Machine learning, particularly deep learning, is transforming industrial quality inspection. Yet, training robust machine learning models typically requires large volumes of high-quality labeled data, which are expensive, time-consuming, and labor-intensive to obtain in manufacturing. Moreover, defective samples are intrinsically rare, leading to severe class imbalance that degrades model performance. These data constraints hinder the widespread adoption of machine learning-based quality inspection methods in real production environments. Synthetic data generation (SDG) offers a promising solution by enabling the creation of large, balanced, and fully annotated datasets in an efficient, cost-effective, and scalable manner. This paper presents a hybrid SDG framework that integrates simulation-based rendering, domain randomization, and real background compositing to enable zero-shot learning for computer vision-based industrial part inspection without manual annotation. The SDG pipeline generates 12,960 labeled images in one hour by varying part geometry, lighting, and surface properties, and then compositing synthetic parts onto real image backgrounds. A two-stage architecture utilizing a YOLOv8n backbone for object detection and MobileNetV3-small for quality classification is trained exclusively on synthetic data and evaluated on 300 real industrial parts. The proposed approach achieves an mAP@0.5 of 0.995 for detection, 96% classification accuracy, and 90.1% balanced accuracy. Comparative evaluation against few-shot real-data baseline approaches demonstrates significant improvement. The proposed SDG-based approach achieves 90-91% balanced accuracy under severe class imbalance, while the baselines reach only 50% accuracy. These results demonstrate that the proposed method enables annotation-free, scalable, and robust quality inspection for real-world manufacturing applications.

Recent advancements in deep learning have significantly improved visual quality inspection and predictive maintenance within industrial settings. However, deploying these technologies on low-resource edge devices poses substantial challenges due to their high computational demands and the inherent complexity of Explainable AI (XAI) methods. This paper addresses these challenges by introducing a novel XAI-integrated Visual Quality Inspection framework that optimizes the deployment of semantic segmentation models on low-resource edge devices. Our framework incorporates XAI and the Large Vision Language Model to deliver human-centered interpretability through visual and textual explanations to end-users. This is crucial for end-user trust and model interpretability. We outline a comprehensive methodology consisting of six fundamental modules: base model fine-tuning, XAI-based explanation generation, evaluation of XAI approaches, XAI-guided data augmentation, development of an edge-compatible model, and the generation of understandable visual and textual explanations. Through XAI-guided data augmentation, the enhanced model incorporating domain expert knowledge with visual and textual explanations is successfully deployed on mobile devices to support end-users in real-world scenarios. Experimental results showcase the effectiveness of the proposed framework, with the mobile model achieving competitive accuracy while significantly reducing model size. This approach paves the way for the broader adoption of reliable and interpretable AI tools in critical industrial applications, where decisions must be both rapid and justifiable.

Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy. In this paper, we propose a FL algorithm for object detection in quality inspection tasks using YOLOv5 as the object detection algorithm and Federated Averaging (FedAvg) as the FL algorithm. We apply this approach to a manufacturing use-case where multiple factories/clients contribute data for training a global object detection model while preserving data privacy on a non-IID dataset. Our experiments demonstrate that our FL approach achieves better generalization performance on the overall clients' test dataset and generates improved bounding boxes around the objects compared to models trained using local clients' datasets. This work showcases the potential of FL for quality inspection tasks in the manufacturing industry and provides valuable insights into the performance and feasibility of utilizing YOLOv5 and FedAvg for federated object detection.

In the end-of-line test of geared motors, the evaluation of product qual-ity is important. Due to time constraints and the high diversity of variants, acous-tic measurements are more economical than vibration measurements. However, the acoustic data is affected by industrial disturbing noise. Therefore, the aim of this study is to investigate the robustness of features used for anomaly detection in geared motor end-of-line testing. A real-world dataset with typical faults and acoustic disturbances is recorded by an acoustic array. This includes industrial noise from the production and systematically produced disturbances, used to compare the robustness. Overall, it is proposed to apply features extracted from a log-envelope spectrum together with psychoacoustic features. The anomaly de-tection is done by using the isolation forest or the more universal bagging random miner. Most disturbances can be circumvented, while the use of a hammer or air pressure often causes problems. In general, these results are important for condi-tion monitoring tasks that are based on acoustic or vibration measurements. Fur-thermore, a real-world problem description is presented to improve common sig-nal processing and machine learning tasks.

