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Manufacturing automation systems refer to the integration of emerging technologies such as artificial intelligence, digital twin, blockchain, and the internet of things within the manufacturing systems engineering environment to streamline and optimise production processes. Manufacturing automation systems attempt to achieve enhanced human-machine interactions with a view to improving efficiency, productivity and the effectiveness of automated technologies within the manufacturing environment. By implementing effective automation systems, manufacturers can achieve higher productivity and quality while reducing operational costs. Additionally, these systems enable real-time monitoring and data analytics, allowing robust data-driven decision-making and ultimately accelerating agile and competitive manufacturing operations. Despite all the positive impacts that automation systems can bring, the industry yet needs these advancements to provide value for the business, contribute to economic growth, and assist the industry towards sustainable manufacturing.

Generative AI in manufacturing automation systems holds significant implications for revolutionising manufacturing in terms of innovative systems engineering, adaptive autonomous robotic systems integration, leading to robust productivity, agile supply management, and energy efficiency. Generative AI promotes innovative design solutions for manufacturing systems that not only optimise manufacturing performance but also incorporate creative elements.

Generative AI allows flexible and adaptive automation system integration within product development, advancing efficient manufacturing. AI-driven quality control systems ensure streamlined production safety and product quality. Generative AI enables effective optimisation of material and energy usage and contributes to more sustainable manufacturing practices, reducing waste and environmental impact. This PhD aims to build a generative AI model and algorithm within the automation system integration for optimising manufacturing systems engineering design and processes.

It is expected that the researcher works closely with the academic team to develop a novel generative AI-based model and algorithm to research, analyse and optimise the efficiency and effectiveness of manufacturing automation system integration. The research findings and outcomes will be used to evaluate the feasibility of generative AI applications in manufacturing automation systems and their impact on the efficiency, effectiveness, and sustainability of the next generation of manufacturing systems. The research outcome is expected to be used to develop the automation technology transformation framework for a full-scale generative AI integration in manufacturing automation systems. The outcome of this research will also influence the research strategies and technology roadmaps, including the technology lifecycle and reliability management framework.

At ÂãÁÄÖ±²¥, you will be based within the Manufacturing and Material theme at the Centre for Digital Engineering and Manufacturing (CDEM). The Centre hosts cutting-edge simulation and visualisation facilities. The student will join a diverse and inclusive team at ÂãÁÄÖ±²¥ that focuses on the development and integration of advanced digital technologies in manufacturing.