Search Results (156)

This study investigates the effectiveness of the Xsens motion capture system in performing ergonomic analysis compared to traditional manual assessments by experts in the specific environment of assembly lines. A comprehensive literature review emphasizes the need to investigate the reliability of new, promising high-tech systems. The main objective was therefore to compare the Ergonomic Assessment Worksheet (EAWS) assessment approach performed with Xsens motion capture technology and Process Simulate V16 software with the manual method using EAWS digital prepared by experts in the controlled workflow. The greatest value of the research conducted lies in the novel integration of the state-of-the-art Xsens motion capture technology with the Process Simulate V16 software environment and the use of the licensed EAWS ergonomic method and Methods-Time Measurement Universal Analyzing System (MTM-UAS). The results are presented in the form of a case study. The results show a large similarity between the whole-body results and a large difference in the upper limb results, confirming the initial benefits of the Xsens equipment but also pointing to the need to verify its reliability on larger samples. The study highlights the potential of integrating Xsens motion capture data into ergonomic assessments and tuning of the assembly line to increase productivity and worker safety. Full article

Packaging equipment plays a crucial role in the semiconductor industry by enhancing product quality and reducing labor costs through automation. Research was conducted on IGBT module packaging equipment (an automatic pin insertion machine) during the pin assembly process of insulated gate bipolar transistor (IGBT) modules to improve productivity and product quality. First, the manual pin assembly process was divided into four stages: feeding, stabilizing, clamping, and inserting. Each stage was completed by separate cams, and corresponding step timing diagrams are drawn. The profiles of the four cams were designed and verified through theoretical calculations and kinematic simulations using a seventh-degree polynomial curve fitting method. Then, image algorithms were developed to detect pin tilt defects, pin tip defects, and to provide visual guidance for pin insertion. Finally, a pin insertion machine and its human–machine interaction interface were constructed. On-machine results show that the pin cutting pass rate reached 97%, the average insertion time for one pin was 2.84 s, the pass rate for pin insertion reached 99.75%, and the pin image guidance accuracy was 0.02 mm. Therefore, the designed pin assembly machine can reliably and consistently perform the pin insertion task, providing theoretical and experimental insights for the automated production of IGBT modules. Full article

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

The payload module of a high-throughput satellite involves the complex assembly of various components, which plays a vital role in maintaining the satellite’s structural and functional integrity. To support this, inspections during the assembly process are essential for minimizing human error, reducing inspection time, and ensuring adherence to design specifications. However, the current inspection process is entirely manual. It requires substantial manpower and time and is prone to errors such as missed or false detections, which compromise the overall effectiveness of the inspection process. To enhance the inspection efficiency and accuracy of the payload module in high-throughput satellites, this paper proposes a framework for text detection and recognition targeting diamond labels, R-hole labels, and interface labels within payload module images. Detecting and recognizing text labels on products in the high-throughput satellite payload module provides a means to determine the individual products’ assembly states and the correctness of their connection relationships with the waveguides/cables. The framework consists of two key components: a copy-and-paste data augmentation method, which generates synthetic images by overlaying foreground images onto background images, together with a text detection and recognition model incorporating a dual decoder. The detection accuracy on the simulated payload module data reached 87.42%, while the operational efficiency improved significantly by reducing the inspection time from 5 days to just 1 day. Full article

While early studies on macroscopic self-assembly peaked in the late 20th century, recent research continues to explore and expand the field’s potential through innovative materials and external control strategies. To harness this potential, a unit cell was designed and 3D-printed that could form a face-centered cubic lattice and stabilize it through a biomimetic mechanism for mechanical interlocking. The wing coupling structures of the brown marmorated stink bug were examined under a scanning electron microscope to be used as a source of bio-inspiration for the interlocking mechanism. A total of 20 unit cells were studied in five different self-assembly processes and in different compression scenarios. A maximum average of 34% of unit cells remained stable, and 20% were mechanically interlocked after self-assembly tests. The compression tests performed on a single unit cell revealed that the cell can withstand forces up to 1000 N without any plastic deformation. Pyramid configurations from 5-unit cells were manually assembled and assessed in compression tests. They showed an average compression force of 294 N. As the first study focused on self-assembly through mechanical interlocking, further studies that change the unit cell production and self-assembly processes are expected to improve upon these results. Full article

