Surface Defect Detection for Automated Inspection Systems using Convolutional Neural Networks

Defect detection in industrial production processes is an important and necessary part of quality control. Many defects can occur during the manufacturing process, causing high manufacturing costs. Thus the inspection of screws, which represent an indispensable element of many mechanical components, is a critical process. To reduce manufacturing costs and increase efficiency, a reliable method for inspection is Deep Learning. It can help simplify the process of quality control and increase the velocity and volume of detected defects in screws. This approach uses a CNN model to classify non-defective and defective screws with different types of defects. Instead of manual quality control methods, which can be easily biased, our CNN approach is accurate, cost-efficient, and fast, with an accuracy of over 97 percent. With this approach corresponding to industrial production processes, different defects in screws and non-defective screws can be classified from images according to a real-world industrial inspection scenario.

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“…Hence, the preprocessing of images for certain applications is not necessary. Also, the transferability of the results is facilitated [31].…”

Section: Quality Inspection Of Screwsmentioning

confidence: 99%

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Breitenbach¹,

Eckert²,

Mahal³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…ANNs have been utilised in manufacturing applications ranging from conventional machine health monitoring [33,83,42] to product quality monitoring in additive manufacturing [63]. The proposed applications of ANNs to machining domain problems include chatter prediction [12,50], fault diagnosis [78], surface defect detection [39] and several others [38].…”

Section: Artificial Neural Network In Manufacturingmentioning

confidence: 99%

Conditional StyleGAN modelling and analysis for a machining digital twin

Зотов¹,

Tiwari²,

Kadirkamanathan³

2021

ICA

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Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model.

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“…Konrad et al [64] The authors applied CNN for defect detection on different surfaces in the images, taken by unmanned aerial vehicles. Experiments were performed with 2500 images.…”

Section: Accuracy Is Not Specifiedmentioning

confidence: 99%

Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

Urbonas¹,

Raudonis²,

Maskeliūnas

et al. 2019

Applied Sciences

114

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In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.

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Surface Defect Detection for Automated Inspection Systems using Convolutional Neural Networks

Cited by 5 publications

References 12 publications

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Conditional StyleGAN modelling and analysis for a machining digital twin

Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

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