Capítulo 1: Fundamentación Teórica
1.5 Bibliotecas de visualización
The manufacturing network in this dissertation is a realistic implementation of an operating system for cloud manufacturing based off java-script web development tools. The operating system is capable of acquiring technical, statistical, and logistic data, in real time, from customers,
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manufacturer providers, and manufacturing resources. This opens the possibility for knowledge databases feeding artificial intelligence algorithms that perform decision-making task in manufacturing such as reducing the technical challenges of capturing a manufacturing resource, selecting process parameters for a manufacturing process, and, ultimately, minimizing the amount of human interaction to expedite manufacturing transactions. Alternatively, this data can be mined to support both, real-time and longitudinal decision making, seamlessly bridging the gap between real-time control of processes executed on the machine, computational multi-physics models executing in super computers, and artificial-intelligence and machine-learning system used in planning processes and operations. The architecture of this platform can be enhanced to support predictive control of manufacturing process, in-situ corrections, automated process monitoring and process control.
The area of cloud Numerical control (Cloud NC) is also proposed as promising area of future research. Intimately related with the numerical control as a service architecture in this dissertation, cloud NC is the missing piece/natural progression in the digital-manufacturing (industry 4.0) revolution that allows computer-numerical-control to be distributed as a shared service from distant servers, and automated machinery to be the essential hardware-clients, executing the motion. The basic idea is that with a reasonable internet connection speed (0.2Mbps – 0.4Mbps) and good communication interfaces across the web (e.g. web-sockets), the NC tasks of numerical codes interpretation, and interpolation of motion, can be carried by a remote, cloud server, while the tasks of programmable logic and servo control are locally executed , by a, light, embedded controller. In this scheme, the cloud controller interprets the NC commands and processes them, with the help of offline trajectory-generation algorithms, into a stream of compiled motion specifying the reference pulses of the axes at every clock-cycle of the machine-embedded controller. Outsourcing a significant part of the control task to the cloud results in a technology with reduced complexity, lower fixed-investment on the machine and maintenance costs.
Additionally, offloading the tasks of NC interpretation and trajectory generation to the cloud creates the opportunity of implementing computing-intensive control algorithms that specify trajectories down to the precision of the machine. This results in detailed data streaming and mining on how the CNC systems behaves, that can be accessed ,in parallel, by web services to perform computing intensive task such as real-time-process analysis and simulation.
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Cloud manufacturing and cloud numerical control define new areas of research that tackle the problems of elasticity and elasticity of numerical computer farms (NC farms). This involves developing network hypervisor programs that create instances of numerical control as a service (NCaaS) to meet the real-time demand of the cloud-manufacturing networks, and secure high-speed socket interfaces between node servers and client adapters to handle growing data-transfer requirements.
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APPENDIX A
The polynomial equation representing the forward position kinematics of the manipulator, with design parameters of r = 2.5 mm, L1 = 1, L2 = 0.7071, is given by:
72.0 653.6 10.0 10.0 10.0 16.24 2.0 10.0 16.24 2.0 2.0 162.4 10.0
144.0 2.000 7.000 7.000 54.14 ( 7.000 4.000 27.
432
7.000 108.3 435.7 2.000 2.000 54.14 435.7 54.14 7.000
7.000 108.3 435.7 1158.)
12.00 4.000 4.000 7.000 7.000 48.73 2.000( 24.00 9.000 37.90 4.000 )
24.00 341.1 1311.) 2.000( 14.00 9.000 37.90 4.000 4.000 9.000 146.2 652.6 14.00 341.1 2610. 6947.) 4.000 4.000 194.9 2622. 13890. 4.000 48.73 7.000
12.00(4.000 1.000 1.000 5.414 4.000 3.000 37.90 4.000 3.000 37.90 131.9
21.66 527.6 4675. 18280. 4.000 5.414 1.000 4.000 3.000 37.90 131.9
2.000 6.0
00 129.9 915.4 2337. 28550.*2.000( 6.000 12.00 10.83 1.000
12.00 194.9 915.4 6.000 129.9 915.4 2337.)
2.000( 2.000 2.000 37.90 4.000 12.00 194.9 915.4
4.000 2.000 48.73 389.7 1152. 2.000 75.80 915.4 4610. 9139.)
2.000 6.000 12.00 10.83 1.000 12.00 194.9 915.4 6.000 129.9 915.4 2337.))
160
(4.000 2.000 16.24 2.000 2.000 16.24 43.97 4.000 2.000 16.24 43.97
4.000 2.000 48.73 307.8 714.2 6.000 10.83 205.2 1623. 6034. 9516.
2.000 4.000
4.000 2.000 48.73 4.000 4.000 64.97 307.8 2.000 48.73 307.8 714.2
2.000( 2.000 32.49 4.000 2.000 4.000 64.97 307.8
8.00
94.9 1559. 4869. 4.000 129.9 1559. 8181. 18100.
3.000 10.83 205.2 1623. 6034. 9516. )
3.000 58.63 952.3 6210. 19030. 24470. 2.000 32.49 4.000 2.0 0
4.000 129.9 1559. 8181. 18100. 3.000( 2.000 32.49 4.000
2.000 4.000 64.97 307.8 8.000 194.9 1
4.000 129.9 1559. 8181. 18100. 3.000 10.83 205.2 1623. 6034. 9516. ) ))
161
APPENDIX B
Parametric finite element simulations show the computed angular errors for different positions of the table as it moves along edges of the boundary of the workspace. Fig. B1(a) shows the path of actuation while Fig. B1(b) shows the recorded values of angular errors. The maximum allowed displacements for the comb drives is 25 µm and the maximum titling error observed is 2.6 milli-rad. Other paths along the edges of the workspace to the point of maximum z-displacement are possible but, because of symmetry of the workspace (and the structure) around the z-axis, are equivalent to the path taken. Analysis of the causes of these errors indicates that they accrue from the bending of the folded leaf springs, which can be reduced by increasing the thickness of device layer DL2.
Fig. B. Path of actuation at the moment of computing the angular parasitic (a) errors and evolution of the angular errors (b)
162
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