Collective classification for the detection of surface defects in automotive castings

Pastor-López, Iker; Santos, Igor; de-la-Peña-Sordo, Jorge; Salazar, Mikel; Santamaría-Ibirika, Aitor; Bringas, Pablo García

doi:10.1109/iciea.2013.6566502

Cited by 2 publications

(1 citation statement)

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“…In [15][16][17], a number of ML algorithms were proposed for the automated inspection of surface quality in castings. In these specific publications, the part images were captured by a laser camera with 3D technology.…”

Section: State Of the Artmentioning

confidence: 99%

Defect Recognition in High-Pressure Die-Casting Parts Using Neural Networks and Transfer Learning

Andriosopoulou

Mastakouris

Masouros

et al. 2023

Metals

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The quality control of discretely manufactured parts typically involves defect recognition activities, which are time-consuming, repetitive tasks that must be performed by highly trained and/or experienced personnel. However, in the context of the fourth industrial revolution, the pertinent goal is to automate such procedures in order to improve their accuracy and consistency, while at the same time enabling their application in near real-time. In this light, the present paper examines the applicability of popular deep neural network types, which are widely employed for object detection tasks, in recognizing surface defects of parts that are produced through a die-casting process. The data used to train the networks belong to two different datasets consisting of images that contain various types of surface defects and for two different types of parts. The first dataset is freely available and concerns pump impellers, while the second dataset has been created during the present study and concerns an automotive part. For the first dataset, Faster R-CNN and YOLOv5 detection networks were employed yielding satisfactory detection of the various surface defects, with mean average precision (mAP) equal to 0.77 and 0.65, respectively. Subsequently, using transfer learning, two additional detection networks of the same type were trained for application on the second dataset, which included considerably fewer images, achieving sufficient detection capabilities. Specifically, Faster R-CNN achieved mAP equal to 0.70, outperforming the corresponding mAP of YOLOv5 that equalled 0.60. At the same time, experiments were carried out on four different computational resources so as to investigate their performance in terms of inference times and consumed power and draw conclusions regarding the feasibility of making predictions in real time. The results show that total inference time varied from 0.82 to 6.61 s per image, depending on the computational resource used, indicating that this methodology can be integrated in a real-life industrial manufacturing system.

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Section: State Of the Artmentioning

confidence: 99%