Untitled

Abstract

Manufacturing processes are very much essential to transform the raw material into useful product. These manufacturing processes are associated with numerous input parameters. The cost of operating these equipments is also high. The main aim of the manufacturing engineers is to increase the productivity and quality economically. Therefore, one of best options is to optimize the process parameters of the manufacturing processes. There are number of optimization techniques and each optimization technique consist of number of experiments. In each experiment, a significant time and resources are used. Thus, a Design of Experiment (DoE) is required to reduce the number of experiments without loosing its accuracy. In the present paper, the DoE by Taguchi and Response Surface Methodology (RSM) is discussed. In addition of this, the optimization by different statistical optimization techniques are also explained. The procedure to select the DoE and optimize the process parameters is also discussed. Subsequently, the procedure to implement multi-response optimization techniques like desirability, Grey Relational Analysis (GRA), Multi-objective optimization on the basis of Ratio Analysis (MOORA), Technique for order preference by similarity to ideal solution (TOPSIS) are also explained. In the end of this review, an overview regarding the artificial intelligence (AI) and multi-criteria decision making (MCDM) approach in concern with the manufacturing process are also studied.