Abstract--In this study, we investigate the effectiveness of advanced feature engineering and hybrid model architectures for anomaly detection in a multivariate industrial time series, focusing on a steam turbine system. We evaluate the impact of change point-derived statistical features, clustering-based substructure representations, and hybrid learning strategies on detection performance. Despite their theoretical appeal, these complex approaches consistently underperformed compared to a simple Random Forest + XGBoost ensemble trained on segmented data. The ensemble achieved an AUC-ROC of 0.976, F1-score of 0.41, and 100% early detection within the defined time window. Our findings highlight that, in scenarios with highly imbalanced and temporally uncertain data, model simplicity combined with optimized segmentation can outperform more sophisticated architectures, offering greater robustness, interpretability, and operational utility. In recent years, anomaly detection in time series has become a critical challenge in industrial applications [1].

The ABB YuMi is a 7-DOF collaborative robot arm with a complex, redundant kinematic structure. Path planning for the YuMi is challenging, especially with joint limits considered. The redundant degree of freedom is parameterized by the Shoulder-Elbow-Wrist (SEW) angle, called the arm angle by ABB, but the exact definition must be known for path planning outside the RobotStudio simulator. We provide the first complete and validated definition of the SEW angle used for the YuMi. It follows the conventional SEW angle formulation with the shoulder-elbow direction chosen to be the direction of the fourth joint axis. Our definition also specifies the shoulder location, making it compatible with any choice of reference vector. A previous attempt to define the SEW angle exists in the literature, but it is incomplete and deviates from the behavior observed in RobotStudio. Because our formulation fits within the general SEW angle framework, we also obtain the expression for the SEW angle Jacobian and complete numerical conditions for all algorithmic singularities. Finally, we demonstrate using IK-Geo, our inverse kinematics (IK) solver based on subproblem decomposition, to find all IK solutions using 2D search. Code examples are available in a publicly accessible repository.

Root Cause Analysis (RCA) is a quality management method that aims to systematically investigate and identify the cause-and-effect relationships of problems and their underlying causes. Traditional methods are based on the analysis of problems by subject matter experts. In modern production processes, large amounts of data are collected. For this reason, increasingly computer-aided and data-driven methods are used for RCA. One of these methods are Causal Discovery Algorithms (CDA). This publication demonstrates the application of CDA on data from the assembly of a leading automotive manufacturer. The algorithms used learn the causal structure between the characteristics of the manufactured vehicles, the ergonomics and the temporal scope of the involved assembly processes, and quality-relevant product features based on representative data. This publication compares various CDAs in terms of their suitability in the context of quality management. For this purpose, the causal structures learned by the algorithms as well as their runtime are compared. This publication provides a contribution to quality management and demonstrates how CDAs can be used for RCA in assembly processes.

The multilingual dictionary of noun valency Portlex is considered to be the trigger for the creation of the automatic language generators Xera and Combinatoria, whose development and use is presented in this paper. Both prototypes are used for the automatic generation of nominal phrases with their mono- and bi-argumental valence slots, which could be used, among others, as dictionary examples or as integrated components of future autonomous E-Learning-Tools. As samples for new types of automatic valency dictionaries including user interaction, we consider the language generators as we know them today. In the specific methodological procedure for the development of the language generators, the syntactic-semantic description of the noun slots turns out to be the main focus from a syntagmatic and paradigmatic point of view. Along with factors such as representativeness, grammatical correctness, semantic coherence, frequency and the variety of lexical candidates, as well as semantic classes and argument structures, which are fixed components of both resources, a concept of a multi-sided prototype stands out. The combined application of this prototype concept as well as of word embeddings together with techniques from the field of automatic natural language processing and generation (NLP and NLG) opens up a new way for the future development of automatically generated plurilingual valency dictionaries. All things considered, the paper depicts the language generators both from the point of view of their development as well as from that of the users. The focus lies on the role of the prototype concept within the development of the resources.

