At present, increasing environmental awareness and stringent environmental regulations of the competent authority, the use of green manufacturing (GM) technique is an important topic for the present industries. To overcome the environmental issues, the manufacturing industries use various advanced manufacturing processes for optimal product development. The advance manufacturing process generates large amounts of toxic substances results in various environmental issues during the optimal product manufacturing. Minimization of environmental issues and the amount of waste generated are strongly depends on its process and response parameters. Thus, optimization of process parameters for GM is essential and is a multi-criteria decision making (MCDM) optimization problems. This chapter provides an overview of applications of some MCDM methods for optimization followed by detailed fundamental aspects of optimization issues in green manufacturing. The work proposed an integrated method consisting of AHP coupled with MOORA and validated through an experimental case study.
TopIntroduction
In the process of product development, the manufacturing system plays an important role. In the development of the product from the raw material, the various manufacturing processes such as electrical discharge machining (EDM), laser machining (LM), ultrasonic machining (USM), abrasive water jet machining (AWJM) etc. have been employed. During product manufacturing, the various harmful components such as solid, liquid, and gaseous wastes have been produced which affects the manufacturing environment (Sivapirakasam et al. 2011, Shokrani et al. 2012). Besides, the waste generation, manufacturing processes also use various forms of power sources that directly or indirectly affects the machining atmosphere. Due to government guidelines and public cognizance, ecological aspects in the manufacturing processes become utmost important consideration (Sheng and Srinivasan 1995, Yeo et al. 1998, Abbas et al. 2007). Furthermore, the performance of the product manufacturing processes strongly depends on its process and response parameters (Melngk and Smith 1996, Liu et al. 1999). The influence of process parameters and their corresponding performance characteristics results in the optimal result and affects the performance, quality, as well as manufacturing cost. Hence, process parameter optimization of the manufacturing process is essential during product development from the environment and cost effective point of view.
The optimization of the process parameters for the manufacturing process is further considered to be a complex MCDM optimization problem. It involves several criteria’s, i.e. process parameters and environmental response parameters, and several alternatives, i.e. various experimental settings. In actual practice, the response/process parameters depend on the priority weights and the degree of importance of the various parameters. The assignment of priority weights also depends on the preference of the decision maker. An improper selection of priority weights yields the error in the selection of optimal process parameters and corresponding response parameters, thus it affects the performance, quality of the product, manufacturing costs and efficiency.
In the traditional approach, choices of optimal process parameters for the manufacturing processes are carried out through data handbook values or operator knowledge. However, this mode of selection does not ensure the optimal performance of manufacturing process (Machining Data Center, 1980). On the other hand, selection of process parameter based on operator or expert judgment is also not an appropriate approach because it does not provide the guarantee that process setting obtained is optimal. In spite of these reasons, the appropriate selection of process parameters for any advanced manufacturing process is further considered to be a still ill-defined optimization problem and involves imprecise data and subjective plus objective data and vagueness information (Yadav and Patel 2013), which results in complexity in the optimization of manufacturing processes. Thus, development of an exact mathematical model to control the machining process is a difficult and essential task. Therefore, a systematic, efficient decision-making approach based on MCDM methods is a necessary requirement for effective modeling and process parameter optimization for product manufacturing processes.