Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques

IF 4.6 Q2 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Recycling Pub Date : 2024-05-21 DOI:10.3390/recycling9030043
Mandar Golvaskar, Sammy A. Ojo, Manigandan Kannan
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引用次数: 0

Abstract

To improve the microstructure and mechanical properties of fundamental materials including aluminum, stainless steel, superalloys, and titanium alloys, traditional manufacturing techniques have for years been utilized in critical sectors including the aerospace and nuclear industries. However, additive manufacturing has become an efficient and effective means for fabricating these materials with superior mechanical attributes, making it easier to develop complex parts with relative ease compared to conventional processes. The waste generated in additive manufacturing processes are usually in the form of powders, while that of conventional processes come in the form of chips. The current study focuses on the features and uses of various typical recycling methods for traditional and additive manufacturing that are presently utilized to recycle material waste from both processes. Additionally, the main factors impacting the microstructural features and density of the chip-unified components are discussed. Moreover, it recommends a novel approach for recycling chips, while improving the process of development, bonding quality of the chips, microstructure, overall mechanical properties, and fostering sustainable and environmentally friendly engineering.
材料废物回收利用的最新进展:传统、直接转化和增材制造技术
为了改善铝、不锈钢、超级合金和钛合金等基本材料的微观结构和机械性能,航空航天和核工业等关键领域多年来一直采用传统制造技术。然而,与传统工艺相比,增材制造已成为制造这些具有卓越机械属性的材料的一种高效且有效的手段,使复杂零件的开发变得相对容易。快速成型制造工艺产生的废料通常以粉末的形式存在,而传统工艺产生的废料则以芯片的形式存在。当前的研究重点是传统制造和快速成型制造的各种典型回收方法的特点和用途,目前这两种方法都用于回收这两种工艺产生的材料废料。此外,还讨论了影响芯片统一部件的微观结构特征和密度的主要因素。此外,报告还推荐了一种回收芯片的新方法,同时改进了芯片的开发过程、粘接质量、微观结构和整体机械性能,并促进了可持续和环境友好型工程的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Recycling
Recycling Environmental Science-Management, Monitoring, Policy and Law
CiteScore
6.80
自引率
7.00%
发文量
84
审稿时长
11 weeks
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