A Lightweight Deep Network for Defect Detection of Insert Molding Based on X-ray Imaging

Aiming at the abnormality detection of industrial insert molding processes, a lightweight but effective deep network is developed based on X-ray images in this study. The captured digital radiography (DR) images are firstly fast guide filtered, and then a multi-task detection dataset is constructed using an overlap slice in order to improve the detection of tiny targets. The proposed network is extended from the one-stage target detection method of yolov5 to be applicable to DR defect detection. We adopt the embedded Ghost module to replace the standard convolution to further lighten the model for industrial implementation, and use the transformer module for spatial multi-headed attentional feature extraction to perform improvement on the network for the DR image defect detection. The performance of the proposed method is evaluated by consistent experiments with peer networks, including the classical two-stage method and the newest yolo series. Our method achieves a mAP of 93.6%, which exceeds the second best by 3%, with robustness sufficient to cope with luminance variations and blurred noise, and is more lightweight. We further conducted ablation experiments based on the proposed method to validate the 32% model size reduction owing to the Ghost module and the detection performance enhancing effect of other key modules. Finally, the usability of the proposed method is discussed, including an analysis of the common causes of the missed shots and suggestions for modification. Our proposed method contributes a good reference solution for the inspection of the insert molding process.

Method for Producing Three-Dimensional Designs in Film Insert Molding

Plastic is an important material in product design because it easily transforms, is very light, and has multifarious structural characteristics. Nevertheless, it gives the customer the impression of a cheap product. Therefore, decorative techniques are often applied to plastic products. Decorative techniques can improve the appearance of a plastic product while making use of its beneficial characteristics. At present, film insert molding method is predominantly used in decorating plastic products. In general, a designer creates a three-dimensional design but the film insert molding method uses a two-dimensional pattern. Thus, it is difficult to reproduce a three-dimensional design with a two-dimensional film; no study has been conducted on the reproduction of three-dimensional design using film insert molding method to-date. The objective of this study is to develop a design generation method to reproduce a three-dimensional design with a two-dimensional film pattern. The effectiveness of the proposed method is experimentally validated.