ... 1

1.2 Problem Statement ... 2

1.3 Research Questions ... 3

1.4 Research Objectives & Scope ... 4

1.5 Structure & Solution Overview ... 5

Chapter 2: Literature Review ... 6

2.1 Additive Manufacturing ... 6

2.1.1 Additive Manufacturing: Definition, Added-Value Features & Industrial Applications ... 6

2.1.1.1 Additive Manufacturing Processes for Metallic Alloys ... 10

2.1.1.2 Added-Value Features ... 15

2.1.1.3 Industrial Applications ... 16

2.1.2 Current Challenges in Additive Manufacturing ... 18

2.2 Additive Manufacturing Processes ... 18

2.2.1 Vat Photopolymerization ... 18

2.2.2 Powder Bed Fusion ... 20

2.2.3 Binder Jetting ... 22

2.2.4 Material Jetting ... 23

2.2.5 Sheet Lamination ... 24

2.2.6 Material Extrusion ... 25

2.2.7 Direct Energy Deposition ... 27

2.2.8 Hybrid ... 28

2.3 Additive Manufacturing Materials ... 29

2.3.1 Wires... 29

2.3.2 Resins ... 30

2.3.3 Powders ... 32

2.3.3.1 Polymers Powders ... 33

2.3.3.2 Powder-based Metallic Alloys for Additive Manufacturing ... 35

2.3.3.2.1 Steel ... 40

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2.3.3.2.3 Aluminium ... 42

2.3.3.2.4 Inconel ... 44

2.4 Additive Manufacturing Design Principles & Standards ... 47

2.4.1 Design for Additive Manufacturing ... 47

2.5 Simulation and Predictive Models for Additive Manufacturing ... 51

2.5.1 Simulation Models ... 51

2.5.1.1 Finite Element Analysis ... 51

2.5.2 Predictive Models ... 55

2.5.2.1 Machine Learning Techniques ... 56

Chapter 3: Towards a Selective Laser Melting Process Parameters Optimization Approach using Regression Algorithms ... 59

3.1 Traditional Approaches based on Finite Element Analysis ... 59

3.1.1 The Melt Pool ... 59

3.1.2 Residual Stress ... 60

3.2 A Novel Approach based on Regression Algorithms ... 61

3.2.1 Proposed SLM Process Parameters Optimization Approach using ML Algorithms ... 61

3.2.1.1 Part Design ... 70

3.2.1.2 Part Engineering Analysis ... 71

3.2.1.3 Part Testing... 75

3.2.1.4 Prototyping/Manufacturing ... 76

3.2.1.5 Data Analysis with R... 76

Chapter 4: Experimentation ... 78

4.1 Design of Experiments (DOE) ... 78

4.2 Limitations of the Experiments ... 89

Chapter 5: Conclusions & Future Research ... 90

5.1 Conclusions ... 90

5.2. Future Research ... 94

5.2.1 Further Experimentation ... 94

5.2.2 Further Research Opportunities: Combining FEA and ML ... 95

Bibliography ... 96

Appendix A: X and Y Coordinates for the Laser Focus (mm) ... 108

Appendix B: R Code for Data Analysis ... 112

B1. Surface Roughness Analysis ... 112

B2. Porosity Statistical Analysis R Code ... 113

Appendix C: COMSOL Best Practices Usages ... 116

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Dedicatory

To my parents, Jose and Claudia,

for your unconditional love and support,

and for inspiring me to achieve greater heights.

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Acknowledgements

To Tecnológico de Monterrey, and in particular to the Advanced Manufacturing Research Group, for the opportunity to pursue my Master in Science in Manufacturing Systems.

To National Council for Science and Technology (CONACYT) for your living expenses scholarship.

To the National Laboratory for Additive and Digital Manufacturing (MADiT) for your infrastructure and experts network.

To my professor and thesis advisor, Prof. Dr. David Romero, for your support, insights, availability, and advice in all matters of the thesis, research activity, and professional life.

Thank you for your deep and honest concern for my learning and development as both as an engineer and as a researcher.

To my thesis co-advisor, Prof. Dr. Leopoldo Ruiz, for your advice, expertise and contributions towards the elaboration of this research work; may the collaborations between UNAM and Tecnológico de Monterrey be an example of teamwork all around the country.

To my thesis committee members, Prof. Dr. Ciro Rodríguez and Dr. Elisa Vázquez, for your feedback to perfect this research work.

To my friends and classmates, whether I met you through a class or by sheer coincidence, thank you for your company, your humour and your encouragement. I wish you all nothing but the best in your future ventures.

To my girlfriend, for your love, your support and constant encouragement throughout my studies and the writing of this thesis.

Finally, to my family for your unconditional love and support in all possible matters. Thank you for encouraging me to aspire towards greater heights. This would not have been possible without you.

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Abstract

In recent decades, Additive Manufacturing (AM) technologies have received increasing interest from both academia and industry. Thanks to an unprecedented opportunity to create designs and products difficult to create through conventional manufacturing processes, such as those from subtractive manufacturing, the understanding of these processes have become imperative for the creation of reliable products. Different processes may produce parts from different materials, and from the many processes available, Powder Bed Fusion (PBF) stands out for its capacity to produce high-quality products with metallic alloys. From these metallic alloys, nickel-based superalloys are of particular interest for the aerospace and defence industry, because it possesses excellent mechanical properties during high-performance applications, such as those found in turbines, where high stresses and high temperatures bring design and engineering to its limits. Novel crystallographic structures, process complexity, and mechanical defects are but a few of the challenges AM technologies face to produce consistent and reliable parts. Selective Laser Melting (SLM), a subprocess of PBF, has been found to produce defects such as porosities and rough surfaces on additively manufactured parts, which have been found to hinder the fatigue life of as-built products.Thisresearchattemptstounderstandtherelationshipsbetweenvariablesinvolved in the SLM process and the formation of these defects. To achieve this, a literature review is realized to create a causal-loop that helps to understand the impact and correlation between the variables involved in the process, and their effect on the mechanical properties of the part. A compilation of governing equations, boundary conditions, and loads was also reviewed to allow the simulation of the SLM process on a Finite Element (FE) environment.

Finally, regression analysis is made to determine the significance of the impact the process parameters and temperature gradients determined through the FE Analysis have over the mechanical defects. Recommendations based on this analysis for optimal process parameters values are given. Further research is required to analyse the impact of process parameters on the formation of residual stresses and crack formation.

Keywords: Additive Manufacturing, Powder Bed Fusion, Selective Laser Melting, Inconel 718, Mechanical Properties.

