A Decision Support System for Selecting Additive Manufacturing Technologies

Park, Hong Seok; Tran, Ngoc-Hien

doi:10.1145/3077584.3077606

Cited by 11 publications

(11 citation statements)

References 4 publications

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“…That is, this research stands out for proposing a methodology for selecting technologies integrated with product and process development. Park & Tran (2017) provide a decision support system to select an appropriate 3D printing method for printing end products, using Java and SQL. Based on product requirements, printing methods have been analyzed and evaluated to decide which one meets the product requirements in the best way (Park & Tran, 2017).…”

Section: Selection Of Technologies For Additive Manufacturingmentioning

confidence: 99%

“…Additionally, it complements the criteria used (material and component specification, part application, technological limitations, accuracy, finishing quality and post processing) for future research. Park & Tran (2017), present their research in a partial manner, that is, without demonstrating the steps followed for validation and development of the system. In this way, this research will contribute as a reference for future work to present its system in a functional and total manner, in addition to clearly exposing validation processes, choice of AM technologies and decision-making during the development.…”

Section: Comparative Analysis Of the Model Of This Research With The Literaturementioning

confidence: 99%

“…However, each AM technology has advantages and disadvantages that may be suitable, or not, depending on the competitive criteria prioritized in the production system under analysis, being one of the reasons why the technology should be technically selected. Additive Manufacturing technologies have divergent aspects, which aligned to the lack of benchmarking standards and the lack of experience by the organizations makes selecting AM technology complex (Park & Tran, 2017).…”

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confidence: 99%

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Selection of additive manufacturing technologies in productive systems: a decision support model

2020

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Abstract: Additive Manufacturing (AM) has seen continued growth in adoption by organizations in recent years, changing production processes, supply chain, maintenance, product development and the global economy. There are several Additive Manufacturing technologies and equipment on the market, however, there are no guidelines, benchmarking or decision support tools for proper selection. After a systematic review of the literature, the lack of propositions that act during the development of the product and process was evidenced. This research focuses on the selection of Additive Manufacturing technologies for a production system. The general objective being to propose a decision support model based on the characteristics of additive technologies and competitive criteria, resulting in a choice aligned with the guidelines of organizations and their production systems. For the operationalization of the model, the AHP techniques and conjoint analysis were used together, where the characteristics of the Additive Manufacturing technologies were related to the competitive criteria for the model to indicate the recommended technology to the production system or organization in question. Finally, the artifact recommended the right technology in three distinct situations, from a vendor, user, and expert point of view. Thus, this research contributes to both academia and business by developing a functional artifact of additive manufacturing technology selection. Also, by contributing to the increased availability of information on the nine most commonly used additive technologies in industry.

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Section: Selection Of Technologies For Additive Manufacturingmentioning

confidence: 99%

Section: Comparative Analysis Of the Model Of This Research With The Literaturementioning

confidence: 99%

mentioning

confidence: 99%

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Selection of additive manufacturing technologies in productive systems: a decision support model

2020

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“…Concerning their architecture, most of the DSS incorporate expert systems based on rules for assessing the situations presented [21][22][23]. Some of the most recent ones also incorporate or assisted [17,24,25] machine learning procedures to enlarge its knowledge base during its operation.…”

Section: Formal Models and Trends Of Dss Applied To Manufacturing Promentioning

confidence: 99%

“…Most of models use as parameters variables that evaluate economic and time aspects (i.e., costs and times), which are included at some point in almost every system developed. Many models incorporate technological rules and advise on manufacturing best practices [17,23]. Finally, the newest models usually incorporate additional variables related to energy use [20], sustainability of the technology [31] and/or user-friendliness in order to build a balanced scorecard for decision-making.…”

Section: Formal Models and Trends Of Dss Applied To Manufacturing Promentioning

confidence: 99%

Decision Support Models for the Selection of Production Strategies in the Paradigm of Digital Manufacturing, Based on Technologies, Costs and Productivity Levels

Minguella-Canela¹,

Buj-Corral²

2020

New Trends in the Use of Artificial Intelligence for the Industry 4.0

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Digital manufacturing has opened a new window in the way to approach the manufacture of parts. The possible switch from manufacturing and holding physical stock to manoeuvring with a fully-digital one is promising but still has not been undertaken-or only in a small proportion-by the majority of the manufacturing companies. What are the cost and productivity frontiers that halt the transformation taking place so far? When does it make sense, in terms of production volume and costs, to undertake this transformation? What level of savings could be achieved and what investments would be favourable? The base line of the present chapter is to depict quantitative tools to address the potential impact of endeavouring digital transformation in manufacturing environments, considering costing and production variables, as well as technological decision-making parameters. Keeping the modelling of the demand very basic, some exploration on the degree of postponement of the production is discussed. Also, decision support systems (DSSs) for manufacturing selection are reviewed. Finally, a case study serves to apply the mathematical framework presented and to quantify the results in a realistic industrial case. Using this case, the chapter outlines and describes how to apply artificial intelligence (AI) techniques to implement the DSSs.

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Decision Support System for a Metal Additive Manufacturing Process Chain Design for the Automotive Industry

Kratzer

Mayer

Höfler

et al. 2020

Industrializing Additive Manufacturing

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A Decision Support System for Selecting Additive Manufacturing Technologies

Cited by 11 publications

References 4 publications

Selection of additive manufacturing technologies in productive systems: a decision support model

Selection of additive manufacturing technologies in productive systems: a decision support model

Decision Support Models for the Selection of Production Strategies in the Paradigm of Digital Manufacturing, Based on Technologies, Costs and Productivity Levels

Decision Support System for a Metal Additive Manufacturing Process Chain Design for the Automotive Industry

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