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.
Highlights
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
Multi-choice goal programming (MCGP) is another concept that provides a range of ideal solutions for each objective function [21], thereby it is more flexible than goal programming (GP) in situations when DM underestimates the initial ideal solution set for the model. lack of available recourses and information could be reasons for changes in DM’s preferences in different situations and times
This method considers each objective function separately, solves the optimization problem and obtains the optimal solution. This method is based on this concept that the optimal solution of each objective function is an effective solution for the multi-objective optimization problem
Summary
Many researchers have focused on optimizing the grinding process. Baskar et al [2] proposed an ant colony-based optimization approach to optimize the grinding parameters using a multi-objective model with a weighted method under thermal damage, wheel wear parameter, surface finish, and tool stiffness constraints They compared the results with Quadratic programming (QP) and Genetic Algorithm (GA) presented in previous researches. Gholami and Azizi [20] presented a non-dominated sorting genetic algorithm (NSGA II) to obtain the optimal values of workpiece speed, wheel speed, and depth of cut in the grinding process They presented different Pareto solutions for the multi-objective optimization problem selected by the decision-maker (DM) under different scenarios. The majority of previous studies combined the objective functions to construct a single weighted objective function in order to optimize the grinding parameters This conversion may lead to significant deviations in obtaining the optimal value of the decision variables and the solution's quality.
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