Modeling and Optimization of Advanced Manufacturing Processes

This chapter introduces various advanced machining processes and optimization techniques discussed in this book. It starts with an introduction and classification of advanced machining processes such as electric discharge machining, electrochemical machining, ultrasonic machining, abrasive water jet machining, and laser beam machining. The introduction is followed by multi-criteria decision-making (MCDM) techniques that have been used in the aforementioned advanced processes. Based upon the literature review conducted by the authors, the statistics as regards to the percentage use of various MCDM techniques in case of advanced processes is discussed. The chapter ends on conclusion with possible hope to conduct future research on modeling and optimization of advanced machining processes using various single and hybrid MCDM techniques.

This chapter presents modeling and optimization of EDM process for high MRR, low surface roughness, and low electrode wear rate. The integrated SOW and WGRA technique has been researched to optimize EDM-type process. Based upon the optimization results, a single set of EDM parameters produced best values of MRR—1.28 mm³/min, surface roughness—2.37 μm, and electrode wear rate—0.14 mm³/min. The multi-criteria decision-making technique, i.e., integrated SOW–WGRA, has been found to have significant potential for modeling and optimization of EDM and other advanced machining processes. The chapter hopes to facilitate researchers and encourages future work on optimization of advanced processes to establish the field further.

This chapter presents the modeling and optimization of abrasive water jet machining process using TOPSIS integrated DEMATEL technique. Machining of sundi wood dust composites has been done by AWJM process. To improve the MRR in the machining process, surface quality, and geometric accuracy of the machined workpiece, AWJM parameters have been optimized. The implemented optimization technique has significantly improved MRR, surface quality, and geometric accuracy of the workpiece. The implemented multi-criteria decision-making technique has been recognized to have potential to be considered further for modeling and optimization of AWJM-type advanced process.

Ultrasonic machining is a well-known advanced machining process to machine typical shapes, holes, and micro-features in hard and brittle materials. Interventions of multi-criteria decision-making techniques are required to optimize the process parameters for better part quality, productivity, and sustainability of this type of advanced processes. Implementation of integrated fuzzy–MCRA (multi-criteria ranking analysis) for modeling and optimization of ultrasonic machining process to improve material removal rate and dimensional accuracy during machining of zirconia composites is discussed in this chapter. Investigation results indicate effectiveness of this technique for multi-objective optimization of ultrasonic machining parameters to produce high-quality parts made of composites with excellent productivity.

Additive layer or rapid prototyping processes are viable substitutes of conventional manufacturing processes to produce quality parts made of metals and plastics. Same as advanced machining processes, this is an advanced manufacturing process used to manufacture engineered parts and components for various commercial, domestic, and industrial applications. The manufacturing quality of the parts made by these processes is very much dependent on process parameter optimum combinations particular to material and part geometry. This chapter discusses the application, implementation, and effectiveness of multi-criteria decision-making techniques to optimize fused deposition modeling (FDM)-type rapid prototyping process. The results of experimental study-based optimization of FDM process reveal the suitability of fuzzy integrated M-COPRAS technique to optimize parameters for improved strength, dimensional accuracy of FDM made parts and process productivity.