%0 Conference Proceedings
%T Part Selection for Freeform Injection Molding: Framework for Development of a Unique Methodology
%+ Aalborg University [Denmark] (AAU)
%+ AddiFab ApS
%+ The Maersk Mc-Kinney Moller Institute
%A Sharifi, Elham
%A Chaudhuri, Atanu
%A Wæhrens, Brian, Vejrum
%A Staal, Lasse, G.
%A Farahani, Saeed, D.
%Z Part 13: Production Ramp-Up Strategies for Product
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Novi Sad, Serbia
%Y Bojan Lalic
%Y Vidosav Majstorovic
%Y Ugljesa Marjanovic
%Y Gregor von Cieminski
%Y David Romero
%I Springer International Publishing
%3 Advances in Production Management Systems. Towards Smart and Digital Manufacturing
%V AICT-592
%N Part II
%P 723-730
%8 2020-08-30
%D 2020
%R 10.1007/978-3-030-57997-5_83
%K Freeform injection molding
%K Additive manufacturing
%K Part identification
%K Technical and economic factors
%Z Computer Science [cs]Conference papers
%X The purpose of this study is to provide an overview of a methodology, which will enable industrial end-users to identify potential components to be manufactured by Freeform Injection Molding (FIM). The difference between the technical and economic criteria needed for part selection for Additive Manufacturing (AM) and FIM will be discussed, which will lead us towards proposing a new methodology for part selection for FIM. Our proposed approach starts by identifying the most similar components (from end-user part libraries) to some reference parts, which can be produced by FIM. Identification will be followed by cluster analysis based on important factors for FIM part selection. As there are some interdependency between the factors involved in the clusters, some decision rules using Fuzzy Interference System (FIS) will be applied to rank the parts within each cluster using user-defined technical and economic criteria. Once the first set of potential FIMable parts have been identified, Design of Experiment (DOE) will be conducted to investigate which factors are most important and how they interact with each other to generate the desirable quality of the FIM parts. The DOE results will be validated in order to finetune the ranges of the parameters, which gives the best results. Finally, a predictive model will be developed based on the optimum feasible range of FIM parameters. This will help the end-users to analytically find the new FIMable parts without repeating the algorithm for the new parts.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-03635646/document
%2 https://inria.hal.science/hal-03635646/file/504014_1_En_83_Chapter.pdf
%L hal-03635646
%U https://inria.hal.science/hal-03635646
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-592