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List of Figures

Figure 1. Classification of Standards [17] ... 6

Figure 2. Generic PBF System Schematic [41] ... 12

Figure 3. Generic Schematic of a DED Process [41] ... 12

Figure 4. Schematic of an EBM Process [51] ... 14

Figure 5. Example Schematic showing a GTAW-based WAAM Process with High-Pressure Rolling [55] ... 15

Figure 6. Framework for Understanding AM Paths and Value [64] ... 17

Figure 7. Process Flow in Data Acquisition for Custom-made Implants by AM [51] ... 18

Figure 8. Vat Photopolymerization Schematic [66] ... 19

Figure 9. Laser-PBF Schematic [70] ... 20

Figure 10. Binder Jetting Schematic [72] ... 23

Figure 11. Material Jetting AM Process Principle and Schematic [80] ... 24

Figure 12. Laminated Object Manufacturing Schematic [82] ... 25

Figure 13. Pictures of Potato Starch-based Lemon Juice Gel Printed Products: (A) Anchor, (B) Gecko, (C), Snowflake, (D) Ring, (E) Tetrahedron ... 26

Figure 14. Fused Deposition Modelling Schematic [82] ... 27

Figure 15. Lithography-based AM Technologies: (left) SLA, (centre) DLP, and Non- Lithography-based (right) Inkjet Printer ... 31

Figure 16. Free Radical Photopolymerization Procedure ... 32

Figure 17. Powder Production Processes for Additive Manufacturing [47] ... 32

Figure 18. Morphology of Commercial Powders for Polymer SLS [82]: ... 33

Figure 19. Bulk and Powder Polymer Properties and their Relationship with Process Parameters Operation and Effectiveness [111] ... 34

Figure 20. Schematic of (a) Heat-based and (b) Piezoelectric-based Actuators for the Binder Jet Process ... 35

Figure 21. (a) Gas Atomized vs. (b) Water Atomized Powders ... 37

Figure 22. Satellites on Particles Surfaces [116] ... 38

Figure 23. Average Bed Density vs. Average Sintered Density [116] ... 39

Figure 24. OM Micrographs of L-PBF 316L Microstructure Produced at Different Laser Powers: (a) 200W, (b) 170 W, (c) 140 Wm and (d) Grain Width Correlation to Laser Power Values. Z Specifies the Building Direction [119] ... 41

Figure 25. Tensile vs. Ductility for As-Deposited and Heat-Treated Samples Fabricated by Direct Energy Deposition and Powder Bed Fusion [120] ... 42

Figure 26. Micrographs from a Powder Bed Fusion Specimen Showing Common Defects in Materials Made by Additive Manufacturing: (a) Round Gas Entrapment Pores and (b) Lack of Fusion Defect [120] ... 42

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Figure 27. (a) Marangoni Convection in the Melt Pool, and (b) Oxide Disruption and

Solidification of the Melt Pool [121] ... 43

Figure 28. Ultimate Tensile Strength as a Function of Scan Speed and Laser Power [121] 44 Figure 29. Optical Micrographs of IN718: ... 45

Figure 30. Tensile and Compressive Mechanical Behaviour of SLM Inconel 718 as a Function of Porosity Level [8] ... 46

Figure 31. Types of Porosities Present in SLM Inconel 718: (a) Gas-induced; (b) Process- induced [8] ... 47

Figure 32. Brackets Before and After Topology Optimization [133]: ... 48

Figure 33. Product Assembly Simplification using AM [132] ... 48

Figure 34. Solid Model of a Water Redistribution Manifold Redesigned for AM [133]: ... 50

Figure 35. An Example to Show Interchangeable Parts in a Set of Input Models [135] ... 51

Figure 36. Electron Back Scatter Diffraction (EBSD) Analysis of a Graded Inconel 718 Sample featuring a Single Coarse Columnar Grained Zone Embedded in a Fine-Grained Matrix [57]: Inverse Pole Figure (IPF) Coloured Map of Y-Z Section ... 52

Figure 37. Thermo-Mechanical FE Model Flowchart [71] ... 53

Figure 38. Effect of Laser Energy Density (kJ/mm3) on Relative Density (%) [139] ... 56

Figure 39. Process Overview Showcasing the Melt Pool and the Marangoni Convection Within It [12] ... 59

Figure 40. Melt Pool Flow in m/s [12]... 60

Figure 41. 2D Map of the Vertical Residual Stresses at Mid-Height of a 100 mm High INCONEL 718 Pillar Structure Made by LENS [80] ... 60

Figure 42. Typical Residual Stress in Build Direction of as Printed-SLM Part Without The Substrate: (a) Cross-Sectional Residual Stress Contours Measured Using Neutron Diffraction, and (b) Predicted Residual Stress [138] ... 61

Figure 43. Overview of The Relationship between Input Parameters and Underlying Physics to Meet the Expected Outcome of Metal AM [142] ... 62

Figure 44. SLM Causal-Loop (Blue-System Inputs, Orange-Manufactured Parts’ Characteristics, Red-Resulting Properties, Green-Thermal Evolution) ... 63

Figure 45. General Workflow for ML Implementation for Optimization and Correlation ... 64

Figure 46. Experimental Causal-Loop (Blue-System Inputs, Orange-Manufactured Parts Characteristics, Red-Resulting Properties)... 67

Figure 47. Relationship between SLM Process Parameters and Material Properties in the Generation of Residual Stresses (Blue-System Inputs, Orange-Manufactured Parts Characteristics, Red-Resulting Properties, Green-Thermal Evolution) ... 69

Figure 48. Shape, Orientation and Section Division of the FEM, where (a) is the Powder Layer, and (b) is the Solid Layer ... 71

Figure 49. Mesh for the FE Model showing the Highly-Dense Upper Section, and a Coarse Section towards the Bottom of the Model ... 71

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Figure 50. Mesh Density Analysis and Maximum Temperatures for a Given Set of

Parameters [26] ... 73

Figure 51. Thermal Evolution of Inconel 718 SLM Simulated Process 175 W, 100 mm/s Parameters ... 74

Figure 52. COMSOL Non-Linear Solver to Showcase Repercussions of Increasing Maximum IterationsandJacobianUpdates,alsoshowingErrorPeaks,whichMightExplainthePresence of Outliers in Resulting Simulation Solutions ... 74

Figure 53. Movement of the Laser Beam through Time over a 400 W, 160 um Diameter, and 1000 mm/s of Scanning Speed ... 79

Figure 54. 400 W Laser Power, 1000 mm/s Scanning Speed, 240 um Diameter Simulation Case where a Maximum Temperature of 25,000 K was Reached; This Type of Data is Considered Outliers and Careful Dismissal Should Be Done ... 79

Figure 55. Reference Points for the Determination of Average Maximum Temperature for a Given Simulation ... 80

