MODELING AND OPTIMIZATION OF PROCESS PARAMETERS USING NEURAL NETWORKS AND SIMULATED ANNEALING ALGORITHM FOR ELECTRICAL DISCHARGE MACHINING OF AISI2312 HOT WORKED STEEL

The present study addresses the multi-criteria modeling and optimization of Electrical Discharge Machining (EDM) for AISI2312 hot worked steel parts via optimized back propagation neural networks (OBPNN) and Simulated Annealing (SA) algorithm. The process response characteristics considered are surface roughness, tool wear rate and material removal rate.The process input parameters include voltage, peak current, pulse off time, pulse on time and duty factor. The weighted normalized grades, obtained from Taguchi design of experiments, are used to develop the arteficial neural network (ANN) model. In order to enhance the prediction capability of the proosed model, its architecture is tuned by simulated annealing algorithm. Next, the developed model is embaded into the SA algorithm to determine the best set of process parameters values for a desired set of outputs. Validation of the results has been carried out through a test run under the optimal machining conditions. Experimental results indicate that the proposed modeling and optimization procedures are quite efficient in modeling and optimization of EDM process parameters

PVP2008-61606 MULTI-OBJECTIVE OPTIMAL DESIGN OF SANDWICH COMPOSITE LAMINATES USING SIMULATED ANNEALING AND FEM

ABSTRACT Multi-objective optimal design of sandwich composite laminates consisting of high stiffness and expensive surface layers and low-stiffness and inexpensive core layer is addressed in this paper. The object is to determine ply angles and number of surface layers and core thickness in such way that natural frequency is maximized with minimal material cost and weight. A simulated annealing algorithm with finite element method is used for simultaneous cost and weight minimization and frequency maximization. The proposed procedure is applied to Graphite-Epoxy/Glass-Epoxy and Graphite-epoxy/Aluminum sandwich laminates and results are obtained for various boundary conditions and aspect ratios. Results show that this technique is useful in designing of effective, competitive and light composite structures

Prioritization and Evaluation of Mechanical Components Failure of CNC Lathe Machine based on Fuzzy FMEA Approach

Introduction In recent years, with development of industrial products with complex and precise systems, the demand for CNC machines has been increasing, and as its technology has been progressed, more failure modes have been developed with complex and multi-purpose structures. The necessity of CNC machines’ reliability is also more evident than ever due to its impact on production and its implementation costs. Aiming at reducing the risks and managing the performance of the CNC machine parts in order to increase the reliability and reduce the stop time, it is important to identify all of the failure modes and prioritize them to determine the critical modes and take the proper cautionary maintenance actions approach. Materials and Methods      In this study, conventional and fuzzy FMEA, which is a method in the field of reliability applications, was used to determine the risks in mechanical components of CNC lathe machine and all its potential failure modes. The extracted information was mainly obtained by asking from CNC machine experts and analysts, who provided detailed information about the CNC machining process. These experts used linguistic terms to prioritize the S, O and D parameters. In the conventional method, the RPN numbers were calculated and prioritized for different subsystems. Then in the fuzzy method, first the working process of the CNC machine and the mechanism of its components were studied. Also, in this step, all failure modes of mechanical components of the CNC and their effects were determined. Subsequently, each of the three parameters S, O, and D were evaluated for each of the failure modes and their rankings. For ranking using the crisp data, usually, the numbers in 1-10 scale are used, then using linguistic variables, the crisp values are converted into fuzzy values (fuzzification). 125 rules were used to control the output values for correcting the input parameters (Inference). For converting input parameters to fuzzy values and transferring qualitative rules into quantitative results, Fuzzy Mamdani Inference Algorithm was used (Inference). In the following, the inference output values are converted into non-fuzzy values (defuzzification). In the end, the fuzzy RPNs calculated by the fuzzy algorithm and defuzzified are ranked. Results and Discussion In conventional FMEA method, after calculating the RPNs and prioritizing them, the results showed that this method grouped 30 subsystems into 30 risk groups due to the RPN equalization of some subsystems, while it is evident that by changing the subsystem, the nature of its failure and its severity would vary. Therefore, this result is not consistent with reality. According to the weaknesses of this method, fuzzy logic was used for better prioritization. In the fuzzy method, the results showed that, in the 5-point scale, with the Gaussian membership function and the Centroid defuzzification method, it was able to prioritize subsystems in 30 risk groups. In this method, gearboxes, linear guideway, and fittings had the highest priority in terms of the criticality of failure, respectively. Conclusions The results of the fuzzy FMEA method showed that, among the mechanical systems of CNC lathe machine, the axes components and the lubrication system have the highest FRPNs and degree of criticality, respectively. Using the fuzzy FMEA method, the experts' problems in prioritizing critical modes were solved. In fact, using the linguistic variables enabled experts to have a more realistic judgment of CNC machine components, and thus, compared to the conventional method, the results of the prioritization of failure modes are more accurate, realistic and sensible. Also, using this method, the limitations of the conventional method were reduced, and failure modes were prioritized more effectively and efficiently. Fuzzy FMEA is found to be an effective tool for prioritizing critical failure modes of mechanical components in CNC lathe machines. The results can also be used in arranging maintenance schedule to take corrective measures, and thereby, it can increase the reliability of the machining process

Application of orthogonal array technique and particle swarm optimization approach in surface roughness modification when face milling AISI1045 steel parts

Face milling is an important and common machining operation because of its versatility and capability to produce various surfaces. Face milling is a machining process of removing material by the relative motion between a work piece and rotating cutter with multiple cutting edges. It is an interrupted cutting operation in which the teeth of the milling cutter enter and exit the work piece during each revolution. This paper is concerned with the experimental and numerical study of face milling of AISI1045. The proposed approach is based on statistical analysis on the experimental data gathered using Taguchi design matrix. Surface roughness is the most important performance characteristics of the face milling process. In this study the effect of input face milling process parameters on surface roughness of AISI1045 steel milled parts have been studied. The input parameters are cutting speed (v), feed rate (fz) and depth of cut (ap). The experimental data are gathered using Taguchi L9 design matrix. In order to establish the relations between the input and the output parameters, various regression functions have been fitted on the data based on output characteristics. The significance of the process parameters on the quality characteristics of the process was also evaluated quantitatively using the analysis of variance method. Then, statistical analysis and validation experiments have been carried out to compare and select the best and most fitted models. In the last section of this research, mathematical model has been developed for surface roughness prediction using particle swarm optimization (PSO) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. It has been found that the predicted roughness using PSO is in good agreement with the actual surface roughness