In many industries, advocates of artificial intelligence and autonomous technology are quick to promise sweeping transformation and fully autonomous solutions. However, the optimists usually promise more than they can deliver and soon find the engineering challenges are greater than they first realised. In this article, Zohar Kantor, vice president of sales at artificial intelligence start-up Lean.AI, asks whether we celebrated the arrival of autonomous quality inspection too soon. In 2013, Elon Musk said, "it's a bridge too far to go to fully autonomous cars." Although the world of driverless vehicles has moved on significantly since this admission, it was a belated recognition that the Tesla CEO had under-estimated the challenges of operating a vehicle without a human being in the driver's seat.

Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality inspection (VQI) tailored for real-world manufacturing environments. To enable automated VQI on the edge computing within the fully-integrated VQI system, a highly compact deep anti-aliased attention condenser neural network (which we name LightDefectNet) tailored specifically for light guide plate surface defect detection in resource-constrained scenarios was created via machine-driven design exploration with computational and "best-practices" constraints as well as L_1 paired classification discrepancy loss. Experiments show that LightDetectNet achieves a detection accuracy of ~98.2% on the LGPSDD benchmark while having just 770K parameters (~33X and ~6.9X lower than ResNet-50 and EfficientNet-B0, respectively) and ~93M FLOPs (~88X and ~8.4X lower than ResNet-50 and EfficientNet-B0, respectively) and ~8.8X faster inference speed than EfficientNet-B0 on an embedded ARM processor. As such, the proposed deep learning-driven workflow, integrated with the aforementioned LightDefectNet neural network, is highly suited for high-throughput, high-performance light plate surface VQI within real-world manufacturing environments.

Therefore, quality control plays an important role. However, quality control is also a cost factor that can not be neglected [1]. This is especially true for those industries requiring inspections outside the production line, such as disruptive or non-disruptive testing [2]. Traditional sampling strategies are random sampling, where a fixed number of samples is taken randomly in a fixed interval or periodic sampling, where samples are taken at a fixed interval. However, both sampling strategies entail some disadvantages. A small sampling interval increases the chance of detecting defective parts but also increases sampling costs, especially using destructive testing.

While not a magic bullet, artificial intelligence (AI) is changing the game for manufacturers faced with ongoing labor shortages, decreased productivity and quality control, and an unpredictable supply chain exacerbated by growing consumer demands. Increasingly sophisticated AI is already being used in nearly every industry--from automotive and food & beverage to metal fabrication and plastic molding--to power predictive systems, increase robot capabilities, improve the precision of machine vision and help businesses optimize their processes to improve quality and reduce waste. The future is here--and we're just getting started. As AI becomes more powerful, robots and other machines can quickly learn what they need to do to perform given tasks without expensive and difficult-to-find AI experts. When coupled with decreasing hardware costs and clearer use cases demonstrating the benefits, deploying AI is an obvious choice for small and large companies alike.

One of the great benefits of Industry 5.0 is that it provides the means for robots to help humans work better and faster than ever before. The introduction of collaborative robots (or "cobots" for short) dramatically lowered the bar for automating manual processes by making robotic technology much more accessible and easier to implement alongside existing workers and processes. With the return on investment being easier to justify, manufacturers are looking for ways to automate more tasks. The inherent capabilities of cobots allow them to be assigned to multiple applications and be moved around the facility for different jobs at various times of the day. What's more, the cobots themselves are further galvanizing the transition to Industry 5.0.

It's Monday morning, and Paul opens one of several emails sent by his boss, Heather. Her email seems a bit unusual, asking Paul to rush to a nearby store to purchase $200 in gift certificates and send her the codes found in the cards right away. "This is weird," thinks Paul. After some digging, he finds a typo in his boss's email address and realizes that this is not really coming from Heather, a doubt which was confirmed by the company's IT department shortly thereafter. Paul avoided being robbed of $200 by this (today very common) phishing attack by exercising one of the most fundamental faculties of human thought: doubt.