Figure 56. Temperature Evolution through Time for a Probe of Test 400 W Laser Power, 1000 mm/s Scanning Speed and 240 um Diameter ... 81

Figure 57. VED vs. Surface Roughness ... 83

Figure 58. Max Temperature vs. Surface Roughness ... 84

Figure 59. Laser Diameter and Laser Power vs. Surface Roughness Regression Plane ... 84

Figure 60. Laser Diameter and Scanning Speed vs. Porosity Regression Plane ... 88

Figure 61. VED vs. Porosity ... 88

Figure 62. The Relation between Endurance Limit and Tensile Strength for Unnotched Specimens in Reversed Bending [146] ... 91

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List of Tables

Table 1. Evolution of Metallic Additive Manufacturing Technologies ... 10

Table 2. Vat Photopolymerization Parameters [68] ... 19

Table 3. PBF Process Parameters [71] ... 21

Table 4. PBF Process Defects and Affecting Parameters [12] ... 21

Table 5. Binder Jetting Process Parameters for Sand Casting ... 23

Table 6. Available Pre-Alloyed Powders from AM Manufacturers [115] ... 36

Table 7. Comparison of Powder Production Processes [115] ... 37

Table 8. Fatigue Endurance Limits of L-PBF 316L Samples Under Different Loading and Surface Conditions [13] ... 40

Table 9. Chemical Composition of Virgin and Recycled IN718 Powders (10th Printing Iteration) [123] ... 44

Table 10. Process Parameters and Resulting Porosity and Grain Texture for a Low (250 W) and a High (950 W) Power Source [57] ... 45

Table 11. Mechanical Properties of Conventionally and SLM Manufactured IN718 ... 45

Table 12. Process Parameters for PBF Simulation [71] ... 52

Table 13. Relevant SLM Process Equations ... 65

Table 14. Equations for X-Ray CT Measurement and Characterization of Porosity and Determination of Average Surface Roughness ... 68

Table 15. Relevant Machine Learning Algorithms ... 69

Table 16. Equations for Thermo-Mechanical Analyses ... 70

Table 17. Mesh Properties ... 72

Table 18. COMSOL Variable Definitions ... 73

Table 19. Thermo-Physical Properties of Inconel 718 ... 75

Table 20. Variable and Constant Parameters ... 76

Table 21. Probe Coordinates for Maximum Temperature Determination ... 80

Table 22. Temperature (K) Evolution for Individual Probes for a Probe of Test 400 W Laser Power, 1000 mm/s Scanning Speed and 240 um Diameter ... 81

Table 23. Process Parameters, Maximum Temperatures, and Surface Roughness Data for Regression Analysis From Literature [137], Determined From FEA Simulations, and Calculated ... 82

Table 24. Statistical Analysis for the Regression Model Created by R to find the Significance of the Variables to Surface Roughness ... 82

Table 25. Maximum Temperatures and Process Parameters for Porosity Regression Analysis [137] ... 85

Table 26. Statistical Analysis for the Multilinear Regression Model Created by R to find the Significance of the Variables to Porosity ... 85

Table 27. Statistical Analysis for the Second Order Polynomial Regression Model Created by R to find the Significance of the Variables to Porosity ... 86

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Table 28. Statistical Analysis for the Second Order Polynomial Regression Model Created by R to find the Significance of the Variables to Porosity without the Consideration of Maximum Temperatures ... 87

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Chapter 1: Introduction 1.1 Motivation

Additive Manufacturing (AM) is a multi-billion dollars industry. Three decades after the first patents that catapulted the technologies capable of manufacturing three-dimensional structures, AM has increasingly captured the interest of researchers, engineers, do-it- yourself (DIY) enthusiasts, sometimes referred to as the “maker” movement, investors, and the general public. For a sense of scale, a study by the Frost and Sullivan Research Group estimates that revenues expected from this industry will grow at a Compound Annual Growth Rate of 15%, which translates to an increase in revenue of $21.50 billion USD by 2025 [1]. From the many technologies available, Selective Laser Melting stands out as a Metal Additive Manufacturing process capable of processing Inconel 718, a nickel-based superalloy widely used in the aerospace and defence sectors. Many challenges still exist before widespread application can be expected from SLM processed INC718, but its unparalleled design freedom attracts massive interest from both academia and industry alike.

Different AM technologies have proven themselves useful for different applications.

From Powder Bed Fusion’s (PBF) Biomedical, Electronics, and Aerospace applications with fine resolution and high-quality parts; Direct Energy Deposition’s (DED) retrofitting, repair, and biomedical capabilities with low manufacturing times and cost, excellent mechanical properties, and microstructural control; Material Jetting’s (MJ) fast and larger structure capabilities; or Vat Photopolymerization’s (VP) fine resolution and high quality biomedical and prototyping, to name just a few differing advantages each technology has when compared to each other [2].

As stated before, there are multiple areas of opportunity for the industry and academia to tackle before the widespread use of Additive Manufacturing, and particularly Metallic AM technologies are implemented. PBF processes, for example, are expensive and may present undesirable porosities in the manufactured part; SLM, specifically, presents a complex thermal evolution, with still little understanding of the impact the process parameters have on the final quality of the product, making it hard to simulate and predict possible resulting properties, with the added challenge of carefully looking for potential cross contaminants from poor machine cleaning or maintenance. DED on the other hand, while being more efficient time and cost-wise, presents low accuracies and poor surface quality, limiting its applications for products with very fine details, and the general crystallographic and microstructural heterogeneity of the process limits its potential repair applications as repaired pieces must be considered as having an overall performance; Vat Photopolymerization is both expensive, presents slow printing times, and very limited materials, but presents a real possibility for the printing of organic tissue, but only of the big

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and homogenous kind because of large shear forces on very small nozzles, and the lack of a more varied array of biodegradable and cytocompatibility materials; while Material Jetting struggles to maintain workability, suffers from coarse resolution, and difficulty in layer adhesion, while also sharing some of the application challenges with Vat Photopolymerization [2]–[6].

Besides what was mentioned, PBF processes suffer from time-consuming and barely in development simulation capabilities. Efforts are being made to create models that can predict resulting material properties and optimize the process; however, the total number of variables involved in the process; from the many temperature-dependent material properties of both the powder form and solidified and their interactions; the thermal evolution and melt pool formation and behaviour, with complex physical phenomena like the Marangoni Convection occurring during the melting and solidification of the powder bed;

and the characteristics of both the chamber, the gas, and the type of laser used. Overall, estimates put the number of parameters for PBF processes in over a hundred [7].

