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• W4384696554 abstract "Purpose This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options. Design/methodology/approach A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework. Findings The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research. Practical implications The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements. Originality/value The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries." @default.
• W4384696554 created "2023-07-20" @default.
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• W4384696554 date "2023-07-19" @default.
• W4384696554 modified "2023-09-25" @default.
• W4384696554 title "A decision-support framework for selecting additive manufacturing technologies" @default.
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• W4384696554 doi "https://doi.org/10.1108/jmtm-02-2023-0047" @default.
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