A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection

Abstract

With the introduction and increased use of newer and harder materials such as titanium, stainless steel, high-strength temperature-resistant (HSTR) alloys, fibre-reinforced composites, and ceramics in the aerospace, nuclear, missile, turbine, automobile, tool, and die-making industries, a different class of machining processes has emerged. Instead of employing the conventional cutting tools, these non-traditional machining (NTM) processes use energy in its direct form to remove materials from the workpiece. Selection of the most suitable NTM process for machining a shape feature on a given work material requires consideration of several factors. A combined method using the ‘technique for order preference by similarity to ideal solution’ (TOPSIS) and an analytical hierarchy process (AHP) is proposed to select the most appropriate NTM process for a specific work material and shape feature combination, while taking into account different attributes affecting the NTM process selection decision. This paper also includes the design and development of a TOPSIS-AHP-method-based expert system that can automate the decision-making process with the help of a graphical user interface and visual aids. The expert system not only segregates the acceptable NTM processes from the list of the available processes, but also ranks them in decreasing order of preference. It also helps the user as a responsible guide to select the best NTM process by incorporating all the possible error-trapping mechanisms.

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

“…The weights of NTMP selection criteria were used to calculate overall scores for the possible NTMPs. Das Chakladar and Chakraborty [8] utilized a combined AHP-TOPSIS-based methodology for selection of the best NTMPs for some given machining applications. Edison Chandrasselan et al [9] proposed a web-based knowledge base system for identifying the most appropriate NTMP, while considering material requirements, shape applications, process economy and process capabilities parameters.…”

Application of PROMETHEE-GAIA method for non-traditional machining processes selection

“…Using AHP method, the criteria weights were determined as w TSF = 0.0783, w PR = 0.0611, w MRR = 0.1535, w C = 0.1073, w E = 0.0383, w TF = 0.0271, w TC = 0.0195. w S = 0.0146, w M = 0.2766 and w F = 0.2237, which are used for the PROMETHEE-GAIA method-based analysis. While applying a combined AHP-TOPSIS method, Chakladar and Chakraborty (2008) obtained a ranking of NTM processes as EDM-ECM-WEDM-CHM-USM-EBM-LBM-WJM-AJM in descending order of preference. When the same problem is solved using PROMETHEE-GAIA method, EDM is observed to be the most appropriate NTM process for this machining application, as shown in Figure 7.…”

“…The earlier researchers have already employed various tools and techniques, like computer-aided decision-making method (Cogun, 1993(Cogun, , 1994, combined analytic hierarchy process (AHP) and technique for order performance by similarity to ideal solution (TOPSIS) method (Yurdakul & Cogun, 2003;Chakladar & Chakraborty, 2008), AHP method (Chakraborty & Dey, 2006), management information system (Chakrabarti et al, 2007), quality function deployment-based expert system (Chakraborty & Dey, 2007), web-based knowledge base system (Chandraseelan et al, 2008), digraph-based expert system (Chakladar et al, 2009), analytic network process (ANP) (Das & Chakraborty, 2011), data envelopment analysis (DEA) (Sadhu & Chakraborty, 2011) and multiobjective optimization using ratio analysis (MOORA) method (Chakraborty, 2011) for selecting the best NTM processes for varying machining applications. It is sometimes not possible for the adopted multi-criteria decision-making (MCDM) methods, like AHP, ANP, TOPSIS, DEA etc.…”

Developing an efficient decision support system for non-traditional machine selection: an application of MOORA and MOOSRA

“…Karthikeyan, Lakshmi Narayanan, and Naagarazan (1999) worked on mathematical modeling for EDM of aluminum-silicon carbide metal matrix composite. The earlier researchers have also employed various tools and techniques like data envelopment analysis (DEA) (Sadhu & Chakraborty, 2011) and multiobjective optimization using ratio analysis (MOORA) method (Chakraborty, 2011) for selecting the best NTM processes for various machining applications, (TOPSIS-based methodology for selecting the best non-traditional machine by Chakladar and Chakraborty (2008), (ANP for selection of non-traditional machining processes by Das and Chakraborty [2011]). …”