In the product life cycle, sustainability, environmental efficiency, and circular economy are critical and demand continuous improvement. Moreover, the ongoing pandemic has also compelled businesses to rethink their life cycle engineering strategies for recovery and adaptation to the "New Normal". Thus, businesses are attempting to adjust their facilities better to position themselves in the face of these new problems, the most pressing of which is determining how to build competitive advantages in the Industry 4.0 era. Herein, artificial intelligence, machine learning, modern computing technologies, automation, robotics, internet of things, and additive manufacturing can all help to improve the product life cycle by combining people, processes, and machines. In addition, researchers and industry specialists worldwide have been working on progressing life cycle engineering using various algorithms, techniques, data models, improved materials, and other methods. For instance, composite materials, such as carbon fiber reinforced epoxies and bio-composites, can reduce a product's through-life environmental impact.
Nevertheless, there are gaps between life cycle engineering and evolving Industry 4.0 technologies. Consequently, a greater emphasis on life cycle engineering-digitization, innovation, resilience, and sustainability is essential. With this, more value can be created across the product life cycle, from design and resource planning to environmentally friendly production, unrestricted operational availability, and complete recycling or reusability.
This research topic intends to bring together research articles highlighting Industry 4.0 motivating advancements in Life Cycle Engineering (LCE). Providing a perfect opportunity to research a specific issue, examine previously unexplored aspects, propose and develop new ideas, share insights, and inspire new research paths. Original research papers, review papers, industrial case studies, and short communications are welcome to submit on themes such as, but not limited to:
• Artificial intelligence (incl. machine learning, deep learning, collaborative learning) for LCE
• Modern computing technologies in LCE viz. quantum computing, cloud computing, etc.
• Resilience and sustainability-oriented engineering methodology
• Digitally optimizing the product life cycle and minimizing pollution and waste
• Framework for plug and play digital entities and their evolution in Industry 4.0
• Maintenance; prognostics and health management; reliability engineering and system safety
• Engineering modelling and simulation
• Solutions and standards for the digital transformation, internet-of-things, and smart factories
• Manufacturing systems for Industry 4.0
• Advancement in operations management viz. decentralized decision making; circular economy; business models
• Automation, robotics, and additive manufacturing for LCE
• Transition of the energy system; sustainable materials
• Life cycle design, design for environment/sustainability
• Applications and software (incl. open-source, platforms, etc.)
• Industrial implementation case studies
In the product life cycle, sustainability, environmental efficiency, and circular economy are critical and demand continuous improvement. Moreover, the ongoing pandemic has also compelled businesses to rethink their life cycle engineering strategies for recovery and adaptation to the "New Normal". Thus, businesses are attempting to adjust their facilities better to position themselves in the face of these new problems, the most pressing of which is determining how to build competitive advantages in the Industry 4.0 era. Herein, artificial intelligence, machine learning, modern computing technologies, automation, robotics, internet of things, and additive manufacturing can all help to improve the product life cycle by combining people, processes, and machines. In addition, researchers and industry specialists worldwide have been working on progressing life cycle engineering using various algorithms, techniques, data models, improved materials, and other methods. For instance, composite materials, such as carbon fiber reinforced epoxies and bio-composites, can reduce a product's through-life environmental impact.
Nevertheless, there are gaps between life cycle engineering and evolving Industry 4.0 technologies. Consequently, a greater emphasis on life cycle engineering-digitization, innovation, resilience, and sustainability is essential. With this, more value can be created across the product life cycle, from design and resource planning to environmentally friendly production, unrestricted operational availability, and complete recycling or reusability.
This research topic intends to bring together research articles highlighting Industry 4.0 motivating advancements in Life Cycle Engineering (LCE). Providing a perfect opportunity to research a specific issue, examine previously unexplored aspects, propose and develop new ideas, share insights, and inspire new research paths. Original research papers, review papers, industrial case studies, and short communications are welcome to submit on themes such as, but not limited to:
• Artificial intelligence (incl. machine learning, deep learning, collaborative learning) for LCE
• Modern computing technologies in LCE viz. quantum computing, cloud computing, etc.
• Resilience and sustainability-oriented engineering methodology
• Digitally optimizing the product life cycle and minimizing pollution and waste
• Framework for plug and play digital entities and their evolution in Industry 4.0
• Maintenance; prognostics and health management; reliability engineering and system safety
• Engineering modelling and simulation
• Solutions and standards for the digital transformation, internet-of-things, and smart factories
• Manufacturing systems for Industry 4.0
• Advancement in operations management viz. decentralized decision making; circular economy; business models
• Automation, robotics, and additive manufacturing for LCE
• Transition of the energy system; sustainable materials
• Life cycle design, design for environment/sustainability
• Applications and software (incl. open-source, platforms, etc.)
• Industrial implementation case studies
