Affordable Access

Access to the full text

Additive manufacturing processes from an environmental point of view: a new methodology for combining technical, economic, and environmental predictive models

Authors
  • Yosofi, Mazyar1
  • Kerbrat, Olivier1
  • Mognol, Pascal1
  • 1 University Rennes, CNRS, Gem - UMR 6183, Rennes, F-35000, France , Rennes (France)
Type
Published Article
Journal
The International Journal of Advanced Manufacturing Technology
Publisher
Springer London
Publication Date
Mar 06, 2019
Volume
102
Issue
9-12
Pages
4073–4085
Identifiers
DOI: 10.1007/s00170-019-03446-2
Source
Springer Nature
Keywords
License
Yellow

Abstract

Additive manufacturing is an innovative way of producing complex parts. There is abundant knowledge about mechanical properties and production costs, with many studies in the literature reporting comparisons between different additive manufacturing technologies based on technical or economic criteria. However, there has been only limited environmental analysis of what happens during the manufacturing stage. Most of the studies concern the specific energy consumption and only rely on electrical consumption of the machine during the building process. In this paper, environmental impact is based on life cycle inventory data (energy, material, fluid). To ensure the continuing development of these processes, it is important to develop predictive models of the environmental impact, so that products can be evaluated not only from a technical and economic perspective, but also from an environmental point of view. This paper describes a new methodology for developing predictive models to jointly evaluate technical, economic, and environmental data. First, the authors propose a generic method to study the environmental aspects during the manufacturing stage. Then, these aspects are combined technical and cost information. This methodology can be applied to numerous additive manufacturing processes and will enable manufacturers to choose manufacturing technology or machine based on multiple criteria.

Report this publication

Statistics

Seen <100 times