Decision support system to select a 3D printing process/machine and material from a large-scale options pool

Additive manufacturing (AM) is a promising manufacturing technology; it has great manufacturing capabilities and large material diversity, making it applicable for many applications. However, the limited information related to AM processes, materials, and rules restricts its potential popularity over traditional manufacturing methods. For a particular application, the choice of available AM process/machine and material is critical to the application’s quality, mechanical properties, and other important factors. Moreover, the large number of AM processes/machines and materials and the overlap among them in terms of capability and functionality further complicate the selection task. AM service selection as a problem has three essential aspects, and all support systems that are built to help users select the optimal AM service must consider these aspects during the system development stage. These aspects are the AM resource capability information source, user intervention, and the methodology suggesting the best AM resources that meet user requirements. These aspects actually represent AM resource definition accuracy and availability, selection system usability, and reliability. This study analyses the essence of these three aspects, reviews the literature, and develops a new AM selection system framework that enhances system functionality regarding these aspects. First, 3D printing service providers in the proposed system are considered the source of AM resource capability information to expand AM service options to users, provide accurate resource definitions, and ensure their availability. Second, the proposed system applied the posteriori-based multicriteria decision-making approach through the integration of decision making trial and evaluation laboratory (DEMATEL), analytic hierarchy process (AHP), and modified Technique for Order Preference by Similarities to Ideal Solution (TOPSIS) techniques. This greatly minimizes user inputs during AM service selection process, fully supports the DFAM (Design for additive manufacturing) concept, and maintains the accuracy of final selection decisions.