AIoT for sustainable manufacturing: Overview, challenges, and opportunities

The integration of IoT and AI has gained significant attention as an emerging means to digitize manufacturing industries and drive sustainability in the context of Industry 4.0. In recent times, there has been a merging of AI and IoT technologies to form an “Artificial Intelligence of Things” (AIoT) infrastructure. This integration aims to enhance various aspects such as human–machine interactions, operations in the field of IoT, big data analytics, and more. AIoT-based solutions offer numerous benefits to the manufacturing industry. These solutions improve efficiency, reduce waste, and enhance safety measures. By utilizing AIoT, manufacturers are able to achieve Industry 4.0 goals and increase productivity through automation, process optimization, and more informed decision-making. Additionally, the adoption of AI and IoT-based solutions in manufacturing companies has increased substantially. These solutions enable the early detection and prevention of defects in equipment, leading to the production of high-quality products. By minimizing waste, reducing costs, improving efficiency, and boosting productivity, manufacturers can further optimize their operations. Academic researchers and industry practitioners are currently prioritizing the development of highly advanced and streamlined AIoT-based solutions specifically designed for sustainable manufacturing. The primary objectives of this paper are (i) to provide a comprehensive overview of the domain-centric AIoT-based industry technology for sustainable manufacturing; (ii) to conduct a thorough survey of the existing research on AIoT-enabled manufacturing; (iii) to discuss the current challenges faced by AIoT in the context of sustainable manufacturing and explore the research prospects in this field. Therefore, this paper presents a systematic review of state-of-the-art AIoT-based techniques employed in industries for sustainable manufacturing and analyzes the key contributions and opportunities. Finally, the key challenges are explained for future research prospects. Copyright © 2023 Elsevier B.V. All rights reserved.