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INFORMATION FLOW ANALYSIS ENABLING THE INTRODUCTION OF ADDITIVE MANUFACTURING FOR PRODUCTION TOOLS-INSIGHTS FROM AN INDUSTRIAL CASE

Published online by Cambridge University Press:  19 June 2023

Tina Hajali*
Affiliation:
Chalmers University of Technology;
Adam Mallalieu
Affiliation:
Chalmers University of Technology;
Arindam Brahma
Affiliation:
Chalmers University of Technology;
Massimo Panarotto
Affiliation:
Chalmers University of Technology;
Ola Isaksson
Affiliation:
Chalmers University of Technology;
Lina Stålberg
Affiliation:
Mälardalen University
Johan Malmqvist
Affiliation:
Chalmers University of Technology;
*
Hajali, Tina, Chalmers University of Technology, Sweden, tina.hajali@chalmers.se

Abstract

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Additive Manufacturing (AM) has traditionally been used for prototyping of products, however, in the last few decades, it has seen a rising growth in the manufacture of final products. The addition of AM as a manufacturing method in the portfolio of a company's production capabilities increases the complexity of decision-making. This is because the decisions are often not based on the same criteria and constraints, as related to conventional manufacturing processes. In this paper, we investigate this challenge by studying how AM affects the current workflow and the associated information flow for a design-make process in a Swedish manufacturer before and after the integration of AM. In this paper, it is argued that apart from an understanding of how to design for AM, it is equally important to consider how introducing AM alters the existing information flow and how to benefit from information available in various design-make process steps to facilitate decision making process. The result clarifies that the current process relies largely on tacit and experiences-based knowledge, whereas to take advantage of AM, more precision is required to capture and process the available information.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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