Trend - Hot topic at glasstec 2024 : Artificial Intelligence
Machine learning as a subsegment of Artificial Intelligence focuses on the development of algorithms , which can learn from data without having been explicitly programmed for it . They identify patterns of , and relations between data making forecasts or taking decisions based on this . Deep Learning , in turn , is a method of machine learning that uses multi-layered , deep neuronal networks . These networks are highly performant in data processing as they detect complex structures in vast data volumes and are capable of extracting abstract features from this . This is precisely the “ high potential that the flat glass industry can still tap into ,” reports Peter Seidl , Head of Product Management at machinery producer Grenzebach . This technology innovator serves the flat glass industry with tailor-made automation solutions for the production and processing of industrial float glass . Seidl : “ For many of the processes running in those highly automated factories experience shows nothing is more important than having an experienced operator in all important positions from the melting unit through to monitoring all processes in the IT centres . Here , the industry and modern factories are already very advanced and the efficiency of glass production and the resulting glass quality using conventional means have almost reached the limits of optimisation . The use of Deep Learning AI still offers plenty of potential especially for defect detection – and assessment .” Those looking at the progress made by the flat glass industry will be surprised : “ While some decades ago the cameras used in production were only able to tell good from bad glass sheets , today ’ s systems are so highly performant that a multitude of data is made available for defect analysis permanently ”. Seidl explains : “ Thanks to image analysis and information gathered by sensors Deep Learning models can identify the smallest defects , complete defect patterns or irregularities in the manufacturing process quickly and precisely , recognise patterns in data and infer potential root causes from these . AI can then translate these assessments into recommended actions for the respective operator to optimise production “ on the fly ”. AI cannot replace experienced members of staff but it can provide and support them with increasingly better information .” Likewise , the complexity of the complete glass journey from the hot to the cold end of production is an ideal field of application for AI : batch , glass melt , heating , cooling , sizing – a complex interplay of many line segments and hundreds of parameters and states which often mutually influence each other . This is difficult to understand and manage even for the most experienced operators . Malfunctions occur abruptly or already
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