6,576 research outputs found
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches
Additive manufacturing (AM) is gaining attention across various industries
like healthcare, aerospace, and automotive. However, identifying defects early
in the AM process can reduce production costs and improve productivity - a key
challenge. This study explored the effectiveness of machine learning (ML)
approaches, specifically transfer learning (TL) models, for defect detection in
3D-printed cylinders. Images of cylinders were analyzed using models including
VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2.
Performance was compared across two datasets using accuracy, precision, recall,
and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and
MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest
performance, with an average F1-score of 0.32. Similarly, in the second study,
MobileNetV2 correctly classified all instances, while ResNet50 struggled with
more false positives and fewer true positives, resulting in an F1-score of
0.75. Overall, the findings suggest certain TL models like MobileNetV2 can
deliver high accuracy for AM defect classification, although performance varies
across algorithms. The results provide insights into model optimization and
integration needs for reliable automated defect analysis during 3D printing. By
identifying the top-performing TL techniques, this study aims to enhance AM
product quality through robust image-based monitoring and inspection
An Extended Review on Fabric Defects and Its Detection Techniques
In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection
Defect detection using weakly supervised learning
In many real-world scenarios, obtaining large amounts of labeled data can be
a daunting task. Weakly supervised learning techniques have gained significant
attention in recent years as an alternative to traditional supervised learning,
as they enable training models using only a limited amount of labeled data. In
this paper, the performance of a weakly supervised classifier to its fully
supervised counterpart is compared on the task of defect detection. Experiments
are conducted on a dataset of images containing defects, and evaluate the two
classifiers based on their accuracy, precision, and recall. Our results show
that the weakly supervised classifier achieves comparable performance to the
supervised classifier, while requiring significantly less labeled data
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
Data-hunger and data-imbalance are two major pitfalls in many deep learning
approaches. For example, on highly optimized production lines, defective
samples are hardly acquired while non-defective samples come almost for free.
The defects however often seem to resemble each other, e.g., scratches on
different products may only differ in a few characteristics. In this work, we
introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent
defect types independent of and across various background products and yet can
apply defect-specific styles to generate realistic defective images. An
empirical study on the MVTec AD and two additional datasets showcase DT-GAN
outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and
diversity in defect generation. We further demonstrate benefits for a critical
downstream task in manufacturing -- defect classification. Results show that
the augmented data from DT-GAN provides consistent gains even in the few
samples regime and reduces the error rate up to 51% compared to both
traditional and advanced data augmentation methods.Comment: Accepted by BMVC 202
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