Automatic Assembly Research Articles

A Calibrator Fuzzy Ensemble for Highly-Accurate Robot Arm Calibration.

The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be easily affected by the insufficient representation of the solution space, therefore, suffering from the calibration accuracy loss. To address this issue, this study proposes a calibrator fuzzy ensemble (CFE) with twofold ideas: 1) implementing eight data-driven calibrators relying on different sophisticated machine learning algorithms for an industrial robot arm, which guarantees the accuracy of individual base models and 2) innovatively developing a fuzzy ensemble of the obtained eight diversified calibrators to obtain impressively high calibration accuracy for an industrial robot arm. Extensive experiments on an ABB IRB120 industrial robot implemented with MATLAB demonstrate that compared with state-of-the-art calibrators, CFE decreases the maximum error at 8.59%. Hence, it has great potential for real applications.

Automatic assembly and adjustment system for target surface of CMOS circuit board of star sensor

Automatic assembly and adjustment system for target surface of CMOS circuit board of star sensor

Automatic cover assembly system for a certain type of pendulum accelerometer

Automatic cover assembly system for a certain type of pendulum accelerometer

In-line measuring and feature similarity-based group and pairing assembly for metallic BPPs

Assembly of proton exchange membrane (PEM) fuel cell stacks with metallic bipolar plates (BPPs) is a process of high precision stacking for hundreds of individual sheet components. The accuracy of stacking process and the uniformity of contact status for layers effect the working efficiency of the stack. The manufacturing of metallic BPPs consists of several processes including pressing, welding and coating, in which each process will have an impact on the final shape of BPPs. Especially for large-scale metallic BPPs, which are critical parts for manufacturing high power fuel cell stack, the shape deviation will be magnified. In this research, a batch of normal size metallic BPPs are investigated as preliminary study for large-scale BPPs. They are measured by a 3D laser scanner and their geometric deviation are analyzed. An approach of in-line measuring integrated automatic assembly line for BPPs and a method of feature similarity-based group and pairing assembly are proposed with which the uncertainty of stacking result with random placing order of fuel cell components is expected to be reduced without changing previous manufacturing processes. The scanned features are compared to the nominal model of BPP and then fitted into surfaces to visualize the individual deviations. K-medoids algorithm is used to cluster these BPPs into groups. The result shows feasibility of applying this method to large-scale BPPs in future work.

Automatic assembly method of shield segments using line laser sensors

Automatic assembly method of shield segments using line laser sensors

Design and test of a Human-Machine Interface for assembly lines in a learning factory

Human-Machine Interfaces are pivotal in modern manufacturing, serving as the critical link between human operators and complex machinery. This paper explores the evolution of HMIs, emphasizing their importance in enhancing efficiency, safety, and productivity within manufacturing environments. While HMIs offer real-time monitoring and control functionalities, their effectiveness hinges on human-centric design principles. Incorporating insights from human-centred design, this research presents a case study conducted at the SLIM laboratory of the University of Bergamo. The study encompasses theoretical exploration, HMI re-design, and user evaluation within an automatic assembly line setting. The research identifies key factors influencing HMI usability through critical analysis and user feedback and offers valuable insights for optimizing HMIs and empowering operators in the digital manufacturing landscape.

Cooperative Control Strategy of Multiple Magnetic Millirobots for Automatic Assembly

Cooperative Control Strategy of Multiple Magnetic Millirobots for Automatic Assembly

High-Speed and High-Precision Online Measurement Technology for Engine Connecting Rod Weight and Barycenter

With the rapid advancement of manufacturing science and technology, the associated measurement technology has also been transformed. Traditional manual and single-instrument detection modes are unable to meet the requirements of fast beat, high precision, and online measurement in intelligent manufacturing. As connecting rods are typical and key components for automobile engines, their precision performance highly impacts the ultimate quality of engine assemblies. The online measurement of connecting rods was studied in this paper. According to the structural characteristics and parameter requirements of the connecting rods, an online multi-station measurement platform was designed and developed to measure, mark, and classify the measurements of the connecting rods in an automatic assembly line. Among these measurements, two significant parameters, the weight and the barycenter, were focused on in our work. A multi-point balance method was developed and applied to obtain the weights of the big and small ends as well as the position of the barycenter. An approach for weight sensor calibration and measurement data processing was also proposed. Finally, automatic and online measurement platforms were built for experimental verification. The results show that the measurement beats of the weight and the barycenter can reach 3 s/piece. The measured data were compared with a high-precision balance, and the average error of the connecting rod’s weight was 0.27%. The on-site application verification further proved the effectiveness and efficiency of our proposed measurement methods and machine, which enables fast and high-precision online measurement of connecting rods.

