A decision support system for the selection of an additive manufacturing process using a new hybrid MCDM technique

Recently, Additive Manufacturing (AM) has been widely used in many applications. For a particular AM component, the choice of available AM processes is critical to the component’s quality, mechanical properties, and other important factors. In that context, this article presents an efficient decision support system for the selection of an appropriate AM process. A novel hybrid Multi-Criteria Decision Making (MCDM) technique has been proposed to select an appropriate AM process from available AM processes. The Best Worst Method (BWM) is used to determine optimal weights of criteria and the Proximity Indexed Value (PIV) method is employed to rank the available AM processes. For benchmarking the abilities of an AM process, a conceptual model of spur gear was fabricated by four available AM processes viz., Vat Photopolymerization (VatPP), Material Extrusion (ME), Powder Bed Fusion (PBF), and Material Jetting (MJ). Additionally, Dimensional accuracy (A), surface roughness (R), tensile strength (S), percentage elongation (%E), heat deflection temperature (HDT), process cost (PC) and build time (BT) has been considered as most significant criteria. Further, sensitivity analysis has been performed to validate the reliability of the results. The results suggested that the Material Jetting (MJ) process produces dimensionally accurate and quality parts among available alternatives AM processes. The ranking obtained using the PIV method is consistent and reliable.