Abstract

AbstractInjection‐molded rubber parts are widely used in automotive, aeronautical, and industrial engineering applications Therefore, such rubber parts are often critical to the safe operation of the entire system, and part failure can result in significant human or environmental damage. To avoid shipping any parts of subpar quality, manufacturers need to continuously monitor the quality of their product. xIn this work, we apply a principal component analysis (PCA) based process monitoring method. This method is able to detect process fluctuations (faults) in real‐time solely from sensor data features, only requiring pretraining on data from about 10 in‐control cycles. Specific faults were set to critically affect the dynamic performance of the manufactured NBR rubber parts. Fisher discriminant analysis (FDA) was employed to automatically cluster individual molding cycles into those of being in control, those of defectives caused by unfavorable raw material storage and those of out‐of‐tolerance induced by an overheated mold, again solely from sensor data. Both PCA fault detection and FDA fault identification decisions were validated by oscillatory rheology and dynamic compression testing of the manufactured parts. This combined method approach is scalable, transferable, and can be implemented on standard industrial injection molding equipment.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.