Professor Denis Dowling
Director, I-Form Advanced Manufacturing Research Centre
Partnerships between academia and industry are helping manufacturers across Ireland become more efficient with the use of new digital tools.
Analysing manufacturing process data offers significant opportunities to boost yields and optimise material and energy efficiencies. Academia and industry are seeing the development of new digital tools and processes offering solutions to manufacturing challenges.
Advanced manufacturing expertise
Playing a pivotal role is I-Form, the Science Foundation Ireland-funded Research Centre for Advanced Manufacturing. Headquartered at University College Dublin with eight partners in research and academic institutions across Ireland, their research focus is on the application of digital technologies to materials processing.
Centre Director Professor Denis Dowling, explains: “We look at manufacturing processes and try to enhance their efficiency, reliability and sustainability. We achieve this by applying a range of digital tools, such as machine learning and artificial intelligence.”
Manufacturing has a real need for expertise
in using digital tools in its operations.
Digital tools in manufacturing
Within the past six years, I-Form has trained over 120 PhDs and post-doctoral researchers, with new skill sets combining the use of digital tools with engineering and physical sciences to support manufacturing. “Manufacturing has a real need for expertise in using digital tools in its operations,” he says, adding that graduates have had little difficulty securing good jobs in the sector.
Another element is enhancing Ireland’s international reputation for scientific excellence, with research attracting investment while giving indigenous companies a competitive advantage. Industry-academic links are two-way, explains Dowling. “Helping companies develop and adopt new technologies and increase efficiency is a core element of what we are doing, as well as addressing product and process development.”
Sustainable economic models
In one example, researchers combined the outputs from machine learning data analysis to develop a novel rules-based Recommender System, which provides operators with feedback directly onto tablet devices or mobile phones.
“The ability to identify processing anomalies at an early stage, rather than after a manufacturing process has completed, clearly enhances processing sustainability and avoids waste,” adds Dowling.
Other digital tools are being developed to accelerate the prediction of optimum processing conditions. One industry partner found that the application of I-Form predictive modelling tools led to a 90% reduction in the number of physical experiments required for fabrication of its new product range.
The Centre’s work also helps companies reduce greenhouse gas emissions and address the growing shortages of critical raw materials of high economic importance in the EU. Through its ‘Industry 5.0, a transformative vision for Europe’ document, the EU Commission has highlighted the role digital technologies will play in enabling more sustainable economic models.