PBF, as one of the processes of Additive Manufacturing, envelops multiple techniques to process metal powders, such as Selective Laser Sintering (SLS) and Melting (SLM). The motivation of this thesis is to create a diagram, specifically of SLM, based on recent available literature that helps to establish the current relationships between the parameters of the SLM process and the resulting properties of the additively manufactured parts. The main purpose is to establish a correlation between the variation of the process parameters and the resulting on thermal gradients and mechanical properties from the process using a combination of Finite Element Analysis (FEA) and regression algorithms.

Findings from this work provides a compendium of relevant equations for the study of the SLM process, particularly of the equations that are used to define the behaviour of metal powder’s material properties; along with the predictive equations, boundary conditions, and defining laser equations necessary to simulate the SLM process in a finite element environment; simulation which will be used in conjunction with an analysis supported by regression techniques to verify the established correlations in the constructed process diagram.

1.2 Problem Statement

Ideally, a complete understanding of SLM processes, in general, would allow for both fast and enough accurate simulations, and a framework to be based from that would hasten the analysis of the process parameters’ impact in resulting properties. This would not only allow the optimization of the process to be quicker and more efficient, but it would also approach simulation capabilities to their ideal as a predictor of resulting properties.

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However, many challenges still exist for a full understanding of the SLM process.

Numerous variables and complex physical phenomena limit the applicability of SLM, particularly for high-performance applications, as very high-quality resulting parts are required for such applications.

The limit number of established correlations will hinder the efforts being made for the optimization of the SLM process [4], [8], [9]. These optimizations are necessary in order to, for example, minimize both porosities and surface roughness, two common problems for SLM manufactured pieces, both of which are common crack initiators during high- performance applications such as those required for aeroplane turbines. Simulations are indispensable to develop as they are the ones that will ultimately reduce the need for destructive tests, which are highly expensive. Regression models, on the other hand, are what could allow the determination of which parameters to optimize, based on the impact they have on the resulting properties.

A literature review should allow for the creation of a diagram that makes the connections between relevant process parameters and critical resulting mechanical properties [4], [9]–

[13]. Simulations supported by Finite Element techniques should throw some insight into the complexities and time-consuming nature due to high computational power requirements for the determination of relevant variables such as temperature. Finally, regression techniques should allow us to both verify the established correlations of the diagram and determine to what degree do the parameters and results from the simulations impact the quality of the manufactured pieces.

1.3 Research Questions

Research Questions:

RQ1.- How do the process parameters that control the SLM process affect the resulting mechanical properties of the additively manufactured product?

RQ2.- What are the predictive equations and boundary and test conditions necessary to predict the SLM process in a Finite Element environment?

RQ3.- Is there a correlation between both the process parameters and thermal gradients present during the SLM process and resulting mechanical properties?

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1.4 Research Objectives & Scope

The general objective of this research is to understand the relationship between the process parameters involved in the SLM process. Specifically, this research aims to create a causal- loop that connects the reported effects the SLM process parameters have on the resulting mechanical properties of the additively manufactured parts. Namely, how do Laser Power, Scanning Speed, Hatch Distance and Layer Thickness affect the resulting mechanical properties such as material strength, porosity, and surface roughness?

The first thesis objective, the creation of the causal-loop, will be limited to analyse the relevant correlations of the SLM process. It is not meant to be taken as a template to analyse the variables or correlations of other Additive Manufacturing technologies.

The second thesis objective, the analysis of the SLM process through finite element analysis will be limited to analyse the impact of Laser Power and Scanning Speed parameters on the magnitude of the temperatures present during the process. Other process parameters such as hatch spacing, and powder bed thickness will not be analysed; as per a literature review revealing that Laser Power and Scanning Speed are the parameters with the most impact on the characteristics of resulting mechanical properties.

The third thesis objective, the regression analysis, will be limited to determine the magnitude of the impact Laser Power and Scanning Speed parameters, and determine if the variation of temperatures obtained from the Finite Element Analysis (FEA) have an impact at all in the resulting mechanical properties.

After constructing a workflow diagram (see Figure 45) and identified the necessary predictive equations, boundary and test conditions, and material properties to simulate the SLM process in a finite element environment are to be compiled and applied in the software COMSOL, with the specific objective of determining the temperatures present during the process at varying laser powers, scanning speeds, and beam diameters from recorded parameters and measured surface roughness.

Having created the diagram and simulated the process at varying process parameters, regression models are to be used to determine if the determined correlations from the causal-loop are correct, and find out if the variation of temperatures found from the second objective has a significant impact on the variation of resulting surface roughness and porosities, in order to determine if process temperatures can be used as a control variable for the process.

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1.5 Structure & Solution Overview

This thesis is divided into five chapters. The content of these chapters is divided as follows:

Chapter 2 contains the literature review for additive manufacturing concepts, benefits, importance, and current challenges, along with the different processes and materials AM envelops, with a strong emphasis on Selective Laser Melting technology from the Powder Bed Fusion process.

Chapter 3 contains the developed causal-loop and the compendium of predictive equations, boundary and test conditions and material properties for the finite element model of the SLM process.

Chapter 4 contains the Design of Experiments for the Finite Element Simulations, along with the material properties, process parameters and resulting mechanical properties used for the simulations. It also contains the resulting thermal gradients for the different tests and the corresponding regression analysis equations used.

Chapter 5 contains the results from this research and the conclusions and suggested future work based on the obtained results.

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Chapter 2: Literature Review 2.1 Additive Manufacturing

2.1.1 Additive Manufacturing: Definition, Added-Value Features & Industrial Applications

AM is not a single process. It is the result of the amalgamation of multiple technologies integrated into individual technologies, each of which possesses clear advantages and limitations in applicability, available materials, reliability, and cost. Careful consideration of multiple factors must be taken care of in order to select the most appropriate technology for a specific application. Though this work will mainly focus on the manufacturing of parts through the Additive Manufacturing of Metals, also referred to as Metal Additive Manufacturing (MAM), Metal 3D printing, or 3D Printing of Metals, a review of the technologies that have allowed the creation of this technologies is necessary for the understanding of the technology. Most of the current standards for AM are being developed by a conjoined effort of ISO and ASTM, with SAE generating standards for the aerospace industry, and NASA providing general guidelines (see Figure 1) [14]–[17].

Figure 1. Classification of Standards [17]

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Historically, the Institute for Defense Analyses (IDA), through its Science and Technology Policy Institute (STPI), in its 2013 report of the “Origin and Evolution of Additive Manufacturing in the US” [18], divides the advent of AM into three eras.

