Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.

Purpose: Ultrasound (US) and Shear Wave Elastography (SWE) imaging are non-invasive methods used for breast lesion characterization. While US and SWE images provide both morphological information, SWE visualizes in addition the elasticity of tissue. In this study a Discriminative Convolutional Neural Network (DCNN) model is applied to US and SWE images and their combination to classify the breast lesions into malignant or benign cases. Furthermore, it is identified whether analysing only the region of the elastogram or including the surrounding B-mode image gives a superior performance.Methods: The dataset used in this study consists of 746 images obtained from 207 patients comprising 486 malignant and 260 benign breast lesions. From each image the US and SWE image was extracted, once including only the region of the elastogram and once including also the surrounding B-mode image. These four datasets were applied individually to a DCNN to determine their predictive capability. Each the best US and SWE dataset were used to examine different combination methods with DCNN. The results were compared to the manual assessment by an expert radiologist.Results: The combination of US and SWE images with the surrounding B-mode image using two ensembled DCNN models achieved best results with an accuracy of 93.53 %, sensitivity of 94.42 %, specificity of 90.75 % and area under the curve (AUC) of 96.55 %.Conclusion: This study showed that using the whole US and SWE images through DCNN was superior to methods, in which only the region of elastogram was used. Combining breast cancer US and SWE images with two ensembled DCNN models in parallel improved the results. The accuracy, sensitivity and AUC of the best combination method were significantly superior to the results of using a single dataset through DCNN and to the results of the expert radiologist.

Dynamic Fault Trees (DFTs) augment Static Fault Trees (SFTs) with temporal gates such as Priority AND (PAND) and Sequential Enforcing (SEQ) to capture order-dependent failure logic. Existing data-driven DFT learning methods, such as Dynamic LIFT (DLIFT), rely on Temporal Truth Tables (TTTs), which record only first-event order and cannot represent event duration or co-activity. This paper introduces the Interval-based Temporal Event Table (ITET), which stores event intervals, and Interval-based DLIFT (IDLIFT), which infers OR, AND, PAND, and SEQ gates from interval overlap relations using a statistical association test and a non-greedy search over all candidate subsets. On a benchmark of 18 reference DFT structures, IDLIFT reaches a mean gate recall of $0.58$ across nine parameter configurations and $0.70$ at the strictest Confidence Level (CL), versus $0.36$ and $0.47$ for DLIFT. On the NASA Milling Wear dataset, the learned structures differ at one gate, judged plausible for IDLIFT and inconsistent for DLIFT by a domain expert. On held-out runs, the IDLIFT structure predicts tool wear with accuracies of $0.79$ at $\mathrm{CL}=0.90$ and $0.78$ at $\mathrm{CL}=0.80$, versus $0.76$ and $0.52$ for DLIFT and $0.55$ for a majority baseline. These results show that interval-based IDLIFT improves DFT structure learning.