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Chen, Joseph C. "Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations" Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton: CRC Press LLC,2001 16 Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations Joseph C. Chen Iowa State University 16.1 Introduction 16.2 Methodologies 16.3 Experimental Setup and Design 16.4 The In-Process Surface Roughness Recognition Systems 16.5 Testing Results and Conclusions 16.1 Introduction Different machining processes produce different products with varying qualities. When evaluating the quality of a finished piece, surface roughness is the most important result of the machining process to consider, because many product attributes can be determined by how well the surface finish is produced. The quality of the surface finish, or surface roughness, affects several functional attributes of parts, such as surface friction, wear, reflectivity, heat transmission, porosity, coating adherence, and fatigue resistance. The desired surface roughness value is usually specified for individual parts, and a particular process is selected in order to achieve the specified roughness. Typically, surface roughness measurement has been carried out by manually inspecting machined surfaces at fixed intervals. A surface profilometer containing a contact stylus is used in the manual inspection procedure. This procedure is both time-consuming and labor-intensive. In addition, a num-ber of defective parts could be produced during the time needed to complete an off-line surface inspection, thereby creating additional production costs.Another disadvantage of using surface profilo-meters is that they register the serious interference of extraneous vibration generated in the surrounding environment. This extraneous vibration might significantly influence the accuracy of surface measure-ments. For these reasons, researchers are seeking solutions to model the surface roughness in an on-line or in-process fashion. ©2001 CRC Press LLC The studies of Martellotti [1941, 1945] are among the earliest that represent a major contribution to the understanding of kinematics and the mechanism of surface generation in milling processes. Martel-lotti developed parametric equations to describe the trochoidal path that the tool follows. These studies also provide approximate analytical expressions for the ideal peak-to-valley roughness generated in up-and down-slab milling, and face milling. Numerous other studies have explored the topography of milled surfaces. Many of these focused on predicting the two- or three-dimensional shape of a milled surface under ideal and non-ideal conditions. Kline et. al. [1982] demonstrated the effects of cutter runout on surface errors, and surface errors or dimensional inaccuracies were predicted using the cantilever beam theory for cutter runout. Another study by Babin et al. [1985] applied the cantilever beam theory to predict the topography of wall surfaces produced by end milling.Armarego and Deshpande [1989] presented one more milling process geometry model that incorporates cutter runout to predict cutting forces. Sutherland and Babin [1985] demonstrated a two-dimensional worst-case analysis of the slot floor surface. However, the model for the slot floor surface significantly underpredicted surface roughness values. Research by Kolarits and DeVries [1989] extended the previous model to account for varying cut geometries and feed rates. This extended floor surface generation model improved prediction capabilities considerably. However, the roughness parameter predictions for some of the tests were found to deviate greatly from measured values. You and Ehmann [1989] developed a comprehensive model to predict the three-dimensional surface texture generated by ball end mills. They also presented an algorithm for three-dimensional representa-tions of the machined surface; however, the effect of flexibility of the cutter-workpiece system was not considered in this model. Montgomery and Altintas [1991] presented the effects of the cutter-workpiece system flexibility in their force and surface prediction model in order to analyze the surface generation mechanism in peripheral milling under dynamic cutting conditions. All models previously discussed represent only deterministic cutting models, but most machined surfaces exhibit interrelated characteristics of both random and deterministic components. Zhang and Kapoor [1991] demonstrated the effect of random vibrations on surface roughness in the turning process. These vibrations were shown to occur due to random variations in the microhardness of the workpiece material. Ismail and others presented a surface generation model in milling that included both cutter vibrations and the effects of tool wear [Ismail et al., 1993]. Melkote and Thangaraj [1994] presented another enhanced end milling surface texture model including the effects of radial rake and primary relief angles. These three models, limited to laboratory usage or based on theoretical analysis, could not be implemented as an in-process monitoring system. The findings of this literature review, in addition to communication with leading private industrial research and development