The first era, the early history of AM technologies before 1984, was when two important motivators and four precursor technologies both motivated and allowed for the development of AM. The motivators, specifically, are photo sculpture and topography, both of which originated in the 19th century. Photo sculpture, a process where photographs are taken from an object or subject from multiple points of view to serve as a guide for the hand- sculpting of a clay figure, evolved from Francois Willèmes’ 24 camera replication of a sculpture in 1860 [19], to a method for photo-recording of phenomena in three dimensions using a photosensitive medium developed by Otto John Munz in 1951 [20]. Topography, on the other hand, evolved as a need for cartographers to represent three-dimensional data. It started with the use of moulds and wax plates to create contour relief maps in 1862 [21], to the development of lamination techniques for the manufacturing of more complex steel parts [22]. Both are regarded as the precursor ideas that motivated further development of AM technologies.

Furthermore, the development of other technologies allowed the steady progress of AM applicability and academic understanding. The advent of computer technology and its widespread availability has significantly contributed to both individual and group effort development of AM. One example is the ISO/ASTM international standards currently being developed by the conjoined efforts of both committee F42 for Additive Manufacturing of ASTM, and the International Standards Organization’s (ISO) Technical Committee (TC) 261 for AM.

Increased computing power brought with it more powerful software, software that has seen widespread integration into the manufacturing and product development industries through Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), Computer-Aided Manufacturing (CAM), and Computer Numerical Control (CNC). Together they have increased the understanding and allowed the integration of different the concepts surrounding AM.

CAD software (Solidworks, Unigraphics, CATIA, ProEngineer, AutoCAD, Inventor) has evolved from simple code-generated 2D surfaces to more user-friendly graphical interfaces with complex surface creation capabilities. Through the last three decades, CAD files for AM have been defined by the Stereo Lithography file format (*.stl), which was developed in 1987 by 3D Systems, for the simplification of geometries through its representation using a triangular mesh generated by tessellation of the boundary surface of the 3D solid [23].

Currently, a format file called the additive manufacturing file (*.amf) is being developed by ISO and ASTM as the standard ISO/ASTM 52915:2016 (to be withdrawn and replaced by

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ISO/ASTM DIS 52915) because of the need to represent colour, texture, multi-materials, substructure and other properties in modern and more complex AM machines [24].

CAE software is a general term used to describe engineering techniques, particularly numerical techniques, that support the solution of engineering problems, and are especially useful where analytical solutions are time-consuming. In AM, CAE is particularly useful for studying the heat transfer and geometrical behaviour of the melt pool generated during the melting of materials using the Finite Element Method (FEM) [25], [26], topological optimization of mechanical systems for increased performance efficiency [27], [28], and Residual Stress prediction and controlling for the prevention of Mechanical Stress Distortions [29], [30]. More details about Residual Stress and its influence in preventing the widespread implementation of Metallic AM will be discussed further in this work.

CAM and CNC software refer to those that are used for the control of the manufacturing step of a part and generally refer to the creation of M and G codes used to control the movement of the mechanisms that realize the lathing, machining, or, in the context of AM, the movement of a nozzle in an AM process, for example. Of particular importance for the AM field, is the proven inclusion of Machine Learning (ML) algorithms for the optimization of machining processes [31], and are being implemented in the analysis of different aspects of AM process behaviour, such as geometric tolerance and distortion prediction [32], [33], and holds potential in parameter optimization of AM processes [34].

Further discussion and description of ML techniques and their applicability to AM will be presented in this work.

The second era, which encompasses what is referred to as the development and commercialization stage of AM technologies, between 1984 and 2006, saw the birth of the patents regarded today as those who helped jumpstart the Additive Manufacturing industry.

Multiple patents were filed for during this era, however, only four are regarded as the foundation of the AM field. The first patent, filed in 1984 by Charles Hull [35], describes a system for generating a three-dimensional object by forming successive, adjacent, cross- sectional laminae of that object at the surface of a fluid medium using stereolithography (SLA) to alter the physical status of the medium. The second patent, filed in 1986 by Carl Deckard, describes a method and apparatus for producing parts by Selective Sintering (SLS), where a laser selectively sinters target areas, layer by layer, of the deposited material [36].

The third patent, filed in 1988 by S. Scott Crump, describes an apparatus and method for creating 3-Dimensional Objects, by dispensing a material at a controlled rate from dispensing head unto a substrate or base member in a predetermined pattern dictated by the shape of the article to be formed, and where the material is dispensed layer-by-layer until the whole article is formed [37]. The fourth patent, filed in 1989 by Emmanuel Sachs, John S.

Haggerty, and Paul A. Williams as a collaborative effort between professors and graduate

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students, developed the binder jetting process, where a powdered ceramic, plastic, or metal, deposited in sequential order one on top of the other [38].

The invention of Hull’s patent brought with it the creation of 3D systems, who developed the widely used STL format. Subsequently, multiple companies were created thanks to the creation of AM technologies, such as Japan’s NTT Data CMET and Sony’s versions of SLA in 1988 and 1989, or Germany’s Electro-Optical Systems (EOS), who sold its first stereolithography machine in 1990 [39].

The third era, 2007 and forward, marks the beginning of the current AM era, where multiple technologies have been created or upgraded, and standardization efforts are being made to facilitate the union of efforts between research groups and industry for the understanding of the AM processes and their integration into the supply chain.

Standardization has seen a constant evolution as the committees of the different Organizations (ASTM, ISO, ASME, AINSI) constantly withdraw, renew and publish standards.

The committees F42 and TC261 from ASTM and ISO were born in 2009 and 2011, respectively, and their first efforts were focused on establishing a baseline for the different concepts surrounding the rapidly growing AM industry.

Some of the terms first used to describe the processes created in the foundational patents have been continuously used to describe the general process of AM technologies, particularly the layer by layer and three-dimensional descriptions. ASTM F42 described Additive Manufacturing in the 2013 standard ASTM F2792-12a as “a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies”. The joint efforts of ASTM and ISO had the consequence of making the more popular term 3D printing a subset term of the general AM industry, more focused on cheaper and commercially available technologies. It should be noted that, unlike the foundational patents, ASTM did clarify a fundamental difference between the technologies versus their subtractive counterparts, where the use of the words additive and subtractive distinguish both technologies nature, where one selectively adds material, and the other one selectively removes it, be it by drilling, machining, turning, electrical discharge machining (EDM), or any other process. It also recognized other popular synonyms of AM, such as additive fabrication, additive processes, additive techniques, additive layer manufacturing, layer manufacturing, and freeform fabrication.

As the technology evolved, and ISO and ASTM came to an agreement of joint cooperation, the newest attempt from both organizations to define Additive Manufacturing brought the currently active standard definition, ISO/ASTM 52900:2015, which is to be withdrawn and replaced by ISO/ASTM DIS (Draft International Standard) 52900, where it is currently defined as a “process of joining materials to make parts from 3D modelling data, usually layer upon layer, as opposed to subtractive manufacturing and formative

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manufacturing methodologies” [40]. This terminology now adds the differentiation of AM to formative manufacturing, where processes such as forging, ending, casting, injection moulding, conventional powder metallurgy and ceramic processes, where no addition of subtraction generally represent them, are included.