This paper presents the development of a real-time simulator for the validation of controlling a large vehicle manipulator. The need for this development can be justified by the lack of such a simulator: There are neither open source projects nor commercial products, which would be suitable for testing cooperative control concepts. First, we present the nonlinear simulation model of the vehicle and the manipulator. For the modeling MATLAB/Simulink is used, which also enables a code generation into standalone C++ ROS-Nodes (Robot Operating System Nodes). The emerging challenges of the code generation are also discussed. Then, the obtained standalone C++ ROS-Nodes integrated in the simulator framework which includes a graphical user interface, a steering wheel and a joystick. This simulator can provide the real-time calculation of the overall system's motion enabling the interaction of human and automation. Furthermore, a qualitative validation of the model is given. Finally, the functionalities of the simulator is demonstrated in tests with a human operators.

Generative models for structure-based molecular design hold significant promise for drug discovery, with the potential to speed up the hit-to-lead development cycle, while improving the quality of drug candidates and reducing costs. Data sparsity and bias are, however, two main roadblocks to the development of 3D-aware models. Here we propose a first-in-kind training protocol based on multi-level contrastive learning for improved bias control and data efficiency. The framework leverages the large data resources available for 2D generative modelling with datasets of ligand-protein complexes. The result are hierarchical generative models that are topologically unbiased, explainable and customizable. We show how, by deconvolving the generative posterior into chemical, topological and structural context factors, we not only avoid common pitfalls in the design and evaluation of generative models, but furthermore gain detailed insight into the generative process itself. This improved transparency significantly aids method development, besides allowing fine-grained control over novelty vs familiarity.

With demand for robots growing as companies in multiple sectors look for new ways to enhance their productivity and competitiveness post-pandemic, ABB has compiled a set of growth predictions, looking at key trends driving demand for robots in the coming year. "The pandemic accelerated far-reaching global mega trends – from labor shortages and supply chain uncertainty, to the individualized consumer and growing pressure to operate sustainably and resiliently – leading new businesses to look to robotic automation," said Marc Segura, ABB's newly appointed robotics division President. "As technology opens new opportunities for meeting customer demands, new trends will continue to emerge that will further drive demand in areas where robots have traditionally not been used." Based on customer conversations, market research and a global survey of 250 companies across multiple industries, ABB has identified three key trends that will shape the demand for robots in 2022. With many countries restricting and phasing out the production of combustion engine vehicles over the next decade, the race towards electric cars has accelerated.

Researcher led by the National University of Singapore recently demonstrated that household robot vacuum cleaners can be hacked to behave like listening devices, which spy on their unsuspecting owners. Could industrial robots be similarly compromised? Hackers have exploited LiDAR (Light Detection And Ranging) scanner technology, as used in the latest iPhone, to turn a household vacuum cleaner into a spying device. If that's not sinister enough, elsewhere, in an experimental stunt, a friendly-looking humanoid robot was hacked to act like Chucky, the evil killer doll from the Child's Play movies. A video shows the robot attacking a tomato while emitting an evil laugh. The latter experiment was designed to demonstrate the vulnerabilities of technologies that can be hacked in an increasingly-connected world.

The Institute of Materials and Processes, IMP, of the University of Applied Sciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher Ingenieure e.V, AEN Automotive Engineering Network and their cooperation partners present their competences of AI-based solution approaches in the production engineering field. The online congress KI 4 Industry on November 12 and 13, 2020, showed what opportunities the use of artificial intelligence offers for medium-sized manufacturing companies, SMEs, and where potential fields of application lie. The main purpose of KI 4 Industry is to increase the transfer of knowledge, research and technology from universities to small and medium-sized enterprises, to demystify the term AI and to encourage companies to use AI-based solutions in their own value chain or in their products.

A robotic solution that brings efficiency, flexibility and improved environmental performance to custom spray jobs on cars has been recognised with an award from IFR and IEEE. ABB's PixelPaint robotic non-overspray technology for the automotive industry has won this year's Innovation and Entrepreneurship in Robotics & Automation (IERA) Award for Outstanding Achievements in Commercialising Innovative Robot and Automation Technology. PixelPaint uses inkjet head technology to directly apply high resolution two-tone or individualised designs to a car body in a single pass, enabling manufacturers to meet the rising demand for customised paint jobs while eliminating overspray. "With PixelPaint, 100 per cent of the paint can now be applied in half the time compared to the previous method used for custom paint jobs, with a much better finish quality," said Joerg Reger, managing director of ABB Robotics Auto OEM Business Line. "For our customers, this provides the triple bonus of saving millions of dollars per year through reduced paint consumption, improved efficiency and improved environmental performance through reduced VOC and CO2 emissions, while meeting their customer needs."