2.4 Application of Soft Computing Technique
2.4.1 Surface Finish and Dimensional Deviation
In machining, surface finish and dimensional deviation are two major attributes of quality of turned job. Rangwala and Dornfeld (1989) proposed an artificial neural network method for the prediction of machining performance parameters. An artificial neural network is an information-processing model that is inspired by the way biological nervous systems, such as the brain, process information. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data can be used to extract patterns and detect trends that are too complex to be noticed by either human or other techniques. A trained neural network can be thought of as an “expert” in the category of information it has to analyze. This expert can then be used to provide predictions given unseen situations.
With the pioneering work of Rangwala and Dornfeld (1989), there have been a number of applications of neural networks in machining. In the neural networks, various nodes called neurons are interconnected in the network. These can be used for finding out the relationship between input variables and output. In the recent past, different types of networks have been used for finding out the relation between
the output and the input vector. Two most common types of neural networks are multi-layer perceptron (MLP) neural network and radial basis function (RBF) neural network (Ham and Kostanic, 2001). Most of the researchers used multilayer perceptron (MLP) network trained by a back propagation algorithm for the prediction of machining parameters. Azouzi and Guillot [1997] proposed neural network model to predict surface finish and dimensional deviation of the job taking feed back from various sensors. Feed, depth of cut, radial force and feed force provides the best combination to make a model for on-line prediction of surface roughness and dimensional deviation. Several researchers have compared the effectiveness of neural network model with multiple regression models [Chryssolouris and Guillot, 1990; Lin et al., 2001; Feng and Wang, 2003].
Performance of neural network model was found superior to multiple regression models of Chryssolouris and Guillot [1990] and Lin et al. [2001], whereas Feng and Wang [2003] found that both are equally effective.
Risbood et al. [2003] used neural network to predict surface finish and dimensional deviation for dry and wet turning of steel with HSS and carbide tool.
They observed that, when turning steel rod with TiN coated carbide tool, the surface finish improved with increasing feed up to some particular feed range and then started deteriorating with further increase in feed. However, the authors did not notice such type of behaviour in case of turning by HSS tool. Pal and Chakraborty [2005] predicted the surface roughness considering cutting force, feed force, cutting speed, feed and depth of cut as input parameters in the network. Ozel and Karpat [2005] predicted surface roughness using two different models, one is online model and the other is offline model. Kohli and Dixit [2005] predicted surface roughness in turning process using multi layer perceptron (MLP) neural network with small size of data set as training and testing data. Their methodology of using small size of training and testing dataset is interesting, as generally, the neural network requires a large number of experimental data. Sun et al. [2006] proposed a method for the selection of training data for a neural network. In this work, a systematic procedure is provided to perform the data selection. The generalization error surface is divided into three regions and proper sampling factors are chosen for each region to prune the data points from the original training set.
Besides MLP network, another network called radial basis function (RBF) network is being used as a substitute of MLP network. RBF network can be trained faster compared to MLP network. However, RBF network require more training data than the MLP network. Sonar et al. [2006] studied the performance of RBF network for predicting lower, most likely and upper estimates of surface roughness in turning. Basak et al. [2007] also used RBF network for prediction of surface roughness in finish hard turning. The authors observed that the spread parameter, which is essentially governs the zone of influence of a neuron, plays a significant role in RBF training.
A number of other soft computing techniques have been used for the prediction of surface finish. Fang and Jawahir [1994] presented a methodology for assessing the aspects of total machining performance encompassing surface finish, tool wear rate, dimensional accuracy, cutting power and chip breakability. They quantified the effects of influencing process parameters on total machining performance by fuzzy- set method and developed a series of fuzzy-set models to give quantitative assessments for any given conditions, including work material properties, tool geometries, chip-breaker types and cutting conditions. Chen and Savage [2001] used fuzzy-net based model to predict surface roughness with various combination of tool and job in end milling operation. The input parameters for the fuzzy system were speed, feed, depth of cut, vibration, tool diameter, tool material and work piece material. For the surface roughness, the authors observed prediction errors within 10%.
Abburi and Dixit [2006] developed a knowledge base system using NN model and fuzzy set theory for the prediction of surface roughness in turning process. The trained network is used for generating a large amount of data that are fed to a fuzzy set based rule generation module. This rule-based module is used for predicting surface roughness for given process variables as well as for the inverse prediction of process variables for a given surface roughness. A fuzzy expert system was developed by Iqbal et al. [2007] for prediction of surface roughness and tool life in high speed milling operation.
Nandi and Pratihar [2004] developed a genetic-fuzzy system to predict surface finish. A genetic algorithm was used in the network for optimisation. Brezocnic et
al. [2004] proposed a genetic programming (GP) approach to predict surface roughness in end milling operations. The GP was first introduced by Koza [1992] in the year 1992. The aim of GP is to find out the computer programs (called as chromosomes) whose size and structure dynamically changes during simulated evolution that best solve the problem. Reddy et al. [2005, 2006] and Oktem et al.
[2005] used genetic algorithm (GA) for various purpose of works in end milling viz.
prediction of surface roughness with parameter-optimization, optimum cutting condition for minimum surface roughness, optimization of tool geometry etc. Jiao et al. [2005] developed a fuzzy adaptive neural network (FAN) for the prediction of dimensional deviation. In this model, first, an approximate model is established with the machining parameters that influence the dimensional deviation. This model is then improved by learning with the given training data. The author found that the model is very effective.
Samanta [2009] predicted surface roughness using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GA).The author used spindle speed, feed rate, depth of cut and the workpiece-tool vibration amplitude as inputs.
The ANFIS with GAs (GA-ANFIS) are trained with a subset of the experimental data. The result of this model was found effective compared to other soft computing techniques such as genetic programming (GP) and artificial neural network (ANN).
Lo [2003] used ANFIS to predict surface finish in end milling operation. The model was built using triangular and trapezoidal membership function. Speed, feed and depth of cut were considered as input parameter. Ho et al. [2008] also proposed ANFIS to predict surface finish in end milling operation of aluminium alloy with HSS tool. They used a hybrid Taguchi genetic learning algorithm in the ANFIS to determine the most suitable membership functions and simultaneously find optimal parameters by directly minimizing surface roughness error. The methodology performed better than the normal ANFIS method.