Designing a Cost-Time-Quality-Efficient Grinding Process Using MODM Methods

Meysam Mahjoob and Pouneh Abbasian
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Meysam Mahjoob: University of Tehran, Fouman, Iran
Pouneh Abbasian: Texas A&M University, USA

European Journal of Engineering and Technology Research, 2022, vol. 7, issue 2, 12-17

Abstract: In this paper, a multi-objective mathematical model is developed to optimize the grinding parameters Such as grinding time, cost, and related surface quality metrics such as workpiece speed, depth of cut and wheel speed. The mathematical model consists of three conflicting objective functions subject to wheel wear and production rate constraints. Exact methods cannot solve the NLP model in few seconds, therefore using Meta-heuristic algorithms that provide near-optimal solutions is not suitable. Considering this, five multi-objective decision-making (MODM) have been used to solve the multi-objective mathematical model using general algebraic modelling system (GAMS) software to achieve the optimal parameters of the grinding process. The MODM methods provide different effective solutions where the decision-maker (DM) can choose each solution in different situations. Different criteria have been considered to evaluate the performance of the five MODM methods. Also, a technique for order of preference by similarity to ideal solution (TOPSIS) has been used to obtain the priority of each method and determine which MODM method performs better considering all criteria simultaneously. The results indicated that the weighted sum method (WSM) and goal programming method (GP) are the best MODM methods, as both of them provide competitive solutions. In addition, these methods obtained solutions that have minimum grinding time, cost, and surface roughness among other MODM methods.

Keywords: Goal programming; Grinding; Multi-objective decision making; TOPSIS (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:7:y:2022:i:2:id:62719

DOI: 10.24018/ejeng.2022.7.2.2719

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