In recent years, the AEC industry has increasingly sought sustainable solutions to enhance productivity and reduce environmental pollution, with wood emerging as a key renewable material due to its excellent carbon sequestration capability and low ecological footprint. Despite significant advances in digital fabrication technologies for timber construction, on-site assembly still predominantly relies on manual operations, thereby limiting efficiency and precision. To address this challenge, this study proposes an automated on-site timber construction process that integrates a mobile construction platform (MCP), a fiducial marker system (FMS) and a UWB/IMU integrated navigation system. By deconstructing traditional modular stacking methods and iteratively developing the process in a controlled laboratory environment, the authors formalize raw construction experience into an effective workflow, supplemented by a self-feedback error correction system to achieve precise, real-time end-effector positioning. Extensive experimental results demonstrate that the system consistently achieves millimeter-level positioning accuracy across all test scenarios, with translational errors of approximately 1 mm and an average repeat positioning precision of up to 0.08 mm, thereby aligning with on-site timber construction requirements. These findings validate the method’s technical reliability, robustness and practical applicability, laying a solid foundation for a smooth transition from laboratory trials to large-scale on-site timber construction. Full article

This study provides a set of designs, simulations and experiments for developing an actuating method for manual pipettes. The goal is to enable robotic manipulation and automatic pipetting, while using manual pipetting devices. This automation is designed to be used as a flexible alternative tool in small and medium-sized biochemistry laboratories that do not possess proper automated pipetting technology, in order to relieve the lab technicians from the tedious, repetitive and error-prone process of manual pipetting needed for the preparation of biological samples. The selected approach is to use a set of pressure-controlled pneumatic cylinders in order to control the actuation and force of the pipettes’ manual buttons. This paper presents a mechanical design, analysis, pneumatic simulation and functional robotic simulation of the developed device, and a comparison of possible pneumatic solutions is presented to explain the selected actuation method. Remote pneumatic pressure sensing is employed in order to avoid electrical sensors, connectors and wires in the area of the actuators, thus expanding the possibility of working in some electromagnetic-compatible environments and to simplify the connecting and cleaning process of the entire device. A functional simulation is conducted using a combination of software packages: Fluidsim for pneumatic simulation, URSim for robot programming and CoppeliaSim for application integration and visualization. Experimental validation is conducted using off-the-shelf pneumatic components, assembled with 3D-printed parts and mounted onto an existing pneumatic gripper. This complete assembly is attached to an industrial collaborative robot, as an end effector, and a program is written to test and validate the functions of the complete device. The in-process actuators’ working pressure is recorded and analyzed to determine the suitability of the proposed method and pipetting ability. Supplemental digital data are provided in the form of pneumatic circuit diagrams, a robot program, simulation scene and recorded values, to facilitate experimental replication and further development. Full article

Physiologically based pharmacokinetic (PBPK) modelling for biologics, such as monoclonal antibodies and therapeutic proteins, involves capturing complex processes, including target-mediated drug disposition (TMDD), FcRn-mediated recycling, and tissue-specific distribution. The Simcyp Designer Biologics PBPK Platform Model offers an intuitive and efficient platform for constructing mechanistic PBPK models with pre-defined templates and automated model assembly, reducing manual input and improving reproducibility. This tutorial provides a step-by-step guide to using the platform, highlighting features such as cross-species scaling, population variability simulations, and flexibility for model customization. Practical case studies demonstrate the platform’s capability to streamline workflows, enabling rapid, mechanistic model development to address key questions in biologics drug development. By automating critical processes, this tool enhances decision-making in translational research, optimizing the modelling and simulation of large molecules across discovery and clinical stages. Full article

