An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning

Saiz, Fátima A.; Alfaro, Garazi; Barandiarán, Íñigo

doi:10.3390/info12120489

Cited by 18 publications

(6 citation statements)

References 29 publications

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“…To capture the genuine environment in defect modelling, it is important to include imaging angle, brightness, and scene. For instance, Saiz F A et al [34] simulated the potential geometric and photometric changes on the CAD model of the pipe defects to produce a more comprehensive defect image. Boikov A et al [35] simulated a camera shot of steel through the Blender 3D graphics editor and controlled the texture changes manually by entering parameters.…”

Section: Defect Image Generation Based On Computer-aided Designmentioning

confidence: 99%

An Overview of Image Generation of Industrial Surface Defects

Zhong,

Zhu,

Liu

et al. 2023

Sensors

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Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.

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Section: Defect Image Generation Based On Computer-aided Designmentioning

confidence: 99%

An Overview of Image Generation of Industrial Surface Defects

Zhong,

Zhu,

Liu

et al. 2023

Sensors

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“…In the first case, simulation software use 3D models of the object to represent generating random poses and simulating defects. [1][2][3] Defects are simulated through 3D modeling 2 or the application of textures. 1 Domain-adaptation technique are also applied to compensate the domain-gap between real and synthetic data.…”

Section: Generation Of Synthetic Data For Optical Quality Controlmentioning

confidence: 99%

Synthetic data generation for improvement of machine learning-based optical quality control: a practical approach in the welding context

Mitri¹,

Frommknecht²,

Huber³

et al. 2023

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

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Weld quality inspection allows the detection of defects that may compromise the quality and strength of the weld. Although visual optical inspection offers lower reliability than other non-destructive methods, it enables weld analysis at a significantly lower cost. In this context, developing machine learning-based algorithms for automatic optical weld quality recognition requires acquiring large amounts of data for training. This entails high costs in terms of time, material and energy required for test preparation. However, one possible approach to tackling the problem with limited datasets is to use synthetic data. Using such data increases the amount and variety of data available to the detection algorithm. With a focus on the context of welding, this paper presents an approach that uses synthetic data as a form of data augmentation to improve the performance of the optical detection of weld seams. Specifically, we propose a generative neural network for semantic image synthesis using a limited starting dataset. The network generates new data instances by receiving as input a semantic map of the image to be represented. Weld defects such as porosity or weld spatter are added to the semantic map so that the network synthesizes corresponding defect images. Analysing the performance on a segmentation network, experimental results show how adding synthetic data to the original data can ensure improvements in network performance.

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“…Furthermore, it is also possible to target the actual manufacturing process, for example, welding [102], injection molding [66], or assembly of manufactured components [37]. Additionally, automated visual inspection applies to remanufacturing products at the end of their useful life [91].…”

Section: Artificial Intelligence-enabled Visual Inspectionmentioning

confidence: 99%

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Rožanec,

Montini,

Cutrona

et al. 2023

Artificial Intelligence in Manufacturing

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Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human-digital twins, and cybersecurity.

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An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning

Cited by 18 publications

References 29 publications

An Overview of Image Generation of Industrial Surface Defects

An Overview of Image Generation of Industrial Surface Defects

Synthetic data generation for improvement of machine learning-based optical quality control: a practical approach in the welding context

Human in the AI Loop via xAI and Active Learning for Visual Inspection

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