Artificial intelligence for advanced manufacturing quality

100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

Deep Learning and Image data-based surface cracks recognition of laser nitrided Titanium alloy

Laser nitriding, a high-precision surface modification process, enhances the hardness, wear resistance and corrosion resistance of the materials. However, laser nitriding process is prone to appearance of cracks when the process is performed at high laser energy levels. Traditional techniques to detect the cracks are time consuming, costly and lack standardization. Thus, this research aims to put forth deep learning-based crack recognition for the laser nitriding of Ti–6Al–4V alloy. The process of laser nitriding has been performed by varying duty cycles, and other process parameters. The laser nitrided sample has then been processed through optical 3D surface measurements (Alicona Infinite Focus G5), creating high resolution images. The images were then pre-processed which included 2D conversion, patchification, image augmentation and subsequent removal of anomalies. After preprocessing, the investigation focused on employing robust binary classification method based on CNN models and its variants, including ResNet-50, VGG-19, VGG-16, GoogLeNet (Inception V3), and DenseNet-121, to recognize surface cracks. The performance of these models has been optimized by fine tuning different hyper parameters and it is found that CNN base model along with models having less trainable parameters like VGG-19, VGG-16 exhibit better performance with accuracy of more than 98% to recognize cracks. Through the achieved results, it is found that VGG-19 is the most preferable model for this crack recognition problem to effectively recognize the surface cracks on laser nitrided Ti–6Al–4V material, owing to its best accuracy and lesser parameters compared to complex models like ResNet-50 and Inception-V3

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation

Surface Defect Detection Using YOLO Network

Detecting defects on surfaces such as steel can be a challenging task because defects have complex and unique features. These defects happen in many production lines and differ between each one of these production lines. In order to detect these defects, the You Only Look Once (YOLO) detector which uses a Convolutional Neural Network (CNN), is used and received only minor modifications. YOLO is trained and tested on a dataset containing six kinds of defects to achieve accurate detection and classification. The network can also obtain the coordinates of the detected bounding boxes, giving the size and location of the detected defects. Since manual defect detection is expensive, labor-intensive and inefficient, this paper contributes to the sophistication and improvement of manufacturing processes. This system can be installed on chipsets and deployed to a factory line to greatly improve quality control and be part of smart internet of things (IoT) based factories in the future. YOLO achieves a respectable 70.66% mean average precision (mAP) despite the small dataset and minor modifications to the network