Identified research directions for using manufacturing knowledge earlier in the product life cycle
Thomas D. Hedberg, Nathan W. Hartman, Phil Rosche, Kevin Fischer
The fundamental study of Design for Manufacturing (DFM), and the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, little industry practice has been studied to provide solutions that are ready for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. The functionality may or may not dynamically affect geometry creation, and is typically a customization on a case-by-case basis if the functionality exists in the CAD system. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the affects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. The current state for DFM identifies the question of how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge. The results highlight several research questions needing more study. This paper aims to propose a strategy for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the strategy includes investigating the system-level barriers to reusing manufacturing knowledge. Lastly, the strategy addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle.
, Hartman, N.
, Rosche, P.
and Fischer, K.
Identified research directions for using manufacturing knowledge earlier in the product life cycle, International Journal of Production Research, [online], https://doi.org/10.1080/00207543.2016.1213453
(Accessed March 31, 2023)