Additive manufacturing makes it possible to improve on parts production methods. However, additive manufacturing technologies are diverse, service providers are numerous and very little staff is trained to operate these technologies.
The goal of this study is to suggest concepts for a decision support system, which will provide adapted recommendations relative to the choice of technologies, materials and postprocessing for industrials in the aerospace industry.
In order to achieve this design proposition goal, three sub-goals have been determined. Firstly, the definition of additive manufacturing technologies, materials and post-processing decision-making criterias. Secondly, suggested specifications for such a decision support system, in accordance with industrial needs in the aerospace industry. Finally, three suggested decision support systems designs and their evaluation, in comparison with the determined specifications.
The criteria, which have been identified from 11 industrials in this study, concerns cost, quality, design and delay of obtention categories of criterias. The recommendations, which are made for the design of a decision support system, include: a user interface proposition, a suggestion for a database containing the necessary knowledge for decision-making and an additive manufacturing technology, materials and post-processing selection engine. Having a user-friendly interface, being able to evaluate the level of quality needed and using case studies as part of the decision support system selection engine are three examples of needs gathered in this study. Eventually, we were able to convert these needs into technical requirements that permitted the industrials to evaluate the suggested concepts.
We consequently defined three decision support system design suggestions, which were respectively developed using Microsoft Excel®, Microsoft Access® and an online Platform associated to the machine learning software RapidMiner®. This third tool particularly caught the engineers’ attention who gave it a higher rating based mainly on its factorization of case study data in the determination of suggested preferential technologies, materials and postprocessing.
| Date | 27 Jun 2016 |
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| Original language | French |
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| Awarding Institution | - École de technologie supérieure
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| Supervisor | Yvan Beauregard (Supervisor) & Sylvie Doré (Co-supervisor) |
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Buvat, G. (Author),
Beauregard (Supervisor) & Doré (Co-supervisor),
27 Jun 2016Student thesis: Master's thesis › Master in Engineering: Engineering