Insights from University of Porto: Exploring the future and sustainability in Manufacturing
We had a fruitful discussion on the Future of Manufacturing & Sustainability with Assistant Professor at University of Porto, Gil Gonçalves.
1. How do you see the integration of artificial intelligence and machine learning impacting manufacturing processes in the coming years, and what research areas hold the most promise for further advancements?
Traditional quality management approaches are being superseded by innovative paradigms like Zero-Defect-Manufacturing (ZDM) and First-Time-Right (FTR) production. These are just examples of how AI and ML are impacting manufacturing processes.
ZDM and FTR represent a departure from conventional quality control methods, aiming not merely to detect and rectify defects but to avoid them entirely.
This entails a comprehensive approach encompassing production planning, quality assurance, and maintenance strategies. Leveraging advanced technologies such as in-line data collection, simulation, Digital Twins, Cyber-Physical Systems (CPS), Internet of Things (IoT), and Artificial Intelligence (AI), ZDM enables real-time monitoring and optimisation of manufacturing processes, thus ensuring unparalleled levels of quality and efficiency.
However, implementing ZDM poses challenges, notably in data aggregation, modelling, and optimisation, particularly during system reconfigurations or product transitions. To address this, ongoing research focuses on expediting the transfer of ZDM strategies across products and refining model generation and parameter tuning methodologies.
Hyper Process Modeling (HPM) emerges as a pivotal tool in pursuing First-Time-Right production. HPM involves the dynamic simulation and modelling of manufacturing processes, incorporating real-time data feeds and predictive analytics to anticipate potential defects and optimise production parameters. By creating virtual replicas of production environments, HPM enables manufacturers to use existing knowledge, explore various scenarios, identify potential bottlenecks, and fine-tune process parameters before physical implementation. This proactive approach not only minimises the risk of defects but also enhances agility and flexibility in adapting to changing production demands.
2. What specific research initiatives openZDM project is taking to promote sustainable manufacturing practices and reduce the environmental footprint of industrial processes?
As Europe moves towards eco-friendly practices, sustainability and worker well-being in manufacturing are priorities. openZDM, by optimising processes for the first time right and with zero defects, will contribute to reducing the environmental impact and environmental footprint of industrial processes.
openZDM employs advanced data analytics and predictive maintenance techniques to optimise manufacturing processes. By minimising defects and errors, it reduces the need for rework and scrap materials, which in turn decreases waste generation. Moreover, avoiding rework and streamlining production workflows helps reduce overall energy consumption, lowering carbon emissions associated with manufacturing operations.
For example, in an automotive assembly plant, openZDM approaches help to identify and correct potential quality problems early in the process before they lead to the production of faulty cars. This reduces material waste and energy usage while maintaining high product quality.
openZDM ensures that manufacturing processes are optimised to produce high-quality products with zero defects. This not only reduces the environmental impact associated with producing and disposing of defective goods but also extends the lifespan of products, contributing to a more sustainable approach to consumption.
For example, in a woodboard manufacturing facility, openZDM monitors the process and enviromental parameters to detect any deviations, predict the impact in the final quality of the product, and adjust the process parameters to meet the highest possible quality standards. Ensuring that process parameters are always adjusted to optimal recipes reduces the likelihood of quality failures and the need for replacements, thereby reducing waste and increasing production efficiency.
Overall, by leveraging openZDM’s capabilities to optimise processes for zero defects, the manufacturing industry can significantly reduce its environmental footprint and move towards more sustainable and eco-friendly practices in line with Europe’s objectives for a greener future.
3. From a research perspective, what collaborative efforts or interdisciplinary approaches do you think are essential to foster innovation and address the complex challenges faced by the manufacturing industry in the coming years?
European manufacturers can lead the way to a greener future and at the same time stay competitive by embracing innovation and digitalisation. Transitioning to digital and greener technologies brings grand challenges, requiring careful planning, testing, and experimentation to ensure smooth integration and optimal outcomes.
Whether integrating sustainable energy solutions, reducing resource utilization, or advancing manufacturing practices, partnerships between industry and academia bring together diverse expertise and resources to tackle these complex problems holistically.
Joint research efforts between industry and academia drive advancements in smart manufacturing technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins. Academia contributes expertise in data analytics, machine learning, and cyber-physical systems, while industry provides insights into operational challenges and needs. Together, they develop innovative solutions to optimise production efficiency, quality, and sustainability.
Overall, partnerships between industry and academia, exemplified by initiatives like OpenZDM, enable the joint pursuit of ambitious research agendas to address the grand challenges faced by the manufacturing industry in transitioning to digital and greener technologies. By leveraging diverse expertise and resources, these collaborations drive innovation, facilitate technology transfer, and ultimately contribute to a greener and more competitive future for European manufacturers.
Short Biography
Gil Manuel Magalhães de Andrade Gonçalves (publishes as Gil M. Gonçalves) is Assistant Professor at the Faculty of Engineering of the University of Porto (FEUP) in the Department of Informatics Engineering (DEI). Gil has been developing his scientific and teaching career in software and systems engineering, control architectures, software modelling, and design for complex systems, with main research interests in: digitalization, digital transformation and industry 4.0; information system architectures and models for complex systems; Cyber Physical Systems; IoT and edge computing; Digital Twins; predictive and prescriptive models for system adaptation and reconfiguration.
