A big data-driven framework for sustainable and smart additive manufacturing |
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| Affiliation: | 1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Shaanxi 710072, PR China;2. School of Modern Post, Xi''an University of Posts and Telecommunications, Shaanxi 710061, PR China;3. Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, School of Construction Machinery, Chang''an University, Xi''an 710064, Shaanxi, China;4. Department Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China;5. School of Mechanical Engineering, Shandong University, Jinan 250061, China;6. Xi''an Research Institute of Navigation Technology, China Electronics Technology Group Corporation, Xi''an 710068, China;7. School of Mechanical Engineering, Shaanxi University of Technology, Shaanxi 723001, PR China;1. Sustainable Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and Manufacturing Engineering, The University of New South Wales Sydney, Sydney, NSW 2052, Australia;2. School of Management and Enterprise, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Springfield, QLD 4305, Australia |
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| Abstract: | From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production. |
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