Strategic production process design with additive manufacturing in a make-to-stock environment

• Main Authors: Chua, Ping Chong, Moon, Seung Ki, Ng, Yen Ting, Lopez, Manet
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Additive Manufacturing; Production Systems Optimization
• Online Access: https://hdl.handle.net/10356/171740
• Institution: Nanyang Technological University

With the development and gradual maturity of additive manufacturing (AM) over the years, AM has reached a stage where implementation into a conventional production system becomes possible. With AM suitable for small volumes of highly customized production, there are various ways of implementing AM in a conventional production line. The aim of this paper is to present a strategic design approach to implementing AM with conventional manufacturing in a complementary manner for parallel processing of production orders of large quantities in a make-to-stock environment. By assuming that a single machine in conventional manufacturing can be operated using AM, splitting of production orders is allowed. Therefore, production can be conducted by both conventional and AM processes simultaneously, with the latter being able to produce various make-to-stock parts in a single build. A genetic algorithm with a scheduling and rule-based heuristic for part allocation on the build plate of AM process is used to solve a multi-objective implementation problem of AM with conventional manufacturing, with cost, scheduling, and sustainability being the considered performance measures. By obtaining a knee-point solution using varying numbers of population size and generation number, an experiment involving an industry case study of implementing the fused deposition modeling (FDM) process with injection molding process shows the greatest impact, i.e., increase, in cost. Except for material efficiency, improvements are shown in scheduling and carbon footprint objectives.