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Reusing and Extending Standards-based Unit Manufacturing Process Models for Characterizing Sustainability Performance
Published
Author(s)
Arvind Shankar Raman, KC Morris, Karl R. Haapala
Abstract
Over the past two decades numerous efforts have characterized manufacturing processes for sustainability performance. These efforts have been pursued primarily by manufacturing researchers in academic and governmental labs, and involve the development of frameworks, methodologies, and standards for characterizing discrete manufacturing processes and their representation as information models. Further, characterization of sustainability performance of manufacturing process flows has been attempted through linking, or composing, these unit manufacturing process (UMP) models. This paper reviews these efforts and identifies existing research gaps that should be addressed by academic, industrial, and governmental researchers. The review includes the relevant sustainable manufacturing standards that have been recently published by ASTM International. A methodology for creating and extending composable models of UMPs that builds upon these standards is presented. This research demonstrates how formalization of these prior efforts can address the identified gaps. It is shown that reuse of UMP models can be enabled by encapsulating specific characteristics of complex processes into information models that can be applied for detailed process analysis and evaluation. This research proposes the concept of a template UMP information model, which can further be abstracted and customized to represent an application-specific, higher-order manufacturing process model. The template model concept is illustrated for manual and computer numerically controlled (CNC) milling processes.
Citation
ASME Journal of Computing and Information Science in Engineering
Shankar Raman, A.
, Morris, K.
and Haapala, K.
(2023),
Reusing and Extending Standards-based Unit Manufacturing Process Models for Characterizing Sustainability Performance, ASME Journal of Computing and Information Science in Engineering, [online], https://doi.org/10.1115/1.4054487, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932706
(Accessed December 26, 2024)