Selection of suitable additive manufacturing machine and materials through best–worst method (BWM)

In this competitive world, industries are looking for smart technologies to compete; these technologies help R&D people to explicit the ideas and bring the product to the market at shorter lead times and with affordable cost. Each AM machine has its own unique capabilities in manufacturing a product, utilising materials, material intake and wastages. Machine and material costs are the significant parameters, which play a major role in cost estimation of the prototypes. Costs of both machine and materials are prime factors in AM and it can be helpful for cost reduction due to their uniqueness. However, an alternate strategy is being concentrated on process optimization and consumption of material to reduce the overall cost of the prototype. In this paper, multi criterion decision making (MCDM) technique, namely, best-worst method (BWM), was adopted to select the suitable material for the product. This is along with the end user expectations in AM. In the initial phase, the suitable machine to be selected from the available machines is based on the parameters like cost, accuracy, variety of materials and material wastage. From the variety of materials, the suitable material was selected based on the respondent requirement. The criteria that influenced more in the overall cost of the product manufacture through AM is identified and used. According to BWM, the criteria to be selected by the decision maker based on the respondent expectations are identified. In BWM method, pairwise comparisons are carried out between the best and worst criterion suggested by the decision makers, as that it leads to the selection of the suitable material. Here, a demonstration of such a selection is detailed; this is certainly based on the respondent requirements. The result attained through the proposed methodology can be varied based upon the respondent requirements and further machine availabilities. In conclusion, the end result helps to identify the suitable machine and build materials for the prototype to be produced based on the respondent requirements.