加工工艺能效促进可持续性的最新进展综述

IF 3 4区 工程技术 Q3 ENERGY & FUELS Energies Pub Date : 2024-07-25 DOI:10.3390/en17153659
Shailendra Pawanr, K. Gupta
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引用次数: 0

摘要

追求加工过程的能源效率是可持续制造的一个重要方面。全球能源消耗的很大一部分来自工业部门;因此,提高加工过程的能效可以带来巨大的环境和经济效益。本研究回顾了最近在提高加工过程能效方面取得的进展。首先探讨了机械加工过程的能耗,然后确定了其能耗建模的关键领域和发展。随后,研究探讨了实现机械加工节能的各种策略。这些策略包括高能效机床、加工过程能耗的精确建模、优化技术的实施以及人工智能(AI)的应用。此外,综述还强调了人工智能在进一步降低加工操作能耗和实现能源效率方面的潜力。对加工过程中这些节能策略的回顾显示了大幅降低能耗的巨大潜力:高能效设计可实现高达 45% 的能耗降低,优化切削参数可最大限度地降低约 40% 的能耗,优化刀具路径可降低约 50% 的能耗,优化非切削能耗和排序可节省约 30% 的能耗,而采用人工智能则有望提高约 20% 的能效。总之,本综述对提高加工过程能效的最新进展提供了宝贵的见解。它确定了可实现显著节能的关键领域。
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A Review on Recent Advances in the Energy Efficiency of Machining Processes for Sustainability
The pursuit of energy efficiency in machining processes is a critical aspect of sustainable manufacturing. A significant portion of global energy consumption is by the industrial sector; thus, improving the energy efficiency of machining processes can lead to substantial environmental and economic benefits. The present study reviews the recent advancement made for improving the energy efficiency of machining processes. First the energy consumption of the machining processes was explored and then the key areas and developments in their energy consumption modeling were identified. Following this, the review explores various strategies for achieving energy savings in machining. These strategies include energy-efficient machine tools, the accurate modeling of the energy consumption of machining processes, the implementation of optimization techniques and the application of artificial intelligence (AI). Additionally, the review highlights the potential of AI in further reducing energy consumption within machining operations and achieving energy efficiency. A review of these energy-saving strategies in machining processes reveals impressive potential for significant reductions in energy consumption: energy-efficient design can achieve up to a 45% reduction, optimizing cutting parameters may minimize consumption by around 40%, optimizing tool paths can reduce consumption by approximately 50%, optimizing non-cutting energy consumption and sequencing can lead to savings of about 30% and employing AI shows promising energy efficiency improvements of around 20%. Overall, the present review offers valuable insights into recent advancements in making machining processes more energy-efficient. It identifies key areas where significant energy savings can be achieved.
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来源期刊
Energies
Energies ENERGY & FUELS-
CiteScore
6.20
自引率
21.90%
发文量
8045
审稿时长
1.9 months
期刊介绍: Energies (ISSN 1996-1073) is an open access journal of related scientific research, technology development and policy and management studies. It publishes reviews, regular research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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