Grey Regulatory Focus Theory Weighting Method for the Multi-Criteria Decision-Making Problem in Evaluating University Reputation

Determination and managerial implications of machine conditions for high-grade industrial polycaprolactam (nylon 6)

Polycaprolactam (PA6) is a thermoplastic polymeric material and because of its excellent mechanical properties, it has found an extensive application in military, textile, biomedical, building and construction, and several others. Because of its extensive applications, machine turning operation becomes a crucial section in the manufacturing of high-grade PA6. Hence, to have a high-grade PA6, turning operational conditions (cutting speed, feed rate and depth of cut) are optimized on the three surface profile responses and one material removal rate (MMR) with help probability based multi-response optimization analysis. This analysis is employed for an efficient multi-criterial decision making when PA6 is manufactured with a turning operation machine. The result revealed an optimal turning operational conditions to be 860 rpm cutting speed, 0.083 mm/rev feed rate, and 4 mm depth of cut. Furthermore, the analysis of variance and the numerical presentation of the turning operational conditions revealed that the feed rate is the most significant condition with a contribution of 34.09%, followed by cutting speed with a contribution of 32.05%, and then depth of cut with a contribution of 28.62%. Also, the confirmation analysis revealed a very high efficacy of the multi-objective optimization method employed in this study. This suggests that probability based multi-objective optimization is efficacious for optimizing machine conditions of any manufactured engineering material. It is interesting to state that the high confidence level placed on the considered turning operational conditions gives room for probable machine conditions adjustments for better PA6 in the case where different machine types are employed.

Cost and Quality Optimization Taguchi Design with Grey Relational Analysis of Halloysite Nanotube Hybrid Composite: CNC Machine Manufacturing

Researchers who work on manufacturing hybrid composites have significant concerns about holistically optimizing more than one performance characteristic, as in the case of cost and quality optimization. They usually trade off one for the other. Hence, this study employed statistical tools and grey relational analyses (GRA) design to model and optimize the surface roughness and cutting force of Computer Numerical Control (CNC) machine settings to manufacture halloysite nanotube hybrid composite. In this paper, the GRA was able to address the multiple optimization complications by producing 0.6 mm depth of cut, 1500 rpm spindle speed, and 40 mmpm feed rate as the CNC machine settings for high-quality and low-cost hybrid composite. It was noticed that the mathematical and interaction modeling of surface roughness, cutting force, and grey relational grade (GRG) allowed different CNC machines to manufacture hybrid composites. This can assist researchers and production engineers of CNC machines. Variance analysis and delta statistical characteristics revealed that the depth of a cut is the most significant machine setting, with a contribution of 49.12%. This paper outlines the possible CNC machine settings for high-quality composite manufacturing. In future studies, it is recommended for researchers in the field of CNC machine manufacturing to consider the modeling analysis aspect of the optimization, which comprehensively provides the opportunity for the adjustment of CNC machines for better material performance, which has been lacking in the literature.

Application of the Analytic Hierarchy Process and Grey Relational Analysis for Vendor Selection of Spare Parts Planning Software

The success of after-sales services fundamentally depends on the possibility of symmetry balancing needs, supply and customer satisfaction. Spare parts planning (SPP) software selection is an investment in balancing the symmetry of inventory control, which directly affects the success of after-sales services. This paper presents the analytic hierarchy process (AHP) and grey relational analysis (GRA) as potential multi-criteria decision making (MCDM) methods for software selection. The application of these methods can be decreased by decision-makers’ subjectivity, which leads to consistency and symmetry in the weight values of the criteria. The criteria were evaluated solely based on system functions by business consultants with respect to the modularity of the software to meet various business requirements, distinguishing this paper from other papers that focus on the financial aspects of selecting software. The results suggest that the AHP and GRA are feasible methods for software selection that effectively integrate consultants’ experience to identify the suitable software and the most important function for adoption in real businesses. The findings of our data suggest that inventory optimization is the most important function of SPP software, as the demand forecasting function may be subjective. In conclusion, this paper represents a bridge between practitioners and researchers.