2.1.1.1 Additive Manufacturing Processes for Metallic Alloys

Though polymers and some ceramic resins are recognized as the most widely used and understood AM technologies, Additive Manufacturing of Metals Processes for Metallic Alloys (Metallic AM) is constantly gaining traction thanks to the development of Metallic AM technologies and interest of its implementation by companies around the world such as Renishaw, General Electric, and Airbus.

The first mention of metallic alloys materials being implemented for the creation of parts through an Additive Manufacturing process is the commercialization of a Laser- Engineered Net Shaping (LENS) metal powder system in 1998 by Optomec [39], which was based on the technology developed at Sandia National Labs. As the technologies evolved, more companies commercialized metal processing AM machines through the years, as observed in Table 1.

Table 1. Evolution of Metallic Additive Manufacturing Technologies

Year Technology Company Country of Origin

1998 LENS Optomec USA

1999 ProMetal RTS-300 Controlled Metal Build-up (CMB)

Extrude Hone AM (Now Ex One)

Roders

USA Germany 2000 Direct Metal Deposition

(DMD) Precision Optical

Manufacturing (POM) USA

2001 Stereolithography (Polymer, Ceramic,

Metal Pastes) Direct Steel 20-V1

OptoForm (acquired by 3D Systems) Electro-Optical Systems

(EOS)

France

Germany 2002 Phenix 900 (Solid-Phase

Sintering)

Phenix Systems France

2003 EOSINT M 270 (Direct Metal Laser Sintering) Tumaform LF (PBF-L)

EOS Trumpf

Germany Germany

2004 RX-1 (DMD)

M1 (laser-melting) POM

Concept Laser USA

Germany 2008 Stainless PH1 (direct

metal laser-sintering) EOS Germany

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2009 M series (capability for Inconel 718 and Al-Si10-

Mg)

EOS Germany

2010 Magics Material SG (Metallic AM support

software) Laser Metal Deposition Cobalt-Chromium DMLS

(for Dental) DMLS + Conformal

Cooling + EDM

Materialise

EasyCLAD Systems Renishaw EOS + GF AgieCharmilles

Belgium

France UK

2011 SLM 280 HL (Selective Laser Melting) LUMEX Avance-25

SLM Solutions Matsuura

Germany Japan 2012 DMLS of Precious Metals EOS + Cookson Precious

Metals Germany + UK

2015 Renam 250 SLM Renishaw UK

Though multiple processes of Additive Manufacturing exist, only some of them are available for processing metallic alloys. How these technologies are classified vary by author, and the process themselves might be referred to as their commercial names. In general, however, they can be classified depending on the heat source they use, the type of feedstock, and whether it is pre-placed for deposit. Processes capable of handling metallic alloys for AM applications include Powder Bed Fusion (PBF), Direct Energy Deposition (DED), Extrusion- based AM Composite Extrusion Modelling (CEM), Binder Jetting (BJ), Cold Spraying, Friction Stir Welding, Direct Metal Writing, and Diode-based processes. PBF systems include more commercially known systems such as Selective Laser Melting (SLM), Electron Beam Melting (EBM), and Direct Metal Laser Sintering (DMLS); while DED systems include LENS, for powder-based systems, and wire-feed-based systems, such as those based on welding plasma arcs (WAAM), and Electron Beam Free Form Fabrication (EBFFF) [2], [14], [41]–[45].

In Powder Bed Systems, a powder bed is dispersed over a work area with a rake.

Afterwards, a heat source is used to selectively sinter or melt the powder. When the layer has been cooled, the working area platform is lowered, and a new powder layer is added.

The process repeats until the part has been fully formed. The process is realized in an enclosed chamber, and an inert gas is used to prevent unwanted chemical reactions, such as oxidation, to occur during the sintering, melting and vaporizing processes, which would be detrimentaltofor the final part. A generic schematic of the PBF process is shown in Figure 2.

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Figure 2. Generic PBF System Schematic [41]

In Direct Energy Deposition (DED) systems, the melting and feeding of material occur simultaneously, as opposed to PBF systems, where the powder is applied first, and then the heat source is applied to melt the material. Instead of using argon to chemically protect the process, it is done by using an Nd:YAG diode or a CO2 laser, while the feedstock material, which can be either a powder or a wire, is provided by a coaxial nozzle or a multi-jet one [46]. Advantages of these metal deposition systems include larger build volumes and the capability of working on to-be refurbished and/or damaged workpieces. In these type of systems, either the workpiece remains stationary while the nozzle or nozzles move, or vice versa [41]. A generic schematic of the DED process is shown in Figure 3.

Figure 3. Generic Schematic of a DED Process [41]

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All the mentioned processes require a heat source for the melting of the material, some of which is lost before it reaches the material, some is reflected, and some is lost by radiation. Be it a Laser, an Electron beam, Electric /Plasma Arc, or even hybrid heat sources, the general process involves selectively melting the feedstock material in a manner controlled by a computer until the final shape is created [47].

Lasers generate a high energy density beam which heats and, subsequently, melts and even vaporizes the metal in question. It can provide a small spot size, resulting in a smaller and faster heat and cooling melt pool, allowing the generation of finer grain sizes, contributing to improved mechanical properties. The high temperatures allow the processing of refractory materials, and those whose mechanical properties make their machining difficult [48]. The general equation that describes the volume energy provided to the powder layer of PBF laser system is defined in Equation 1.

𝐸𝐸𝑉𝑉 = 𝑃𝑃𝐿𝐿/(𝑉𝑉𝑆𝑆∗ ℎ𝑆𝑆∗ 𝐷𝐷𝑆𝑆) (1)

Where 𝐸𝐸𝑉𝑉 is the Volume Energy provided, typically <100 J/mm3 for SLM; 𝑃𝑃𝐿𝐿 is the Laser Power, with values typically ranging from 20W to 1kW, 𝑉𝑉𝑆𝑆 is the Scan Speed of the laser, with values up to 15 m/s; a spot size with a focal plane between 50μm and 180μm, depending on the system; and ℎ𝑆𝑆 is the overlapping hatch distance, all working with a layer thickness 𝐷𝐷𝑆𝑆 of 20μm -100μm [46].

