5.5.1 Classification using Neural Network (NN)
An Artificial Neural Network (ANN) is a mathematical or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. An ANN usually organises its units into several layers. The first layer or input layer, the intermediate layers or hidden layers, which are not always present because they are sometimes not needed, and the last or output layer. The information to be analysed is presented (or fed) to the neurons of the first layer and then propagated to the neurons of the second layer for further processing.
These results are propagated through each layer, converting the information into the network output in the final layer. The goal of an ANN is to discover some association between input and output patterns. Many different neural network
capabilities. Neural networks are classified as supervised and unsupervised according to their learning characteristics. The decision is greatly dependent on the data obtainable for training the networks. If there is a target class or output for each pattern, then a supervised neural network can be used such as Back Propagation Neural Network (BPNN). However, when the input data do not have target output specified previously, then “unsupervised” neural networks have to be implemented. Unsupervised neural networks, such as Learning Vector Quantisation (LVQ) use a special algorithm to group similar patterns in the input data space into similar output classes [141].
Tool condition monitoring is necessary to obtain good quality product. The relationship between sensor and tool wear is investigated during end milling. For this purpose, reference [142] conducted an experiment using an acceleration sensor assembled on a machinery analyser. Tool wear was measured by a toolmaker's microscope where it was observed that there was an increase in vibration amplitude with increasing tool wears. However, the problem of associating such an approach with the milling process makes it very difficult to detect the levels of tool wear especially when considering the use of intelligent sensor based monitoring systems within an automated machining environment. Therefore, another approach is needed for the decision making process in order to support tool condition monitoring is the application of neural networks. Reference [143] presented a review of the application of neural networks as outlined in Figure 5.3. Where the sequence of the monitoring process start from the transfer the data from the machining process through the sensors to the computer system. The feature extraction technique will obtain the useful information, and then the data will be trained and tested using neural network to determine the tool stats. It is identified that the development of accurately on-line monitoring system capable of operating within the milling environment is needed further investigations.
Data Compression Current Signal Force Signal AE Signal Feature extraction Neural Network Good Tool Faulty Tool Output of Limit ?
Figure 5.3: Representation of neural network based cutting process monitoring.
5.5.2 Classification using Fuzzy Logic (FL)
A fuzzy logic is an artificial-intelligence-based method has also proved a useful classification technique to tool wear monitoring strategies when combined with multiple sensor inputs. A fuzzy clustering algorithm is used for online tool wear classification. This approach develops the online capabilities of the system which have yet to be established as all analysis is carried out off-line. The application of fuzzy pattern recognition techniques is described to identify and classify tool wear as part of an on-going research activity. Reference [144] presented a method for tool wear monitoring based on fuzzy logic handling multi-sensor inputs, and monitoring the spindle motor power and the cutting forces. During a classification stage, cutting experiments are carried out utilising various tools with known wear.
Skilled human operators are shown to be better than model-based controllers in machining control, therefore fuzzy logic control, is a practical alternative to model based control schemes. Reference [145] employed this fact and proposed a fuzzy- logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors.
Reference [146] performed a comparison between experimental results and consistent fuzzy rule-based model for estimating the cutting forces. For experimental work, a dynamometer and strain gauge are used to measure static and dynamic cutting forces. Experimental results are compared with the predicted fuzzy model; the difference between experimental and predicted results is around 99.6% due to
continuous to achieve good results with this approach. Therefore, reference [147] conducted with the advance of a tool wear condition-monitoring technique based on a two stage fuzzy logic scheme. For this, signals acquired from various sensors are processed to make a decision about the status of the tool. In the first stage of the proposed scheme, statistical parameters derived from thrust force, machine sound and vibration signals are used as inputs to the fuzzy process. The fuzzy output values of this stage are then taken as the input parameters of the second stage. Finally, outputs of this stage are taken into a threshold function, the output of which is used to assess the condition of the tool.
Fuzzy logic approaches are applied for the micromilling processes, as extreme forces and vibrations significantly affect the overall quality of the part. In order to improve the part quality and longevity of tools, so reference [148] examined the factors affecting tool wear using various sensors including accelerometers, force and acoustic emission sensors combined with an optical microscope to measure the real tool condition in micro-milling. The signals are fused through the neuro-fuzzy method to determine whether the tool is in good shape or is worn.
Overall, the applicability of fuzzy logic analysis to monitor tool wear has yet to be established within an industrial context. Correspondingly, the application of the approach as a means of decision making within a tool breakage monitoring system also requires further investigation.