We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud three-dimensional structure database of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature (TcAD) for 4533 nonmagnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest TcAD value, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality data set for the most promising 250 dynamically stable structures, for which we calculate spectral functions, superconducting band gaps, and isotropic Migdal-Eliashberg critical temperatures. For 140 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors and we find 24 unknown ones with a predicted anisotropic Tc value above 10 K. Among them, we identify a possible double-gap superconductor (p-doped BaB2), a nonmagnetic half-Heusler ZrRuSb, and the perovskite TaRu3C, all exhibiting significant Tc values. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

Hysteretic nonlinear elasticity is often observed in consolidated granular media, including concrete, mortar, sandstones, or rocks. Nonlinearity is frequently quantified using Nonlinear Resonant Ultrasonic Spectroscopy (NRUS), which provides tools to define nonlinear parameters for both fast and slow dynamic effects, often observed when analyzing the propagation velocity dependence on strain in such materials. The dependence of these parameters on temperature was studied with the aim of using NRUS to quantify the induced thermal damage; thus, experiments were performed spanning a wide temperature range. However, since most of these materials are used in construction (concrete and sandstone, mostly), it is of interest to understand how sensitive the measured nonlinear parameters are to small environmental temperature fluctuations. In this paper, the dependence on temperature of elastic parameters is investigated, both linear (wave velocity and damping) and nonlinear (the slope and hysteresis of the curves describing the strain dependence of wave velocity and residual conditioning effect on wave velocity), separating the slow from the fast dynamic properties of nonlinearity. The observations reported here denote a different behavior for concrete and Berea sandstone.

The reliability of solder joints plays an increasingly important role in power electronics. The thermal fatigue experienced due to the temperature fluctuations cause catastrophic failures. However, the ability to predict the fatigue for different thermal cycles is lacking. Experimental or simulation based approaches are typically too expensive to be conducted for a wide range of thermal loading conditions. A physics informed Long Short-Term Memory (PI-LSTM) is proposed here for predicting the plastic strain and related fatigue lifetime in solder joints. The LSTM model is trained on data generated by FEM simulations, enhanced by incorporating the flow rule into the loss function. The PI-LSTM accurately predicts the plastic strain and the stress components, enabling efficient reliability predictions. Using different reliability models, the estimated cycles to failure are found to be in close agreement with those from conventional FEM simulations, demonstrating the PI-LSTM's capability for reliability assessments.

Mission-critical electronic systems demand early and accurate detection of solder joint degradation to ensure reliability. Quad Flat No-Lead (QFN) packages, widely used in automotive and industrial applications, are especially prone to vibration-induced solder fatigue. However, traditional failure analysis methods (e.g., dye-and-pry, cross-sectioning, manual Xray inspection) are labor-intensive and often insufficient to detect early-stage cracks. This paper presents an automated inspection framework that combines high-resolution 3D X-ray tomography with a YOLOv11-based deep learning model to detect and segment vibration-induced cracks in QFN solder joints. The pipeline achieves precise localization of cracks in volumetric data, discriminates them from voids, and extracts morphological descriptors through parametric fitting. By statistically correlating these image-derived crack features with electrical resistance measurements recorded in situ during vibration tests, we establish a direct link between physical crack evolution and functional degradation of the joint. The results demonstrate that our AI-driven method can automatically identify tiny solder cracks and reliably offer predict impending interconnect failures in comparable granularity of traditional inspection techniques, surpassing them in speed. This approach offers a powerful prognostic health monitoring tool for electronic packaging, and it is extensible to other package types and stress conditions.

The ability to accurately predict the reliability and lifetime of electronics is of great importance to the industry. The failure of the solder joint is of particular interest for these predictions, because of their susceptibility to failure under thermo-mechanical stress. However, the experimental or even conventional simulation techniques employed to estimate the lifetime of a solder joint are often too expensive or time consuming to be of practical use. Therefore, this work introduces a physics-informed Long Short-Term Memory (LSTM) to predict the plastic strain in the critical area of the solder joint. The predicted values are in agreement with the values gained from finite elements, thereby demonstrating the advantage of applying the proposed methodology.

Machine Learning Operations (MLOps) involves software development practices for Machine Learning (ML), including data management, preprocessing, model training, deployment, and monitoring. While MLOps have received significant interest, much less work has been published addressing MLOps in industrial production settings lately, particularly if solutions are not cloud-based. This article addresses this shortcoming based on our and our partner’s real industrial experience in various projects. While there is a broad range of challenges for MLOps in cyber-physical production systems (CPPS), we focus on those related to data, models, and operations as we assume these will directly benefit the reader and provide solutions such as lightweight integration, integration of domain knowledge, periodic calibration, and interactive interfaces. In this way, we want to support practitioners in setting up industrial MLOps environments in CPPS. Further, we discuss explainability as an additional part of MLOps, which should be explored in more detail in the future.

Industrial assembly lines are the heartbeat of modern manufacturing, where precision and efficiency are paramount. This paper introduces a novel hybrid Explainable artificial intelligence (XAI) approach to enhance monitoring and analysis in industrial assembly. By fusing the power of vision anomaly detection models with the clarity of the gradient tree boosting algorithm, this framework not only boosts defect detection accuracy but also provides transparent, actionable insights. This synergy transforms how operators and engineers interact with AI, fostering trust and enhancing operational excellence.