Predicting part quality early during an injection molding cycle
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Lucas Bogedale
,
Stephan Doerfel
Abstract
Data-based process monitoring in injection molding plays an important role in compensating disturbances in the process and the associated impairment of part quality. Selecting appropriate features for a successful online quality prediction based on machine learning methods is crucial. Time series such as the injection pressure and injection flow curve are particularly suitable for this purpose. Predicting quality as early as possible during a cycle has many advantages. In this paper it is shown how the recording length of the time series affects the prediction performance when using machine learning algorithms. For this purpose, two successful molding quality prediction algorithms (k Nearest Neighbors and Ridge Regression) are trained with time series of different lengths on extensive data sets. Their prediction performances for part weight and a geometric dimension are evaluated. The evaluations show that recording time series until the end of a cycle is not necessary to obtain good prediction results. These findings indicate that early reliable quality prediction is possible within a cycle, which speeds up prediction, allows timely part handling at the end of the cycle and provides the basis for automated corrective interventions within the same cycle.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: Parts of this project (HA project no. 864/20–21) were financed with funds of LOEWE – Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence).
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Data availability: The used datasets are publicly available. https://github.com/sc4t1m/scatimdata (Referenced in the text).
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Investigation of the effects of water uptake on the mechanical properties of wood dust particle filled Prosopis Juliflora reinforced phenol formaldehyde hybrid polymer composites
- Experimental investigation on mechanical and tribological analysis of pineapple leaf (Ananas comosus) and sisal (Agave sisalana) fibers reinforced hybrid epoxy composites
- An experimental study of weave pattern effect on the mechanical and dynamic behavior of composite laminates
- Structuring step dependent characteristics in joining using pin-like structures in the vibration welding process
- Fabrication of expandable graphite and soybean oil-based synergistic modified polyurethane foam with improved thermal stability and flame retardant properties
- Fabrication of electrospun nanofiber from a blend of PVC and PHB
- Investigation of mechanical and tribological performance of wood dust reinforced epoxy composite under dry, wet and heated contact condition
- Multi-layer co-extrusion blow molding
- Predicting part quality early during an injection molding cycle
- Optimizing laser-based micro-cutting for PMMA microfluidic device fabrication: thermal analysis and parameter optimization
- Preparation of PVDF/PVA composite films with micropatterned structures on light-cured 3D printed molds for hydrophilic modification of PVDF
- Evaluation of thermal contact resistance of molten resin–mold interface during high-thermal-conductivity polyphenylene sulfide filling in injection molding
- Effect of sinusoidal pulsating speed enhancement on the mixing performance of plastics machinery
- Experimental investigation on the mechanical and wear behavior of epoxy/Indian almond/peepal hybrid composites
- Exploration of the thermal and mechanical characteristics of polymethyl methacrylate-based copolymers: implications for wind turbine blades applications
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Investigation of the effects of water uptake on the mechanical properties of wood dust particle filled Prosopis Juliflora reinforced phenol formaldehyde hybrid polymer composites
- Experimental investigation on mechanical and tribological analysis of pineapple leaf (Ananas comosus) and sisal (Agave sisalana) fibers reinforced hybrid epoxy composites
- An experimental study of weave pattern effect on the mechanical and dynamic behavior of composite laminates
- Structuring step dependent characteristics in joining using pin-like structures in the vibration welding process
- Fabrication of expandable graphite and soybean oil-based synergistic modified polyurethane foam with improved thermal stability and flame retardant properties
- Fabrication of electrospun nanofiber from a blend of PVC and PHB
- Investigation of mechanical and tribological performance of wood dust reinforced epoxy composite under dry, wet and heated contact condition
- Multi-layer co-extrusion blow molding
- Predicting part quality early during an injection molding cycle
- Optimizing laser-based micro-cutting for PMMA microfluidic device fabrication: thermal analysis and parameter optimization
- Preparation of PVDF/PVA composite films with micropatterned structures on light-cured 3D printed molds for hydrophilic modification of PVDF
- Evaluation of thermal contact resistance of molten resin–mold interface during high-thermal-conductivity polyphenylene sulfide filling in injection molding
- Effect of sinusoidal pulsating speed enhancement on the mixing performance of plastics machinery
- Experimental investigation on the mechanical and wear behavior of epoxy/Indian almond/peepal hybrid composites
- Exploration of the thermal and mechanical characteristics of polymethyl methacrylate-based copolymers: implications for wind turbine blades applications
