先进制造业中的人工智能 | 多刊征稿合集

人工智能正在改变先进制造业，推动更智能、更互联的生产系统，从而实现工业 4.0 的目标。从机器学习优化工厂流程，到网络物理系统实现实时适应性，AI 正以惊人的精准度应对诸如可持续性和效率等重大挑战。得益于人工智能驱动的制造业研究的迅猛发展，我们很高兴推出这一特别系列。让我们一起探索 AI 如何革新生产、激发创新，并塑造智能工厂的未来。

本集刊旨在汇集有关人工智能在先进制造业中应用的前沿研究和全面综述。通过促进人工智能、工程学和制造科学领域的跨学科合作，该期刊旨在阐明人工智能如何提升生产系统、推动创新，并解决诸如可持续性和可扩展性等工业难题，从而为智能工厂和工业 4.0 奠定基础。

• AI for Process Optimization: Machine learning and deep learning for real-time process control, parameter optimization, and quality assurance in manufacturing (e.g., machining, casting).

• Predictive Maintenance and Fault Diagnosis: AI-driven predictive analytics for equipment health monitoring, anomaly detection, and remaining useful life estimation.

• Additive Manufacturing Innovations: AI applications in material selection, defect detection, and topology optimization for 3D printing and advanced geometries.

• Human-Robot Collaboration: AI-enabled collaborative robots (cobots) for safe, efficient human-machine interaction in smart manufacturing environments.

• Sustainability and Digital Twins: AI strategies for reducing waste, optimizing energy use, and implementing digital twins for real-time production system optimization.

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人工智能正在重塑先进制造与材料科学，推动工业4.0时代的智能化和可持续发展。从优化材料性能的机器学习，到提升制造精度的信息物理系统，这一技术正攻克资源效率与规模化等核心难题。

本期特辑旨在汇集AI融合材料科学与先进制造的前沿研究，通过促进跨学科合作，重点展示AI如何革新材料设计、优化生产工艺，并应对可持续性与性能优化等产业挑战，为新一代材料与生产系统奠定基础。

本专题将通过高质量研究成果，为研究人员与行业领袖提供重要参考，推动可持续材料创新，助力全球智能制造发展。

• AI for Material Design and Optimization: Machine learning and deep learning for predicting material properties, alloy development, and process parameter tuning (e.g., heat treatment, additive manufacturing).

• Predictive Maintenance in Material Processing: AI-based analytics for monitoring equipment health, detecting defects, and estimating the lifespan of materials processing tools.

• Additive Manufacturing with AI: AI applications in material selection, defect identification, and structural optimization for 3D printing of advanced materials.

• Smart Manufacturing Systems: AI-driven automation and human-robot collaboration for efficient material handling and production in smart factory settings.

• Sustainable Materials Processing: AI strategies for minimizing waste, optimizing energy consumption, and integrating digital twins for real-time material and manufacturing system analysis.

📌本期特刊截稿日期：

30 July 2026

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期刊简介

人工智能正在重新定义聚合物制造业，构建符合工业4.0标准的智能自适应生产体系。从机器学习驱动的工艺优化，到实时映射生产线的数字孪生技术，AI正在破解聚合物行业在可持续性、质量控制与能源效率方面的关键难题。

本期特刊聚焦人工智能与聚合物制造的交叉领域，旨在整合前沿研究与深度综述，通过促进聚合物工程师、AI专家与制造技术专家的跨学科协作，展示聚合物加工、可持续制造与智能生产系统的最新创新成果。我们诚邀关于聚合物制造的原创研究、综述及工业案例研究投稿。

• Smart Polymer Manufacturing: AI applications optimize polymer processing techniques, including extrusion, molding, and compounding.

• Machine Learning in Polymer Processing: Real-time parameter tuning, defect detection, and process control for consistent product quality.

• Process Optimization in Extrusion and Molding: AI-driven techniques to fine-tune polymer flow, temperature control, and tooling adjustments.

• Data-Driven Quality Control in Polymer Fabrication: Predictive analytics for quality assurance and reducing process variability.

• Predictive Maintenance for Polymer Production Lines: AI solutions for monitoring equipment health, anomaly detection, and minimizing downtime.

• Intelligent Additive Manufacturing of Polymers: Enhancing material selection, topology optimization, and defect mitigation in 3D printing of polymers.

• Digital Twins in Polymer Engineering: Virtual replicas of polymer manufacturing systems for real-time monitoring, simulation, and optimization.

• Sustainable Polymer Processing: AI approaches for waste reduction, circular economy initiatives, and environmentally friendly manufacturing practices.

• Energy-Efficient Manufacturing Technologies: AI-driven optimization of energy consumption during polymer processing.

• Adaptive Process Control in Polymer Industries: AI-enabled adaptive systems for dynamic process adjustments based on real-time data.

• Next-Generation Polymer Composites Manufacturing: Smart manufacturing strategies for producing advanced polymer composites.

• Industrial Automation for Polymer Materials: Robotic systems and automation for handling, processing, and assembly of polymer components.

• Real-Time Process Analytics in Polymer Engineering: In-line sensors and AI analytics for continuous monitoring and control of polymer manufacturing processes.

📌本期特刊截稿日期：

30 July 2026

📤  欢迎分享给你的研究群组、学生、合作伙伴～

期刊简介

人工智能正在重塑先进制造业，推动工业4.0智能互联生产体系的发展。从优化工厂流程的机器学习到实现实时自适应生产的信息物理系统，AI正以惊人精度应对可持续性与效率等重大挑战。

本期特辑旨在汇集AI在先进制造领域应用的前沿研究，通过促进跨学科合作，阐释AI如何提升生产系统、驱动创新并解决可持续性及规模化等产业难题，为智能制造铺平道路。

本专题将整合高质量研究成果，为研究人员、行业专家及政策制定者提供关键参考，推动制造业变革，支撑“中国制造2025”等国家战略，增强全球竞争力。

• AI for Process Optimization: Machine learning and deep learning for real-time process control, parameter optimization, and quality assurance in manufacturing (e.g., machining, casting).

