Sustainable Manufacturing and Applications of Wide-Bandgap Semiconductors—A Review

Wide-bandgap (WBG) semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) are revolutionizing high-power electronics due to their superior thermal conductivity, breakdown voltage, and energy efficiency. These materials are critical in electric vehicles, renewable energy systems, and high-frequency applications like 5G infrastructure. However, their production processes are resource-intensive and present significant environmental challenges. This review evaluates recent advancements in sustainable WBG semiconductor manufacturing, focusing on low-energy epitaxial growth, closed-loop recycling, and the mitigation of toxic by-products. Additionally, it highlights the role of Industry 4.0 innovations, such as AI-driven process optimization and IoT-based resource management, in enhancing sustainability. The review identifies research gaps in cost reduction, alternative WBG materials like Gallium Oxide (Ga₂O₃) and Diamond, and scalable green manufacturing solutions. It underscores the necessity for industry-wide collaboration and regulatory frameworks to drive the adoption of eco-friendly semiconductor fabrication. The findings of this study provide a roadmap for advancing sustainability in WBG semiconductor production, ensuring their long-term viability in the transition toward energy-efficient technologies.

A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT

This article introduces OPRA (Observation-Prompt-Response-Action) and its multi-agent extension, COPRA (Collaborative OPRA), as frameworks offering alternatives to traditional agent architectures in intelligent manufacturing systems. Designed for adaptive decision-making in dynamic environments, OPRA enables agents to request external knowledge—such as insights from large language models—to bridge gaps in understanding and guide optimal actions in real-time. When predefined rules or operational guidelines are absent, especially in contexts marked by uncertainty, complexity, or novelty, the OPRA framework empowers agents to query external knowledge systems (e.g., ChatGPT), supporting decisions that traditional algorithms or static rules cannot adequately address. COPRA extends this approach to multi-agent scenarios, where agents collaboratively share insights from prompt-driven responses to achieve coordinated, efficient actions. These frameworks offer enhanced flexibility and responsiveness, which are critical for complex, partially observable manufacturing tasks. By integrating real-time knowledge, they reduce the need for extensive training data and improve operational resilience, making them a promising approach to sustainable manufacturing. Our study highlights the added value OPRA provides over traditional agent architectures, particularly in its ability to adapt on-the-fly through knowledge-driven prompts and reduce complexity by relying on external expertise. Motivational scenarios are discussed to demonstrate OPRA’s potential in critical areas such as predictive maintenance.

Artificial Intelligence and Machine Learning for Sustainable Manufacturing: Current Trends and Future Prospects

Artificial Intelligence (AI) and Machine Learning (ML) are transforming manufacturing processes, offering unprecedented opportunities to enhance sustainability and environmental stewardship. This comprehensive review analyzes the transformative impact of AI technologies on sustainable manufacturing, focusing on critical applications, including energy optimization, predictive maintenance, waste reduction, and circular economy implementation. Through systematic analysis of current research and industry practices, the study examines both the opportunities and challenges in deploying AI-driven solutions for sustainable manufacturing. The findings provide strategic insights for researchers, industry practitioners, and policymakers working towards intelligent and sustainable manufacturing systems while elucidating emerging trends and future directions in this rapidly evolving field.

Maturity Model for the Manufacturing Industry with Case Experiences

This manuscript describes the research path when extending a maturity model. The initial model—ManuMaturity—was for manufacturing companies aiming beyond Industry 4.0. The extended OSME model covers data sharing within a supply chain, an open innovation ecosystem and sustainable manufacturing. The OSME maturity model has five maturity levels: traditional factory, modern factory, agile factory, agile cognitive factory and agile cognitive industry and seven dimensions (such as infrastructure, data, customer, business model, employee, sustainability and processes). The tool was experimented with in manufacturing companies on two occasions: with a set of manufacturing companies and a group of companies. In both cases, feedback was gathered from the respondents. The article follows the maturity assessment development phases such as scope, design, populate, test, deploy and maintain, and reports the software implementation of the maturity tool. With the help of the developed maturity model and the tool, it was possible to make assessments in case companies, where the tool and its results were commented mostly positively. The tool can be applied in various ways. For example, a group of people can jointly submit their common understanding and have a thorough discussion or a group of company representatives submit their responses and the variation is discussed afterwards.

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.

Design of Intelligent and Sustainable Manufacturing Production Line for Automobile Wheel Hub

The wheel hub is an important part of the automobile, and machining affects its service life and driving safety. With the increasing demand for wheel productivity and machining accuracy in the automotive transport sector, automotive wheel production lines are gradually replacing human production. However, the technical difficulties of conventional automotive wheel production lines include insufficient intelligence, low machining precision, and large use of cutting fluid. This paper aims to address these research constraints. The intelligent, sustainable manufacturing production line for automobile wheel hub is designed. First, the machining of automotive wheel hubs is analyzed, and the overall layout of the production line is designed. Next, the process equipment system including the fixture and the minimum quantity lubrication (MQL) system are designed. The fixture achieves self-positioning and clamping functions through a linkage mechanism and a crank–slider mechanism, respectively, and the reliability of the mechanism is analyzed. Finally, the trajectory planning of the robot with dual clamping stations is performed by RobotStodio. Results show the machining parameters for a machining a wheel hub with a diameter of 580 mm are rotational speed of 2500 rpm, cutting depth of 4 mm, feed rate of 0.5 mm/r, and minimum clamping force of 10881.75 N. The average time to move the wheel hub between the roller table and each machine tool is 27 s, a reduction of 6 s compared with the manual handling time. The MQL system effectively reduces the use of cutting fluid. This production line can provide a basis and reference for actual production by reasonably planning the wheel hub production line.

Advanced Materials & Sustainable Manufacturing: A New Open-Access Journal to Share Your Research on Advanced Materials and Sustainable Manufacturing

A New Open-Access Journal of Intelligent and Sustainable Manufacturing (ISM)