In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.

Robotic automation has emerged as a leading solution for replacing human workers in dirty, dangerous, and demanding industries to ensure the safety of human workers. However, practical implementation of this technology remains limited, requiring substantial effort and costs. This study addresses the challenges specific to nuclear power plants, characterized by hazardous environments and physically demanding tasks such as nozzle dam replacement in confined workspaces. We propose a digital twin and deep-reinforcement-learning-driven robotic automation system with an autonomous mobile manipulator. The study follows a four-step process. First, we establish a simplified testbed for a nozzle dam replacement task and implement a high-fidelity digital twin model of the real-world testbed. Second, we employ a hybrid visual perception system that combines deep object pose estimation and an iterative closest point algorithm to enhance the accuracy of the six-dimensional pose estimation. Third, we use a deep-reinforcement-learning method, particularly the proximal policy optimization algorithm with inverse reachability map, and a centroidal waypoint strategy, to improve the controllability of an autonomous mobile manipulator. Finally, we conduct pre-performed simulations of the nozzle dam replacement in the digital twin and evaluate the system on a robot in the real-world testbed. The nozzle dam replacement with precise object pose estimation, navigation, target object grasping, and collision-free motion generation was successful. The robotic automation system achieved a $$92.0\%$$92.0 % success rate in the digital twin. Our proposed method can improve the efficiency and reliability of robotic automation systems for extreme workspaces and other perilous environments.

Sewing defect detection is an essential step in garment production quality control. Although sewing defects significantly influence the quality of clothing, they are yet to be studied widely compared to fabric defects. In this study, to address sewing defect detection and develop an appropriate method for small and labor-intensive garment companies, an on-machine broken stitch detection system is proposed. In hardware, a versatile mounting kit, including clamping, display, and adjustable linkage for a camera, is presented for easy installation on a typical industrial sewing machine and for placing the camera close to the sewing position. Additionally, a prototype is implemented using a low-cost single-board computer, Raspberry Pi 4 B, its camera, and Python language. For automated broken stitch detection, a method is proposed that includes removing the texture of the background fabric, image processing in the HSV color space, and edge detection for robust broken detection under various fabric and thread colors and lighting conditions. The proposed system demonstrates reasonable real-time detection accuracy. The maximum accuracy obtained on a sewing stitch dataset with 880 images and on-site tests of various industrial sewing machines is 82.5%, which is 12.1–34.6% higher than that of the two existing methods.

In this study, we propose a novel method for enhancing the control performance of a degraded robot manipulator by leveraging digital twins and proximal policy optimization, a specific deep reinforcement learning algorithm. Recently, various robotic technologies with high levels of controllability, safety, and reliability that incorporate the fourth industrial technology have been developed. Nevertheless, repairs or replacements owing to the performance degradation of sophisticated robot hardware or control systems are still time-, cost-, and manpower-consuming. To address these challenges, we propose a new strategy:1) approximate the unstable dynamic characteristics of six-degree-of-freedom low-performance robot manipulators to a digital twin with parameter tuning of a physics engine, 2) improve the accuracy and stability of reaching target points under diverse conditions through deep reinforcement learning using the domain randomization method, and 3) deploy a trained policy on an actual robot manipulator with degraded capabilities to validate the control performance improvement. Our method reduced the position error of a real robot manipulator by 63.0% and 39.0% compared to the built-in control method and the proportional-integral-derivative control method, respectively. Randomizing parameters in the physics engine of the digital twin during training allowed the method to simulate the imprecise motions of an actual degraded robot manipulator, facilitating the development of a more robust policy. Notably, our method has the potential to be applicable to all types of articulated robots, and presents a promising solution for maintaining performance while reducing long-term costs.

The growing use of collaborative robots (cobots) in industrial environments raises concerns about worker safety; flexible and adaptive systems are required to predict and prevent real-time cobot-worker collisions. This study proposes a digital-twin-based, cobot-worker collision risk-prediction system that utilizes the Unity game-development engine, robot operating system (ROS), and ultra-wideband (UWB) communication. The proposed system incorporates three modules: 1) real-time cobot-worker data capture, 2) digital-twin-based dynamic collision-zone management, and 3) collision-risk prediction. The core of the system is a data-driven cobot-worker digital twin that reflects the movements of the cobot and worker using ROS and UWB communication. This dynamic approach addresses the limitations of fixed safety-zone configurations by considering the cobot trajectory and the worker path projection in real time within the digital-twin modules. The system demonstrated low latency in data transmission from ROS to Unity (average 13 ms) and from UWB to Unity (average 140 ms), through implementation and evaluation. It also accurately predicted the minimum distance between the worker and cobot, accounting for varying worker speeds and future prediction times. This research offers a promising solution for enhancing worker safety in collaborative robotic environments by providing a real-time, digital-twin-based collision risk prediction system with dynamic collision zone management.