In Electron Beam systems, more commonly referred to as Electron Beam Melting (EBM), a rapidly scanned electron beam with high power density is used to selectively melt a powder layer in a controlled vacuum and heated chamber. The electrons are used by applying a high voltage across a grid cup and anode. A negatively charged cathode is heated to emit electrons in a process called thermionic emission. The electrons are accelerated and focused by the grid cup into a work chamber with a vacuum with a pressure lower than 10^- 2 Pa. The focusing occurs using electromagnetic coils and directed using magnetic deflection coils [47]. Typical process parameters include a layer thickness of 50 μm to 200 μm, scanning speeds between 10^2 and 10^4 mm/s, where the lower values are used to ensure complete melting of the material [46]. EBM processes have shown multiple advantages versus its laser (SLM) counterpart, especially regarding the processing of copper-based alloys, where SLM has shown poor energy absorption, and generally reducing the residual stress of parts when compared with standard laser processes [49], [50]. A generic schematic for the EBM process is shown in Figure 4.

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Figure 4. Schematic of an EBM Process [51]

Electric arcs provide a high energy density heat sources by a stream of electrons transferred through the arc, though at a lower energy density than Electron Beam, and its characteristics can vary depending on current flow (AC, DC+, DC-) [47]. It is more commercially known as Wire Arc Additive Manufacturing (WAAM), and its advantages include its capacity to work larger metal components than other metallic AM processes, high deposition rate, and lower equipment costs, but presents several limitations because of the inherently non-equilibrium thermal process associated, creating hard to predict mechanical properties [52]. The anisotropy of parts’ mechanical properties and high residual stresses and distortion originated from the processes high heat input are also factors that limit the use of this process, but it has been proven useful for large part manufacturing where machining will be used to ensure quality, and different variations of the technology, such as Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), Plasma Arc Welding (PAW), and Cold Metal Transfer (CMT), have been used in the manufacturing of aluminium, titanium, steel, and nickel superalloys [53], at the cost of lower feature resolution (distortion) [54]. An example schematic of a GTAW-based WAAM process with a rolling process is shown in Figure 5.

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Figure 5. Example Schematic showing a GTAW-based WAAM Process with High-Pressure Rolling [55]

2.1.1.2 Added-Value Features

Additive Manufacturing offers several value-added features which have helped increase commercial and academic interest in its integration into the manufacturing supply chain.

Here we name the most relevant and promising features [47]:

Mass Production of Personalized (Customized) Items. Customization would allow the production and distribution of goods on-demand. The combined use of Information and AM technologies would allow clients to obtain products tailored for their specific needs, for industries not limited to manufacturing. For example, an AM supplier could provide tooling and fixtures with specific characteristics for a manufacturing plant; replacement components for jewellery pieces could be fabricated on-site. Custom Prostheses are already being widely fabricated through AM technologies [56].

Functionally Graded Structures and Intermetallic Materials. Functionally grading a material refers to engineer specific sections of a part to exhibit properties different from the rest of the manufactured component. Controlling such properties at a microstructural scale is in its early stages of research, however, its feasibility has been proven in cases such as Inconel 718 by variating process parameters and analysing the resulting orientation of the grain structures [57]. Grading of AM Metallic components also shows potential use in the development of biocompatible metallic implants [58]. Another example would be the use of different metal layers to provide specific properties to a component, such as harder outer layers but stronger inner parts for impact or grinding processes.

Freeform geometry. Understandably the most promising, and already highly valued in polymer and ceramic materials, a value-added feature of Metallic AM is the elimination of the design constraints current manufacturing technologies for metals such as wrought, cast and forming metals suffer. The creation of complex structures, the potential of material saving, and using current materials for higher performance applications for multiple industries is the main reason the current challenges of Metallic AM are thoroughly being researched by both academy and industry [2], [14],

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2.1.1.3 Industrial Applications

Having established the future increase in revenues derived from AM technologies and current interest of both academy and industry, how some of those industries are to benefit from these technologies will provide an insight of specific areas of interest and potential case studies. There are potential applications for multiple industries including, but not limited to:

automotive, medical devices, orthopaedics, thermal control, aerospace, aeronautical, space launch and space flight, arts, and jewellery and precious metals industries [60].

Aerospace

The low-volume but high-cost of aerospace components derived from both processes and materials makes AM of Metals a strong candidate as a solution to those issues thanks to its on-demand production capabilities and low lead times. A case study by EOS and Airbus found that the use of AM technologies reduced CO2 emissions over the entire part lifecycle, total titanium scrap, and overall weight. The aerospace industries would also highly benefit from potential thermal control through the design of internal fluid flow for heat extraction or novel structures for heat dissipation of turbine blades [61], as the basic understanding of

“The Hotter the Engine, The Better” [62], as current engine temperatures are reaching the limit of material capabilities. The combination of complex heat dissipating structures, refractory materials, intermetallic materials, and fluid flow where it was not possible before are potential solutions to these issues.

Automotive

The Additive Manufacturing industry is currently in the process of disrupting the automotive industry, specifically as a source of product innovation opportunity and a driver for supply chain transformation. AM technologies give a competitive edge to automotive industries thanks to their capacity to allow faster development cycles, part consolidation, lightweight, and geometries that were impossible to achieve before, with industry savings projected in the millions of dollars depending on the scale of the application [63].

The most important aspect AM adoption has, is its capacity to break two fundamental performance trade-offs: capital versus scale, and capital versus scope. What this means is that AM lowers the minimum efficient scale required to justify the production of a product, and its flexibility increases the scope or “variety” of products that can produce added value for the company. This break can be done through four distinctive paths (see Figure 6). The Path I refers to a company not radically changing the products themselves or the supply chain that enables their creation but using AM technologies to improve the value of current products. Path II refers to the transformation of the supply chain, where companies can benefit from components on-demand as they are needed in their manufacturing process.

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Path III refers to using AM technologies to innovate on their products, such as the creation of geometrically complex products that drive weight reduction, for example. Path IV refers to an alteration of both supply chain and products themselves, with the objective of creating an environment where customization and added-value features drive a new business model [64].

Figure 6. Framework for Understanding AM Paths and Value [64]

In a more practical sense, AM technologies would allow for flexible and optimized designs, whose production can be rapidly scaled up. It can provide manufacturing plants with on-plant tooling creation capabilities. High and fast customization capabilities, which were not available before due to the low scale of their demand, will now be available to enhance customer satisfaction (see Figure 7), with the possibility of custom made products, such as implants made by AM (see Figure 7) [65].

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Figure 7. Process Flow in Data Acquisition for Custom-made Implants by AM [51]

2.1.2 Current Challenges in Additive Manufacturing

From the available Additive Manufacturing processes technologies, which will be described in Section 2.2, very few are available for Metallic AM. According to Herzog et al. [46], the most relevant technologies for Metallic AM are Laser Beam Melting (LBM), also known as Selective Laser Melting (SLM), Electron Beam Melting (EBM), both of which are Powder-Based Fusion (PBF) based, and Laser Metal Deposition (LMD). This work will focus on the study of the SLM process.