Aiming at the efficiency bottleneck and error risk caused by the over-reliance on manual experience in traditional assembly sequence planning, the urgent demand for deep reuse of multi-source knowledge in complex products, and the growing demand for resource saving and sustainable development, this study focuses on the core problem of the lack of empirical knowledge modeling and reasoning mechanism in the assembly process of complex products, and proposes a three-phase assembly sequence intelligent planning method that integrates deep learning and ontology theory. Method: First, we propose an instance segmentation model based on the improved Mask R-CNN architecture, incorporate the ResNet50 pre-training strategy to enhance the generalization ability of the model, reconstruct the Mask branch, and add the attention mechanism to achieve high-precision recognition and extraction of geometric features of the assembly parts. Secondly, a multi-level assembly ontology semantic model is constructed based on the ontology theory, which realizes the structured expression of knowledge from three dimensions: product structure level (product–assembly–part), physical attributes (weight/precision/dimension), and assembly process (number of fits/direction of assembly), and builds a reasoning system with six assembly rules in combination with the SWRL language, which covers the core elements of geometric constraints, process priority, and so on. Finally, experiments are carried out with the example gearbox as the validation object, and the results show that the assembly sequence generated by the method meets the requirements of the process specification, which verifies the validity of the technology path. By constructing a closed-loop technology path of “visual perception–knowledge reasoning–sequence generation”, this study effectively overcomes the subjective bias of manual planning, integrates multi-source knowledge to improve the reuse rate of knowledge, and provides a solution of both theoretical value and engineering feasibility for the intelligent assembly of complex electromechanical products, which reduces the R&D cost and contributes to the sustainable development. Full article

During the maintenance of railway fasteners, there are issues with the current nut disassembly and assembly operation, including low efficiency, heavy reliance on manual labor, and high physical strain. A mechanical device has been designed to move along the railway track while identifying and locating the center of the nut to perform disassembly and assembly operations. First, based on the nut disassembly and assembly process and the operating environment, the structure of the equipment was designed. This machine can simultaneously disassemble and assemble all the nuts on a single rail tie and accommodate position errors and deviations of spiral spikes. Secondly, to verify the structural reliability of the designed machine, a static simulation analysis was conducted on the key load-bearing structures under extreme operating conditions. Based on the simulation results, a lightweight design was applied to the machine’s carrier platform. The performance of the nut assembly and disassembly mechanism was optimized based on the Kriging model and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimized machine reduced its mass by 21.7% and increased its strength by more than 30%. A transient analysis was also conducted on the optimized machine structure, further validating its strength. Finally, based on the design and optimization results, a physical prototype of the nut disassembly machine was constructed and tested. The results show that the device can efficiently perform nut disassembly and assembly tasks on the railway track. Both the mechanical structure’s reliability and functionality meet the design objectives and requirements, demonstrating significant application value for promoting the intelligent maintenance of railways. Full article

A data-driven 3D simulation for the robotic automation of the most labor-intensive packaging process in meal kit production was developed using Tecnomatix plant simulation software. The workflow and environments of the existing manual process were analyzed. An existing production site was scanned using a 3D Lidar scanner to create 3D models and design the initial assembly layout. Two types of 3D simulation models, implemented with a single or double delta robot, were designed to determine the optimal robot-automated packaging process. Key performance indicators for simulation models of a manual and two robot automation systems were analyzed. The throughputs of the manual, single delta robot and double delta robot models were 2112, 1510, and 2568 ea/h, respectively. The single robot system achieved only 68.3% of the throughput of the manual process, which is attributed to a cycle time of 2.36 s for picking and placing all components. On the other hand, the cycle time of the double robot system was 1.66 times faster, and the throughput was 1.7 times greater compared to the single robot system. The developed 3D simulation model for the meal kit packaging system demonstrates the potential of robotic automation in addressing the labor shortage issue as well as improving production efficiency. Full article

The Printed Circuit Board (PCB) manufacturing industry is a rapidly expanding sector, fueled by advanced technologies and precision-oriented production processes. The placement of Surface-Mount Device (SMD) components in PCB assembly is efficiently automated using robots and design software-generated coordinate files; however, the PCB repair process remains significantly more complex and challenging. Repairing faulty PCBs, particularly replacing defective SMD components, requires high precision and significant manual expertise, making automated solutions both rare and difficult to implement. This study introduces a novel real-time machine vision-based coordinate estimation system designed for estimating the coordinates of SMD components during soldering or desoldering tasks. The system was specifically designed for Selective Compliance Articulated Robot Arm (SCARA) robots to overcome the challenges of repairing miniature PCB components. The proposed system integrates Image-Based Visual Servoing (IBVS) for precise X and Y coordinate estimation and a simplified laser triangulation method for Z-axis depth estimation. The system demonstrated accuracy rates of 98% for X and Y axes and 99% for the Z axis, coupled with high operational speed. The developed solution highlights the potential for automating PCB repair processes by enabling SCARA robots to execute precise picking and placement tasks. When equipped with a hot-air gun as the end-effector, the system could enable automated soldering and desoldering, effectively replacing faulty SMD components without human intervention. This advancement has the potential to bridge a critical gap in the PCB repair industry, improving efficiency and reducing dependence on manual expertise. Full article

