With smart sensors and embedded drivers, today’s automotive industry has taken a giant leap in utilising emerging technologies like machine learning, artificial intelligence, and the Internet of Things. Using digital systems can improve efficiencies in manufacturing industries, by reducing waste and energy from excess use of equipment. Automating processes with smart systems can also reduce costs for businesses and consumers, leading to a better deal for all parts of the supply chain.
But as technology rapidly develops, so too must the processes needed to embed new systems and enable them to be used by industry. Researchers from the University of 91Ö±²¥â€™s Energy Institute and the Advanced Resource Efficiency Centre (AREC) have published new findings from a pioneering study to explore the integration of manufacturing processes with smart systems at process and module levels.
The paper, written by Professor Lenny Koh in collaboration with partners from international universities and published in the International Journal of Production Research, proposes a novel design framework which uses Federated learning-Artificial intelligence (FAI) for decision-making and Smart Contract (SC) policies for process execution and control, in order to show the possibility of a completely automated smart automobile manufacturing industry.
The proposed design introduces a novel element called a Trust Threshold Limit (TTL), which would help moderate the excess usage of equipment, tools, energy, and cost functions, overall limiting wastages in the manufacturing processes, making them more sustainable and efficient.
The study is a pioneering one for the automotive manufacturing industry in multiple aspects. First, it considers how the smart contract is involved in the control, execution, and legalisation of manufacturing and distribution of spare parts and components required for the automobile manufacturing process.
Secondly, the study deliberates the effectiveness of using machine learning for computing suitable TTL values for each tool, method, and component in manufacturing environments. Finally, the study developed an AI-enabled Automobile Assembly Model (AAM) that stresses the need for and importance of IoT and machine learning-based, data-driven decision-making. Critical elements like energy, cost function, time, and productivity are remarkably improved using AAM as a reference framework in the automotive manufacturing industry.
Overall, the study provides evidence that the enhanced data collection, processing, and control procedures offered by AI-driven technology help in efficiently handling the data generated for the manufacturing procedures, and enable more sustainable and economically-sound outcomes for industry.
The experimental study also offers a roadmap for implementing a wide range of smart technologies for vehicle operations, control, and assembly performance valuation using data-driven modelling and analysis.
To read the full study, click here: .