Quality analysis of range hood impeller automated assembly based on generalized grey relational degree

PurposeThis paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.Design/methodology/approachFirst, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.FindingsThe research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.Practical implicationsThe newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.Originality/valueThis study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

Adaptive System of Position Synchronization of Tape Materials in Automatic Assembly

Adaptive System of Position Synchronization of Tape Materials in Automatic Assembly

PLC Controlled Assembly Automation for Spot Welding Work Station

Automation has created an eminent need in the present trend of manufacturing industries to boost its throughput with reduced manual intervention and benefit in terms of good product quality, reduced scrape rate, and efficient usage of resources. Assembly process necessitates more perfection to achieve good quality products, which can only be possible by employing highly skilled and experienced operators which costs more and hence Automation is the best choice to mitigate this problem. The present work is an effort to upgrade the manual assembly station for spot welding process to assemble three sub-components called cup, fastener and mesh together through spot welding process with an automatic part handling mechanism, controlled by sensor-based Programmable Logic Controllers (PLCs). The automated assembly station resulted in flaw less welded components with reduced cycle time, which enhanced the assembly process productivity from 2 parts/min to 6 parts/min.

Real-time defect detection of saw chains on automatic assembly lines based on residual networks and knowledge coding

The use of machine vision methods has rapidly developed for detecting product defects, but it remains challenging to identify shape and color defects as well as the assembly relationship of parts due to occlusion between components and changes in assembly structures. This paper proposed a method for detecting saw chain assembly defects based on residual networks and knowledge coding to achieve parts segmentation and identification of assembly defects. Firstly, we design an adaptive segmentation algorithm for saw chain parts, which uses the Hough transform to locate rivets and completes the image segmentation based on the inherent assembly features between parts. Secondly, A trained ResNet (Residual Network) based deep learning model was used to identify the individual defects of saw chain parts, in which the transfer learning method was used to improve the detection speed of individual defects. Finally, a parallel inference method based on a knowledge encoding matrix is proposed for detecting part defects and constructing a sparse matrix to locate assembly defects. Evaluating the proposed method in building a saw chain testing platform. Results show that the proposed method can achieve real-time and high-precision detection requirements in different application scenarios. The detection accuracy on individual and assembly defects reaches 94.2% and 89.2%, respectively, which is better than the current state-of-the-art object detection models. Assembly defect detection methods are an important reference for inspecting other types of simple assemblies in the industry.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design.

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generatingprocess.

BIM-based framework of automatic tunnel segment assembly and deviation control

The design of universal segments and deviation control of segment assembly are essential for robust and low-risk tunnel construction. A building information modeling (BIM)-based framework was proposed for parametric modeling, automatic assembly, and deviation control of universal segments. First, segment models of different levels of detail (LoDs) were built based on BIM visual programming language (VPL) for different project life cycles. Then, the geometric constraints, requirements, and procedures for parametric segment assembly were distilled to develop a program that combines a novel typesetting algorithm with a 3D path replanning algorithm. Typesetting is implemented by introducing a point indication matrix, characterizing segments by sides, and manipulating geometries in a VPL. Simultaneously, 3D path replanning, with non-uniform rational B-splines (NURBS) and arcs as basic shapes, was used to resolve unacceptable deviation situations after typesetting. Finally, the proposed framework was validated on a water diversion line and was found to be more effective and accurate than the previous method.

Design and development of the deep-rock in-situ condition-preserved coring calibration platform

To systematically validate and calibrate the theory and technology of the deep in-situ condition-preserved coring, the in-situ conditions at different depths should be simulated, and the full-size coring tests should be carried out in this simulated environment. Therefore, a deep-rock in-situ condition-preserved coring calibration platform was designed and developed. The self-tightening sealing structure and the quick-disassembly structure were designed on the basis of an innovative segmented nonuniform-diameter structure, which was a breakthrough from the traditional high-pressure vessel frame and was verified by finite element simulation and actual testing under extreme working conditions, respectively. To simulate the actual deep in-situ environment with a temperature of 150 °C and pressure of 140 MPa for a large Φ450 mm×H1400 mm core, temperature and pressure control systems were designed by coupling, and a pre-embedded high-pressure-resistant temperature sensor was designed. Finally, high-precision assembly automation, complex movement coordination of the coring device with the platform, and rotary dynamic sealing were achieved by utilizing the combination of adaptive cabin body servo control and an adaptive mechanical structure in a limited space, laying a solid foundation for the calibration of in-situ condition-preserved coring.

