首页|Reports from Robert Bosch GmbH Advance Knowledge in Machine Learning (Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding)
Reports from Robert Bosch GmbH Advance Knowledge in Machine Learning (Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting originating from Renningen, Germa ny, by NewsRx correspondents, research stated, “Machine learning (ML) methods pr esent a valuable opportunity for modeling the non-linear behavior of the injecti on molding process.” The news journalists obtained a quote from the research from Robert Bosch GmbH: “They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the in jection molding process and the challenges associated with collecting process da ta remain significant obstacles for the application of ML methods. To address th is, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidel ity numerical simulations. The hybrid modeling approaches include feature learni ng, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injectio n-molded part are utilized.”
Robert Bosch GmbHRenningenGermanyE uropeBusinessBusinessCyborgsEmerging TechnologiesMachine Learning
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