Systematic manufacturability evaluation using dimensionless metrics and singular value decomposition: a case study for additive manufacturing

Abstract

Additive manufacturing has been presented as a novel and competitive method to achieve unprecedented part shapes and material complexities. Though this holds true in niche markets, the economic viability of additive manufacturing for large-scale industrial production is still in question. Companies often struggle to justify their investment in additive manufacturing due to challenges in the integration of such technologies into mainstream production. First, most additive technologies exhibit a relatively low production rate when compared with traditional production processes. Second, there is a lack of robust design for additive manufacturing methods and tools that enable the leveraging of the attendant unique capabilities, including the ability to form organic part geometries and automated part consolidations. Third, there is a dearth of systematic part screening methods to evaluate manufacturability in additive manufacturing. To tackle the challenge of manufacturability evaluation, the present work proposes a novel approach derived from latent semantic analysis and dimensional analysis to evaluate parts and their production for a variety of selected metrics. The selected metrics serve as descriptors of design features and manufacturing functions, which are developed using functional modeling and dimensional analysis theory. Singular-value decomposition and Euclidean distance measurement techniques are used to determine the relative manufacturability for a set of parts for a specified manufacturing process technology. The utility of the method is demonstrated for laser powder bed fusion technology. While demonstrated for additive manufacturing here, the developed approach can be expanded for any given set of manufacturing processes. Expansion of this systemic manufacturability analysis method can support part design decision-making, process selection, and design and manufacturing optimization.

A dimensionless analysis to select directed energy deposition process parameters for proper clad formation

“…To study multiple parameters effectively, one typical strategy is to conduct a dimensionless analysis, which has been developed in several studies concerning the selective laser melting (SLM) technique [17][18][19][20]. Specifically, Mukherjee and coworkers [18] introduced a non-dimensional lackof-fusion number and showed its correlation with more common non-dimensional numbers, such as the Marangoni number, which the same group had elaborated in their earlier work [17].…”

“…Instead, Rankouhi et al [19] derived a more practical non-dimensional number that only requires process parameters that can be obtained prior to printing and can be interpreted as the product of the materials thermophysical properties (i.e., specific heat capacity and heat conductivity), laser volumetric energy density, and laser dwell time. Coatanéa et al [20] took the dimensionless analysis even further by combining it with functional modeling to develop parsimonious metrics, which showed promising capabilities for the evaluation of part manufacturability when coupled with singular value decomposition.…”

Digital Twin Science of Metal Powder Bed Fusion Additive Manufacturing: A Selective Review of Simulations for Integrated Computational Materials Engineering and Science

“…However, such simulations are often more time-consuming compared to experiments. At present, machine learning-based methods (106)(107)(108)(109)(110) are emerging as practical means of searching for optimal conditions while neglecting intrinsic physics. Future development of process optimization technology that combines machine learning technology with physical modelbased predictions will be important.…”

Footsteps Towards a Transdisciplinary Design and Process Science

“…The design should be desirable for users, feasible for the company to cover a need, and economically and environmentally viable. Along this line, Eric Coatanea has concentrated on two aspects of the design innovation process: first, to unveil hidden problems that can generate new needs (Moktharian et al, 2017), and second, to evaluate early innovative solutions before committing too many resources for the companies (Coatanéa et al, 2020). Computer-aided tools have been under development specific to the early phases of the design process to address those two key questions (Coatanéa et al, 2022).…”