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Sustainable Manufacturing and Design
Provides research and practices from a wide range of disciplines to help engineers design more environmentally sustainable products
Kaushik Kumar (Edited by), Divya Zindani (Edited by), J Paulo Davim (Edited by)
9780128221242, Elsevier Science
Paperback, published 12 January 2021
260 pages
22.9 x 15.1 x 1.7 cm, 0.94 kg
Sustainable Manufacturing and Design draws together research and practices from a wide range of disciplines to help engineers design more environmentally sustainable products. Sustainable manufacturing requires that the entire manufacturing enterprise adopts sustainability goals at a system-level in decision-making, hence the scope of this book covers a wide range of viewpoints in response. Advice on recyclability, zero landfill design, sustainable quality systems, and product take-back issues make this a highly usable guide to the challenges facing engineering designers today. Contributions from around the globe are included, helping to form an international view of an issue that requires a global response.
Section A: Sustainability methodology 1. Current tools and methodology for a sustainable product life cycle and design 2. Additive manufacturing for a dematerialised economy Section B: Sustainability in manufacturing 3. Sustainable friction stir welding of metals 4. Heat pipe-embedded tooling for sustainable manufacturing 5. Sustainable manufacturing of plastic packaging material: An innovative approach Section C: Sustainability in product design 6. Performance of microwave-irradiated WC-Co insert during dry machining of inconel 718 superalloys 7. Experimental studies on friction stir welding of AA6061 aluminum alloy 8. Machinability study of Inconel 825 superalloy under nanofluid MQL: application of sunflower oil as base cutting fluid with MWCNTs and nano-Al2O3 as additives Section D: Optimization 9. Performance enhancement approaches for Mahua biodiesel blend on diesel engine 10. Optimization of wear parameters of aluminium hybrid metal matrix composites by squeeze casting using Taguchi and artificial neural network
Subject Areas: Mechanical engineering [TGB]