In the ocean environment, passive acoustic monitoring (PAM) is an important technique for the surveillance of cetacean species. Manual detection for a large amount of PAM data is inefficient and time-consuming. To extract useful features from a large amount of PAM data for classifying different cetacean species, we propose an automatic detection and unsupervised clustering-based classification method for cetacean vocal signals. This paper overcomes the limitations of the traditional threshold-based method, and the threshold is set adaptively according to the mean value of the signal energy in each frame. Furthermore, we also address the problem of the high cost of data training and labeling in deep-learning-based methods by using the unsupervised clustering-based classification method. Firstly, the automatic detection method extracts vocal signals from PAM data and, at the same time, removes clutter information. Then, the vocal signals are analyzed for classification using a clustering algorithm. This method grabs the acoustic characteristics of vocal signals and distinguishes them from environmental noise. We process 194 audio files in a total of 25.3 h of vocal signal from two marine mammal public databases. Five kinds of vocal signals from different cetaceans are extracted and assembled to form 8 datasets for classification. The verification experiments were conducted on four clustering algorithms based on two performance metrics. The experimental results confirm the effectiveness of the proposed method. The proposed method automatically removes about 75% of clutter data from 1581.3MB of data in audio files and extracts 75.75 MB of the features detected by our algorithm. Four classical unsupervised clustering algorithms are performed on the datasets we made for verification and obtain an average accuracy rate of 84.83%. Full article

The need for scalable, immersive training systems is universal and recently has been included in fields that rely on complex, hands-on processes, such as surgery operations, assembly operations, construction processes training, etc. This paper examines the potential to support immersive training via digital tool manipulation in the domain of traditional handicrafts. The proposed methodology employs Finite Element Method simulations to compute material transformations and apply them to interactive virtual environments. The challenge is to accurately simulate human–tool interactions, which are critical to the acquisition of manual skills. Using Simulia Abaqus (v.2023HF2), crafting simulations are authored, executed, and exported as animation sequences. These are further refined in Blender (v3.6) and integrated into Unity to create reusable training components called Action Animators. Two software applications—Craft Studio (v1.0) and Apprentice Studio (v1.0)—are designed and implemented to enable instructors to create training lessons and students to practice and get evaluated in virtual environments. The methodology has wide-ranging applications beyond crafts, offering a solution for immersive training in skill-based activities. The validation and evaluation of the proposed approach suggest that it can significantly improve training effectiveness, scalability, and accessibility across various industries. Full article

Addressing the challenge of manual dependency and the difficulty in automating the online detection of height discrepancies following engine oil seal assembly, this paper proposes a composite vision-based method for the post-assembly size inspection of engine oil seals. The proposed method enables non-contact, online three-dimensional measurement of oil seals already installed on the engine. To achieve accurate positioning of the inner and outer ring regions of the oil seals, the process begins with obtaining the center point and the major and minor axes through ellipse fitting, which is performed using progressive template matching and the least squares method. After scaling the ellipse along its axes, the preprocessed image is segmented using the peak–valley thresholding method to generate an annular ROI (region of interest) mask, thereby reducing the complexity of the image. By integrating three-frequency four-step phase-shifting profilometry with an improved RANSAC (random sample consensus)-based plane fitting algorithm, the height difference between the inner and outer rings as well as the press-in depth are accurately calculated, effectively eliminating interference from non-target regions. Experimental results demonstrate that the proposed method significantly outperforms traditional manual measurement in terms of speed, with the relative deviations of the height difference and press-in depth confined within 0.33% and 1.45%, respectively, and a detection success rate of 96.35% over 1415 samples. Compared with existing methods, the proposed approach not only enhances detection accuracy and efficiency but also provides a practical and reliable solution for real-time monitoring of engine oil seal assembly dimensions, highlighting its substantial industrial application potential. Full article