Advanced Electronic Controller Circuits Enabling Production Processes and AI-driven KM in Industry 5.0

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration Pub Date : 2025-03-25 DOI:10.1016/j.jii.2025.100841

Alessandro Massaro, Francesco Santarsiero, Giovanni Schiuma

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引用次数: 0

Abstract

The proposed paper presents a methodology for mapping electronic manufacturing control processes within a Knowledge Management (KM) framework, aligning with human-centric and transdisciplinary approaches. Specifically, the paper explores a Proportional-Integral-Derivative (PID) process for tuning production machinery, facilitating quality management and predictive maintenance through an AI-driven model. The PID circuit model is designed using the LTspice tool, while the entire production workflow is structured according to the Business Process Model and Notation (BPMN) standard. The model incorporates Artificial Intelligence (AI) to optimize machine control, establishing an advanced Digital Twin (DT) model that enables interactive human-system collaboration. The work further describes Knowledge Base (KB) data sources that support KM within Industry 5.0 environments, emphasizing AI-enhanced, user-centered control systems. Finally, the paper discusses new managerial roles and skill sets necessary for overseeing these integrated, human-centric KM systems in next-generation industrial applications.

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先进的电子控制器电路支持工业5.0中的生产过程和人工智能驱动的KM

本文提出了一种在知识管理（KM）框架内映射电子制造控制过程的方法，与以人为中心和跨学科的方法保持一致。具体来说，本文探讨了一种比例-积分-导数（PID）过程，通过人工智能驱动的模型来调整生产机械，促进质量管理和预测性维护。PID电路模型是使用LTspice工具设计的，而整个生产工作流是根据业务流程模型和符号（BPMN）标准构建的。该模型结合了人工智能（AI）来优化机器控制，建立了一个先进的数字孪生（DT）模型，实现了人机交互协作。该工作进一步描述了在工业5.0环境中支持KM的知识库（KB）数据源，强调了人工智能增强的、以用户为中心的控制系统。最后，本文讨论了在下一代工业应用中监督这些集成的、以人为中心的知识管理系统所必需的新的管理角色和技能。

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

Journal of Industrial Information Integration Decision Sciences-Information Systems and Management

CiteScore

22.30

自引率

13.40%

发文量

100

期刊介绍： The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers. The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.