Hopper addresses the product quality created by automated production equipment. The Process Parameter Recommender generates situational recommendations for best-possible machine settings under the prevailing production conditions in order to keep scrap and cycle time as low as possible. With the Recipe Benchmark, the machine settings are identified for producing highest possible output under consideration of the general conditions. Hopper significantly increases the rate of good parts produced per hour, for example in elastomer, thermoplastic and thermoset injection molding and for materials that are difficult to process, e.g. post-consumer plastics, recyclates or highly hygroscopic materials.
High scrap rates depending on current raw material
Varying cycle time due to individual machine settings and constant changes
Intransparency of best-fitting SET parameter tuning
Lack of efficiency in 24/7 production
Increase in good parts ratio by significant scrap and cycle time reduction
Data analysis on material, production environment and machine
Precise parameter adjustment recommendations based on all influencing factors on the process
Predicted production rate & predicted scrap ratio
Situational and automated recommendations available on smartwatch, smartphone and browser
Analysis of machine processes on signal-level based on high frequency live data of various sources
Comparison of similar machines on a very detailed machine component level
Deduction of optimization actions for each real machine to reduce cycle times
Constant monitoring of machine sub-process behavior with notification of anomalies for maintenance department
The AI project “DarWIN”, a cooperation between plus10 and the SKZ, was all about learning detailed behavior models of injection molding machines on the basis of high-frequency machine data. Through the transferability of pre-trained machine learning models, individual machines can learn from each other. This means that behavior models of a specific machine do not have to be completely relearned each time, but are only adapted to the machine and the product currently being run in a small adaptation phase. These behavior models suggest optimized process parameters for the next machine cycle in order to produce in the shortest cycle time and without scrab under the currently prevailing conditions.
From left to right: LTR Dr. Thorsten Thümen of Sumitomo (SHI) Demag, Felix Georg Müller (plus10), Christoph Mussauer (SKZ), Marco Fischer and Melanie Rohde of Sumitomo (SHI) Demag; Source: Sumitomo (SHI) Demag
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