FPC-BTB detection and positioning system based on optimized YOLOv5

With the aim of addressing the visual positioning problem of board-to-board (BTB) jacks during the automatic assembly of flexible printed circuit (FPC) in mobile phones, an FPC-BTB jack detection method based on the optimized You Only Look Once, version 5 (YOLOv5) deep learning algorithm was proposed in this study. An FPC-BTB jack real-time detection and positioning system was developed for the real-time target detection and pose output synchronization of the BTB jack. On that basis, a visual positioning experimental platform that integrated a UR5e manipulator arm and Hikvision industrial camera was built for BTB jack detection and positioning experiments. As indicated by the experimental results, the developed FPC-BTB jack detection and positioning system for BTB target recognition and positioning achieved a success rate of 99.677%. Its average detection accuracy reached 99.341%, the average confidence of the detected target was 91%, the detection and positioning speed reached 31.25 frames per second, and the positioning deviation was less than 0.93 mm, which conforms to the practical application requirements of the FPC assembly process.

Ergonomic Design of an Adaptive Automation Assembly System

Ergonomics is a key factor in the improvement of health and productivity in workplaces. Its use in improving the performance of a manufacturing process and its positive effects on productivity and human performance is drawing the attention of researchers and practitioners in the field of industrial engineering. This paper proposes an ergonomic design approach applied to an innovative prototype of an adaptive automation assembly system (A3S) equipped with Microsoft Kinect™ for real-time adjustment. The system acquires the anthropometric measurements of the operator by means of the 3-D sensing device and changes its layout, arranging the mobile elements accordingly. The aim of this study was to adapt the assembly workstation to the operator dimensions, improving the ergonomics of the workstation and reducing the risks of negative effects on workers’ health and safety. The case study of an assembly operation of a centrifugal electric pump is described to validate the proposed approach. The assembly operation was simulated at a traditional fixed workstation and at the A3S. The shoulder flexion angle during the assembly tasks at the A3S reduced between 18% and 47%. The ergonomic risk assessment confirmed the improvement of the ergonomic conditions and the ergonomic benefits of the A3S.

Research on automatic assembly technology of complex curved composite components

Sophisticated composite materials have a range of outstanding broad merits such as high particular strength, high specific stiffness, powerful designability, excellent tiredness resistance, excellent durability, are widely used in transportation, medical, electronics and other area. The assembly process of the composite surface components has large assembly stress, which affects the assembly accuracy. At the same time, the manual assembly cycle is elongated, the efficiency is low, and the quality stability is impoverished, which constraints the assembly mass and assembly power of the complex curved composite components. To improve the assembly power and carry out automatic assembly, this paper designs the automatic assembly technology scheme of the complex curved composite components, designs the automatic assembly scheme verification device, and conducts the automatic assembly test to verify the feasibility of the scheme.

Optimal design and verification of the 6-DOF Stewart structure used in vehicle-mounted automatic assembly

The Stewart structure with six degrees of freedoms (6-DOF), which has the advantages of big bearing capacity, strong stiffness and high accuracy, has been widely used in the indoor automatic assembly of aeronautics and astronautics. In this paper, a novel design method for the Stewart structure with limited installation space is proposed to extend the workspace by parameter optimization using genetic algorithm, and the performance is further verified via co-simulation. Firstly, the target function, which describes the workspace that satisfies both kinematic and dynamic constraints, is built with four parameters: the joints circle diameters and the angles between adjacent joints of both bottom and top platforms. Subsequently, the genetic algorithm is used to optimize the target function with the four parameters whose bounds are limited by vehicle-mounted installation space and structure interference. Finally, the geometry model is built in LMS. Motion and is studied using co-simulation method with the hydraulic system in AMEsim and control system in Simulink. With the cylinders motion accuracy within ±0.5 mm, the final position of top platform is consistent with the theoretical calculations, and the position error could be controlled within ±0.7 mm.

SEM Approach for Analysis of Lean Six Sigma Barriers to Electric Vehicle Assembly

This study investigates the barriers that the Lean Six Sigma implementation faces during the assembly of electric vehicles. In order to implement lean Six Sigma methodology in electric vehicle assembly processes effectively, it is crucial to identify and analyze the barriers that hinder process improvement. To identify the obstacles and create a conceptual model, a thorough literature review was conducted. Four factors, namely, integration of assembly, inspection, and testing, lack of trained and knowledgeable human resources, external and in-plant battery transportation, and manual assembly and rigid automation, were found to have the potential to affect the lean Six Sigma implementation. Three drivers, namely assembly cost, assembly time, and assembly effort were selected for the study. The model is then tested using the structural equation modeling and the gathered data. The results show a significant relationship between the three drivers and the four barriers of Lean Six Sigma implementation to the electric vehicle assembly.