2.2 Additive Manufacturing Processes

ISO/ASTM 52900:2015 divides Additive Manufacturing into seven processes or categories, each of which present different available materials to work with, variations in processing, and energy sources. In this Section, their general definitions, primarily based on how the international standard defines it, the general procedure of the process, available materials, advantages, limitations, industrial importance, and current challenges will be discussed.

2.2.1 Vat Photopolymerization

Vat Photopolymerization is an Additive Manufacturing process in which liquid photopolymer in a vat is selectively cured by light-activated polymerization. During the process, a platform moves downwards after each layer of acrylic or epoxy-based liquid

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photopolymer is cured using a laser in Stereolithography (SLA), or a projector during Digital Light Processing (DLP). Its advantages include a high accuracy in the micron scale, but is very limited regarding the available materials, and requires heavy use of support structures.

Attempts to implement ceramics in the process have been made, but the sensing of the process is complicated due to the presence of the ceramic particles. It has gained widespread attention from biomedical researchers for the creation of tissue scaffolds for regenerative medicine thanks to its high precision capacity, and potential use in drug delivery and wound dressing using cytocompatible materials (compatibility with cells), though limitations of available materials limit current research and applications. Studies in build orientation and material-process relation and optimization for the determination of mechanical properties have helped advance the technology, but further studies are required to expand on available materials [5], [66]–[69]. A generic schematic of vat photopolymerization process is shown in Figure 8, and a table of typical process parameters can be seen in Table 2.

Figure 8. Vat Photopolymerization Schematic [66]

Table 2. Vat Photopolymerization Parameters [68]

Factor Description Units

Anti-Alias Edge smoothing -

Variable Strength Exposure Light power -

Part Rotation (Spin) Rotation vs. Longitudinal Axis -

Layer Thickness Height variation between layers mm

Wait (before Exposure) The time between exposure of new layers S

Exposure Time Light exposure time for curing S

Separation Slide Velocity Resin tray rotation velocity Rpm Z-axis Over-lift Distance platform is raised between layers mm Separation Z-axis Velocity Velocity of z-axis over-lift mm/s Approach Slide Velocity Opposite rotation of separation slide velocity Rpm

Approach Z-axis Velocity Platform descent speed. mm/s

Part Orientation Alignment of part with respect to the axis of the printer -

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2.2.2 Powder Bed Fusion

Powder Bed Fusion is an Additive Manufacturing process in which thermal energy selectively fuses regions of a powder bed (see Figure 9). It is the basis categorical process of this work and will be more extensively discussed in other sections. It is widely used for the processing of metallic materials, making it one of the few technologies available for Metal Additive Manufacturing (MAM). Like other AM processes, the process begins with an STL file of a 3D model to be printed. Support structures may be needed to ensure a quality printing, especially for Heat sources for PBF processes are either laser or electron beams, and the fused material generates a melt pool of said material, which is a part of a process difficult to control that involves fluid flow and thermo-mechanical analysis within the liquid and vaporized sections of the beam-material interactions [40], [47].

PBF processes have attracted the attention of multiple industries, such as aerospace, defence, automotive, energy, medical, and even jewellery, thanks in part to a wide range of available metallic materials; such as steel, titanium, aluminium, cobalt, chrome, and nickel superalloys for different technologies like Selective Laser Melting (SLM), Sintering (SLS), Heat Sintering (SHS), Direct Metal Laser Sintering (DMLS), and Electron Beam Melting (EBM) [67], [70].

However, multiple challenges and process limitations are to be addressed before the technology can be applied in high-performance applications. Geometrical and dimensional inaccuracies, poor surface quality, microstructural imperfections, and hard to predict mechanical properties are some of the factors that limit the applicability of the process.

Controlling process parameters (see Table 3) is imperative for the minimization of these defects (see Table 4) [12].

Figure 9. Laser-PBF Schematic [70]

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Table 3. PBF Process Parameters [71]

Process Parameters Units Power of Heat Source Watts

Type of Heat Source Gaussian Distribution

Scanning Speed mm/s

Scanning Pattern Zigzag

Scanning Direction XYZ axis Printing time (by layer) S

Hatch Spacing μm

Spot Size μm

Layer thickness μm

Ambient temperature μm

Efficiency <1

Table 4. PBF Process Defects and Affecting Parameters [12]

Defect Category Specific Defect Affecting Parameters

Geometry and Dimension Geometry Inaccuracy (form dimensional deviations) Staircase Effect

Machine Error Parameters Dimensional Inaccuracy (size dimensional deviations) Shrinkage

High Layer Thickness Laser Positioning Error Platform Movement Error Thermal Variations Scanning Speed Spot Diameter Surface Quality Surface Roughness

Morphology Balling

Surface Deformation Warping

Scan Strategy Scan Speed Scan Pattern Re-melting Hatch Space Spot Size Energy Density Compacted Powder

Microstructure Anisotropy

Heterogeneity

Porosity

Scan Direction Layer Orientation Powder Conditioning Scan Strategy Energy Density Temperature Laser Power Scan Speed Spot Size

Powder Characteristics

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Mechanical Properties Fractures Cracks Holes

Inadequate Layer Bonding Porosity

Heat Penetration Scan Overlap Powder Deposition Gas Flow Direction

2.2.3 Binder Jetting

An additive manufacturing process in which a liquid bonding agent is selectively deposited to join powder materials. A counter-rotating roller or a wiping blade spread a new layer of powder on the surface of the prior layer, and the process is repeated until a green part is completed (see Figure 10). The process is finished by post-sintering of the green part in a high-temperature furnace, where the binder is burned off and the powder particles are sintered through atomic diffusion [24], [72].

It can process a wide arrange of materials, such as gypsum, sand, metallic, ceramic, and polymeric. It is a fast, simple, relatively cheap AM technology, which prevents the formation of residual stresses due to lacking high energy gradients. It does not require support structures or environmentally controlled chambers and is compatible with highly reflective materials. However, it has proven difficult to achieve full density parts, which leads to reduced mechanical properties. Moreover, it is unable to process fine powders due to low flowability and powder agglomeration [67], [72].

Even with limitations, Binder Jetting has proven useful for sand casting, with multiple studies evaluating the resulting moulds’ properties and their effects on the cast metals, design accuracy and capabilities, and material issues. Attempts to integrate two AM technologies (Hybrid Technologies), specifically Material Extrusion and Powder Bed Binder Jetting, have also been studied to determine the feasibility for the fabrication of silicon structures. Though research has highly focused on the effects of curing parameters on the resulting mechanical properties of the mould, printing parameters also have a measurable effect, but few investigations have been made so far [73]–[75].

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