This paper presents a dynamic modeling approach for a 3-phase BLDC motor used in a differential-drive serving robot using bond graph (BG) methodology. Designed for structured indoor environments, the serving robot incorporates mechanical, electrical, and control components that require an integrated modeling strategy. Traditional methods often fall short in handling the multi-domain nature of such systems. Bond graphs, with their energy-based modeling capability, offer a unified framework for capturing electromechanical dynamics and physical interactions. This work develops a complete bond graph model of a three-phase BLDC motor-driven robot, simulates its performance under typical operating conditions, and validates the model through current, torque, EMF, and velocity responses. The results demonstrate the model’s effectiveness in reflecting real-world robot behavior, supporting future design optimization and control development.

Household-chemical bottling lines require high-speed verification of closure integrity, label conformity, expiry-code legibility, and container deformation, yet many existing solutions are either cost-prohibitive multi-camera automated optical inspection (AOI) tunnels or unreliable manual inspection. This paper presents a low-cost single-camera inspection station for mixed stock-keeping-unit (SKU) production that integrates deep-learning-based localization with deterministic, tolerance-based AOI. A fixed USB camera ( $$1280 \times 720$$ px) with ring-light illumination acquires images downstream of the expiry printer at a line-synchronous rate. YOLOv8 performs SKU classification and region selection, while task-specific modules assess cap sealing via hybrid fusion (height-profile AOI with a learned backup classifier), label/color conformity, volume–SKU consistency, expiry-date optical character recognition (OCR), and structural deformation. Per-bottle OK/NG decisions and module-level scores are logged to support traceability. On 33 mixed-SKU bottles, hybrid fusion achieved perfect cap-sealing classification (vs. 90.9% AOI-only and 87.9% YOLO-only), color/SKU accuracy of 100%, deformation detection of 97.0% (100% sensitivity, 94.4% specificity), expiry-date OCR accuracy of 87.9%, and label-conformity accuracy of 90.9%. These results show that, under a calibrated low-cost optical setup, commodity hardware can deliver practical multi-defect inline inspection when learning-based region selection, deterministic AOI rules, and traceability are co-designed.

Die forging is a widely used production method for manufacturing high-strength components with high accuracy and good mechanical properties. Since the die forging process involves many complicated thermo-mechanical coupled field physical problems, such as large plastic deformation, high temperature, friction, and heat transfer, etc., experimental studies are difficult and expensive to perform. The numerical simulation method has become the main method of study and optimal design for the die forging process. This paper reviews the published papers on numerical simulation of the die forging process from 2016 to 2026, in a structured literature review of computational simulations dealing with die forging processes. The literature search was conducted using the Scopus and Web of Science databases. After screening and full-text assessment, 24 relevant journal articles were selected for this paper. The articles studied were analyzed in terms of finite element modelling strategies, constitutive and material models used, friction and thermal boundary conditions considered, and process parameters. Typical results obtained from the studies discussed in the paper include stress, strain, temperature, forging load, and metal flow. The current state-of-the-art research has evolved from simple metal-flow predictions to more complex thermo-mechanical models, and even optimization-based models. Most of the current studies are based on experimentally derived constitutive equations, as well as more complex friction and heat-transfer models. Furthermore, studies applying optimization methods (Taguchi methods, design of experiments, machine learning, artificial intelligence) are increasingly common. The growing interest in the digital twin concept and real-time process control is observed. However, experimental validation, thermal contact modelling, and simulation of stress, strain, temperature, and microstructure in one simulation remain key challenges.