• Predictive Maintenance and Fault Diagnosis: AI-driven predictive analytics for equipment health monitoring, anomaly detection, and remaining useful life estimation.

• Additive Manufacturing Innovations: AI applications in material selection, defect detection, and topology optimization for 3D printing and advanced geometries.

• Human-Robot Collaboration: AI-enabled collaborative robots (cobots) for safe, efficient human-machine interaction in smart manufacturing environments.

• Sustainability and Digital Twins: AI strategies for reducing waste, optimizing energy use, and implementing digital twins for real-time production system optimization.

📌本期特刊截稿日期：

30 July 2026

📤  欢迎分享给你的研究群组、学生、合作伙伴～

期刊简介

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期刊简介

人工智能正通过数学模型革新制造系统，提升工业4.0背景下的生产效率。从优化复杂算法的机器学习到实现实时适应的AI驱动仿真，该技术精准应对资源分配与流程效率等挑战。

本期特辑旨在汇集数学方法与AI在制造系统中应用的创新研究，通过促进数学家、AI专家与工程师的跨学科合作，展示先进数学建模与优化如何改进生产工艺、解决可持续性等产业挑战，推动智能工厂演进。

本专题将呈现制造系统数学优化的高质量研究，为学界与工业界提供关键参考，支持数学科学与智能制造技术发展。

• Mathematical Optimization in Manufacturing: AI-enhanced mathematical models for process control, parameter optimization, and quality assurance (e.g., linear programming, stochastic processes).

• Predictive Analytics with Mathematical Frameworks: AI-driven mathematical techniques for equipment health monitoring, fault prediction, and lifespan estimation.

• Simulation and Optimization of Production Systems: Mathematical modelling and AI for optimizing manufacturing simulations, including resource allocation and scheduling.

• Algorithmic Design for Automation: Mathematical foundations of AI algorithms supporting automation and human-machine interactions in manufacturing.

• Sustainable Optimization Models: AI-integrated mathematical approaches for minimizing waste, optimizing energy use, and developing digital twin simulations for real-time analysis.

📌本期特刊截稿日期：

30 July 2026

📤  欢迎分享给你的研究群组、学生、合作伙伴～

期刊简介

人工智能正在重塑工程与先进制造领域，构建工业4.0的智能互联生产体系。从优化设计流程的机器学习到实现实时调控的信息物理系统，AI以卓越精度应对效率、安全及可持续性等关键挑战。

本期特刊旨在汇集AI与工程制造融合的前沿研究，通过促进工程师、AI专家与制造专家的跨学科协作，阐释AI如何改进系统设计、优化生产流程并解决规模化与环境影响等产业难题，推动智能制造技术发展。

本专题将为工程师与研究人员提供重要参考，支持“印度自立计划”等国家倡议，增强全球竞争力。

• AI for Engineering Process Optimization: Machine learning and deep learning for real-time control, parameter optimization, and quality assurance in engineering processes (e.g., machining, assembly).

• Predictive Maintenance in Engineering Systems: AI-driven analytics for monitoring equipment health, detecting faults, and estimating the lifespan of industrial machinery.

• AI in Manufacturing Design: Applications of AI in optimizing product designs, defect prevention, and process simulation for advanced manufacturing techniques.

• Human-Machine Integration: AI-enabled systems for safe and efficient collaboration between humans and robots in engineering and manufacturing environments.

• Sustainable Engineering Solutions: AI strategies for reducing resource consumption, optimizing energy efficiency, and implementing digital twins for real-time system analysis.

📌本期特刊截稿日期：

30 July 2026

📤  欢迎分享给你的研究群组、学生、合作伙伴～

期刊简介

人工智能正通过增强建模与仿真技术革新先进制造，推动工业4.0演进。从提升仿真精度的机器学习到实现实时自适应生产的AI集成信息物理系统，该技术精准应对流程效率与系统可靠性等挑战。

本期特刊聚焦AI在制造建模与仿真中的应用，旨在汇集创新研究与深度综述，通过促进AI专家、工程师与仿真专家的跨学科合作，展示AI如何提升仿真精度、优化制造流程、解决可持续性等产业挑战，塑造智能制造未来。

本专题将呈现建模仿真领域高质量研究，为学界与工业界提供关键资源，支持“数字印度”等全球倡议，通过先进仿真技术提升智能制造竞争力。

• AI for Simulation Optimization: Machine learning and deep learning for refining simulation models, parameter tuning, and process control in manufacturing simulations (e.g., finite element analysis, computational fluid dynamics).

• Predictive Modelling and Fault Simulation: AI-driven analytics for predicting system behavior, detecting faults, and simulating equipment lifespan in manufacturing environments.

• Simulation of Additive Manufacturing: AI applications in simulating material deposition, defect prediction, and structural optimization for 3D printing processes.

• Virtual Prototyping with AI: AI-enhanced simulation for designing and testing human-robot collaboration in smart manufacturing systems.

• Sustainable Simulation Strategies: AI approaches for minimizing resource use, optimizing energy efficiency, and integrating digital twins for real-time simulation and process improvement.

📌本期特刊截稿日期：

30 July 2026

📤  欢迎分享给你的研究群组、学生、合作伙伴～

期刊简介

期刊简介

期刊简介

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