Abstract
Images semantic segmentation on moulds is presented to achieve automatic and continuous defect detection during injection moulding (IM) processing. Generally, a vital prerequisite of training a robust segmentation neural network is to build a suitable dataset, which requires a large amount of image data with plentiful mould colours, different illumination conditions and specific defect labels. The approach requires long data preparation to collect a sufficient dataset, which could meet the needs of online defect detection during injection moulding. To address robust dataset development, the present work uses 3D CAD modelling software to fabricate defects and colours on moulds, creating an augmented dataset mixed with virtual data and real-process data. In this investigation, moulds printed by vat photopolymerization is used as object for which both mould pictures during injection moulding process and virtual mould pictures from 3D modelling software are collected. Mixed datasets with different proportion of virtual images are labelled and fed into the segmentation neural network. The experimental work shows that the proposed method provides a reliable augmentation on the dataset for subsequent IM semantic segmentation framework.
Original language | English |
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Title of host publication | European Society for Precision Engineering and Nanotechnology, Conference Proceedings : 22nd International Conference and Exhibition, EUSPEN 2022 |
Editors | R.K. Leach , A. Akrofi-Ayesu , C. Nisbet, D. Phillips |
Publisher | euspen |
Publication date | 2022 |
Pages | 175-176 |
ISBN (Electronic) | 978-199899911-8 |
Publication status | Published - 2022 |
Event | 22nd International Conference of the European Society for Precision Engineering and Nanotechnology (euspen 22) - Geneva, Switzerland Duration: 30 May 2022 → 3 Jun 2022 |
Conference
Conference | 22nd International Conference of the European Society for Precision Engineering and Nanotechnology (euspen 22) |
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Country/Territory | Switzerland |
City | Geneva |
Period | 30/05/2022 → 03/06/2022 |
Keywords
- Injection moulding
- Data augmentation
- Semantic segmentation
- Defect detection
- Soft tooling