laboratories in the state of Iowa (including Winnebago Co. in Forest City; Delavan Inc. in Des Moines; Sauer-Sundstrand Inc. in Ames), point to the feasibility of in-process surface roughness recognition (ISRR) systems for implementation in the newer generation of milling machines. The successful implementation of this surface roughness recognition system will enable metal cutting industries to reduce manufacturing costs by eliminating the relatively inefficient off-line quality control aspect of surface roughness inspection. Therefore, reductions in manufacturing costs will increase com-petitiveness in worldwide markets. This implication supports the development of an effective and inex-pensive ISRR system. The development of this system will enable implementation of adaptive control in modern manufacturing environments. 16.2 Methodologies In order to provide an adaptive control mechanism, ISRR systems require two major components: (i) sensors, which receive the dynamics signal from the machining cutting processes; and (ii) an intelligent technique able to learn the dynamics of the machining system while allowing for control features to be built in. The research described in this chapter employed an accelerometer to detect the dynamics mechanism of the tool and material interface. This study also used two major intelligent learning ©2001 CRC Press LLC methodologies to incorporate data about the machining process through actual cuts. These methodol-ogies were also employed to construct a control system that predicts surface roughness during the execution of the machining process. These two learning methodologies are artificial neural networks (ANN) and fuzzy neural (FN) systems.An overview of these two approaches follows in the next section. 16.2.1 Neural Networks Model Several learning methods have been developed for ANNs. Many of these learning methods are closely connected with a certain network topology, with the main categorization method distinguished by supervised vs. unsupervised learning. Backpropagation was chosen from among various learning methods already existing in this field. This approach was adopted into this research for two reasons: primarily, it is the most representative and commonly used algorithm, in addition to being relatively easy to apply; additionally, it has been consistently successful when used in practical applications [Das et al., 1996; Huang and Chiou, 1996]. The backpropagation algorithm can be divided into two main processes, the process of learning and the process of recalling. 16.2.1.1 The Learning Process Step 1. Given network parameters: Set all the necessary parameters, such as the number of input neurons (i), the number of hidden layers and the number of neurons included in each hidden layer (h), the number of output neurons (j), etc. Step 2: Initialize the beginning weights and biases: Set all the initial weights and biases values randomly. Step 3: Load the input vector X and the target output vector T of a training example. Step 4: Calculate and infer the actual output vector Y. (a) Calculate the output vector H of hidden layers. neth = åW _xhih •Xi –q _hh i H = f net = 1 1+exp h Equation (16.1) Equation (16.2) (b) Infer the actual output vector Y. netj = h W_hyhj áHh −q _ yj Y = f (net )= 1 1 exp j Step 5: Calculate the error term. (a) The error term of the output layer: d j =Yj 1–Yj Tj –Yj (b) The error term of the hidden layer: dh = Hh(1–Hh) åW _hyhi •dj j Equation (16.3) Equation (16.4) Equation (16.5) Equation (16.6) ©2001 CRC Press LLC Step 6: Calculate the revised weight of the weight matrix and the revised bias of the bias vector. (a) For the output layer: (b) For the hidden layer: W _hyhj =hdjHh, W _xhih =hdh Xi , q _ yj = –hdj q _hh = –hdh Equation (16.7) Equation (16.8) Step 7: Adjust and renew the weight matrix and the bias vector. (a) For the output layer: W_hyhj = W_hyhj + W_hyhj,q_yj = q_yj + q _yj Equation (16.9) (b) For the hidden layer: W_xhih = W_xhih + W_xhih,q_hh = q_hh + q _hh Equation (16.10) Step 8: Repeat steps 3 through 7, until the energy function has converged or the specified learning cycles are completely executed. 16.2.1.2 The Recalling Process Step 1: Set all the network parameters. Step 2: Read the weight matrix W_xh and W_hy, and the bias vector q_h and q_y. Step 3: Load the input vector X of a testing example. Step 4: Calculate and infer the actual output Y. (a) Calculate the output vector H of hidden layers. neth = åW _xhih •Xi –q _hh i H = f net = 1 1+exp h Equation (16.11) Equation (16.12) (b) Infer the actual output vector Y. net j = åW _hyhj •Hh –q _y j h Y = f net = 1 1+exp j Equation (16.13) Equation (16.14) 16.2.2 Fuzzy-Nets Modeling The proposed fuzzy-nets system was developed by fuzzy rules generated from sampled input–output pairs. This model is built in five steps. 16.2.2.1 Step 1: Divide the Input and Output Spaces into Fuzzy Regions Assume that the domain intervals of input variable xi are [x– ,x+ ], and that the domain intervals of output variable y are [y–,y+]. Each domain interval can be divided into 2N + 1 regions. The value of N is dynamic for different variables, and the lengths of each region can be equal or unequal. Each region is denoted by ©2001 CRC Press LLC ... - tailieumienphi.vn