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A Framework for Additive Manufacturing Technology Selection: A Case for the Rail Industry

A Framework for Additive Manufacturing Technology Selection: A Case for the Rail Industry

Rumbidzai Muvunzi, Khumbulani Mpofu, Malika Khodja, Ilesanmi Daniyan
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 21
ISSN: 2156-1680|EISSN: 2156-1672|EISBN13: 9781683182283|DOI: 10.4018/IJMMME.302912
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MLA

Muvunzi, Rumbidzai, et al. "A Framework for Additive Manufacturing Technology Selection: A Case for the Rail Industry." IJMMME vol.12, no.1 2022: pp.1-21. http://doi.org/10.4018/IJMMME.302912

APA

Muvunzi, R., Mpofu, K., Khodja, M., & Daniyan, I. (2022). A Framework for Additive Manufacturing Technology Selection: A Case for the Rail Industry. International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME), 12(1), 1-21. http://doi.org/10.4018/IJMMME.302912

Chicago

Muvunzi, Rumbidzai, et al. "A Framework for Additive Manufacturing Technology Selection: A Case for the Rail Industry," International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME) 12, no.1: 1-21. http://doi.org/10.4018/IJMMME.302912

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Abstract

Additive manufacturing is a popular emerging technology of producing parts directly from digital models. This technology has presented benefits such as freedom of design, the ability to customise, and shortened process chains. Presently, there are different Additive Manufacturing (AM) processes that are available in the market. Often, transport equipment manufacturing companies are faced with challenges while selecting the AM processes that are suited to their needs. Decision-makers need to consider all the underlining factors before a conclusion is reached. This paper proposes an approach that can be used by companies in the rail sector to select AM technologies that are suited to their applications. The approach involves identifying suitable parts, comparing applicable AM technologies and selecting the most suitable technology. The next stage involves re-designing the parts based on the selected technology. The approach is applied to benchmark parts from the industry. The study provides enlightenment on how AM can be applied in the rail industry.