Prediction and control of injection molded part weight using machine learning – A literature review
Jonas Krauß, Ilona Borchardt
Abstract
Injection molding is an important and widely used process for the production of thermoplastic parts. Internal and external perturbations, such as fluctuating material properties due to the increasing use of recyclates, make it necessary to rapidly and continuously adapt the process to changing conditions. To ensure consistently high product quality and to support machine operators, machine learning methods are increasingly used for process monitoring and control. This study provides an overview of current approaches in this field, focusing on part weight as a decisive quality criterion.
Topics & Concepts
Process (computing)Quality (philosophy)Field (mathematics)Injection molding machineMolding (decorative)Manufacturing engineeringControl (management)Product (mathematics)Computer scienceProcess controlProcess engineeringEngineeringMechanical engineeringControl engineeringArtificial intelligenceMaterials scienceMathematicsComposite materialGeometryPure mathematicsPhilosophyEpistemologyMoldOperating systemInjection Molding Process and PropertiesManufacturing Process and OptimizationAdditive Manufacturing and 3D Printing Technologies