Quality prediction and root cause analysis system and method for injection molding process
Abstract
A quality prediction and root cause analysis system for an injection molding process comprises a front end, a database, a back end and a machine learning module. The machine learning module is configured to train one or more machine learning models based on the process parameter data and the quality feature data stored in the database and one or more algorithms. Wherein the machine learning model comprises a prediction model adapted to predict quality characteristics of a molded product produced by injection molding based on the process parameter data and the quality characteristic data and a prediction algorithm, and a root cause analysis model cooperating with the prediction model, the processor is coupled to the molded product and adapted to analyze one or more deviations of the molded product based on quality characteristic data obtained from the molded product and a root cause analysis algorithm, and determine one or more possible causes of the deviations if the deviations exceed a predetermined tolerance threshold. The invention further discloses a quality prediction and root cause analysis method for the injection molding process.
Inventors 12
- Benny Drescher Aachen author as filed: Drescher, Benny
- Muller, Tobias
- Maslenkova, Evelina
- Claasen, Nils
- Kramer, Simon
- Daniel Buschmann Aachen author as filed: Buschmann, Daniel
- Junjie Liang Aachen author as filed: Liang, Junjie
- Chen, Jiayun
- Yu, Hanxin
- Liu, Guanhong
- W. Song Aachen author as filed: Song, Weilin
- Chang Liu Aachen author as filed: Liu, Chang
Inventors are linked to an Aachen author when surname and first name match exactly one author.
Applicants
- 香港工业人工智能及机械人研发中心(FLAIR)
- 亚琛工业大学机床与生产工程实验室
- Hong Kong Industrial Artificial Intelligence And Mech Services Flair
- Rheinisch Westfu Lische Maschf Aachen RWTH Aachen University
- Kechuang Center Co Ltd
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