协同制造技术:综述

Quadri A. Mumuni, A. Adenowo, L. Akinyemi, O. Shoewu, C. O. Folorunso
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引用次数: 0

摘要

当今经济的竞争性质迫使制造业部门,中小型制造企业(SMMES),与其他部门合作,以实现稳定和一致性。制造业企业正在投入大量精力管理他们的产品和服务,以达到高水平的客户满意度。这是以最高的质量完成的,同时保持有竞争力的成本标签。为此,采用了协同制造技术(CMT)。它需要在涉及价值层次结构中的内部或外部利益相关者的业务流程之间进行信息交换和对话。一个包含这些现有协作网络的主动CMT模型应该提供运营价值节约并显著提高竞争力。因此,最近对这种列入提供详细看法的评价已不复存在。为了促进软件产品成功开发中的协作技术,本文提供了对当前协作模型、各自的好处以及它们的协作特性的完整研究。本文概述了cmt的最新机制、方法和应用可能性。此外,本文还全面考察了目前使用CMT来解决科学和工程问题的技术。更具体地说,它提出了一种革命性的方法,通过应用机器学习(ML),人工智能以及遗传算法和粒子群优化(AI)等元启发式方法来增强当前的CMT方法。总之,这项研究突出了CMT可能很快应用的某些领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative Manufacturing Techniques: A Review
The competitive nature of today's economies has forced the manufacturing sector, small and medium-sized manufacturing enterprises (SMMES), to collaborate with other sectors to achieve stability and consistency. Manufacturing businesses are putting a lot of effort into managing their goods and services to reach a high level of client satisfaction. This is accomplished with the highest quality while maintaining a competitive cost tag. To accomplish this, the collaborative manufacturing technique (CMT) is used. It entails information exchange and dialogue amongst business processes concerning internal or external stakeholders in the hierarchy of value. An active CMT model that incorporates these present collaboration networks should provide operational value savings and significantly increase competitiveness. Therefore, the recent evaluation that provided a detailed view of such inclusion is no longer in existence. To promote collaboration techniques in the successful development of software products, this article provides a complete study of current collaborative models, respective benefits, and their collaborative features. This paper outlines the most recent mechanisms, approaches, and application possibilities for CMTs. In addition, the review paper thoroughly examines the techniques currently in use for employing CMT to solve problems in both science and engineering. More specifically, it suggests a revolutionary method for enhancing the current CMT methods through the application of machine learning (ML), artificial intelligence, and metaheuristics like genetic algorithms and particle swarm optimization (AI). In summary, this research highlights certain areas where CMT may be used soon.
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