Assembly Automation
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Published By Emerald (Mcb Up )

0144-5154

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Yi ◽  
Yang Sun ◽  
Saimei Yuan ◽  
Yiji Zhu ◽  
Mengyi Zhang ◽  
...  

Purpose The purpose of this paper is to provide a fast and accurate network for spatiotemporal action localization in videos. It detects human actions both in time and space simultaneously in real-time, which is applicable in real-world scenarios such as safety monitoring and collaborative assembly. Design/methodology/approach This paper design an end-to-end deep learning network called collaborator only watch once (COWO). COWO recognizes the ongoing human activities in real-time with enhanced accuracy. COWO inherits from the architecture of you only watch once (YOWO), known to be the best performing network for online action localization to date, but with three major structural modifications: COWO enhances the intraclass compactness and enlarges the interclass separability in the feature level. A new correlation channel fusion and attention mechanism are designed based on the Pearson correlation coefficient. Accordingly, a correction loss function is designed. This function minimizes the same class distance and enhances the intraclass compactness. Use a probabilistic K-means clustering technique for selecting the initial seed points. The idea behind this is that the initial distance between cluster centers should be as considerable as possible. CIOU regression loss function is applied instead of the Smooth L1 loss function to help the model converge stably. Findings COWO outperforms the original YOWO with improvements of frame mAP 3% and 2.1% at a speed of 35.12 fps. Compared with the two-stream, T-CNN, C3D, the improvement is about 5% and 14.5% when applied to J-HMDB-21, UCF101-24 and AGOT data sets. Originality/value COWO extends more flexibility for assembly scenarios as it perceives spatiotemporal human actions in real-time. It contributes to many real-world scenarios such as safety monitoring and collaborative assembly.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyu Hao ◽  
Shugang Li ◽  
Tianjun Zhang

Purpose This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection. Design/methodology/approach First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure. Findings The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform. Originality/value The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyu Hao ◽  
Shugang Li ◽  
Tianjun Zhang

Purpose In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law. Design/methodology/approach First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend. Findings This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach. Originality/value For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhifang Wang ◽  
Jianguo Yu ◽  
Shangjing Lin

Purpose To solve the above problems and ensure the stability of the ad hoc network node topology in the process of wireless signal transmission, this paper aims to design a robust adaptive sliding film fault-tolerant controller under the nonlinear distortion of signal transmission in an amorphous flat air-to-ground wireless ad hoc network system. Design/methodology/approach This paper designs a robust adaptive sliding film fault-tolerant controller under the nonlinear distortion of signal transmission in an amorphous flat air-to-ground wireless ad hoc network system. Findings The simulation results show that the amorphous flat wireless self-organizing network system has good nonlinear distortion fault-tolerant correction ability under the feedback control of the designed controller, and the system has the asymptotically stable convergence ability; the test results show: the node topology of the self-organizing network structural stability is significantly improved, which provides a foundation for the subsequent realization of long-distance transmission of ad hoc network nodes. Research limitations/implications Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further. Originality/value The controller can extract the fault information caused by nonlinear distortion in the wireless signal transmission process, and at the same time, its feedback matrix K can gradually converge the generated wireless signal error to zero, to realize the stable transmission of the wireless signal.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weilei Shen ◽  
Qiangqiang Jiang ◽  
Yang Yang

Purpose The purpose of this paper is to construct a task assignment model for U-shaped production lines with collaborative task, which is optimized by minimizing the number of workers and balancing the workload of the operators. The ultimate goal is to increase productivity by increasing the U-line balance and balancing the load on the operators. Design/methodology/approach First, task selection and update mechanism are analyzed and the task selection mechanism suitable for collaborative task is proposed. Second, M-COMOSAL is obtained by improving the original COMOSAL. Finally, The M-COMOSAL algorithm and the COMAOSAL algorithm are used to perform job assignment on the double-acting clutch U-shaped assembly line. Findings According to the allocation scheme obtained by M-COMSOAL, the beat can be adjusted according to the change of order demand. The final allocation scheme is superior to the COMSOAL algorithm in terms of number of workers, working time, production tempo and balance rate. In particular, compared with the old scheme, the new scheme showed a decrease of 16.7% in the number of employees and a 18.8% increase in the production line balance rate. Thus, the method is helpful to reduce the number of operators and balance the workload. Originality/value The new algorithm proposed in this paper for the assignment of collaborative task can minimize the number of workers and balance the load of operators, which is of great significance for improving the balance rate of U-shaped production lines and the utilization of personnel or equipment.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wei Fang ◽  
Mingyu Fu ◽  
Lianyu Zheng

Purpose This paper aims to perform the real-time and accurate ergonomics analysis for the operator in the manual assembly, with the purpose of identifying potential ergonomic injuries when encountering labor-excessive and unreasonable assembly operations. Design/methodology/approach Instead of acquiring body data for ergonomic evaluation by arranging many observers around, this paper proposes a multi-sensor based wearable system to track worker’s posture for a continuous ergonomic assessment. Moreover, given the accurate neck postural data from the shop floor by the proposed wearable system, a continuous rapid upper limb assessment method with robustness to occasional posture changes, is proposed to evaluate the neck and upper back risk during the manual assembly operations. Findings The proposed method can retrieve human activity data during manual assembly operations, and experimental results illustrate that the proposed work is flexible and accurate for continuous ergonomic assessments in manual assembly operations. Originality/value Based on the proposed multi-sensor based wearable system for posture acquisition, a real-time and high-precision ergonomics analysis is achieved with the postural data arrived continuously, it can provide a more objective indicator to assess the ergonomics during manual assembly.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ya'nan Lou ◽  
Pengkun Quan ◽  
Haoyu Lin ◽  
Zhuo Liang ◽  
Dongbo Wei ◽  
...  

Purpose This purpose of this paper is to design a peg-in-hole controller for a cable-driven serial robot with compliant wrist (CDSR-CW) using cable tensions and joint positions. The peg is connected to the robot link through a CW. It is required that the controller does not rely on any external sensors such as 6-axis wrist force/torque (F/T) sensor, and only the compliance matrix’s estimated value of the CW is known. Design/methodology/approach First, the peg-in-hole assembly system based on a CDSR-CW is analyzed. Second, a characterization algorithm using micro cable tensions and joint positions to express the elastic F/T at the CW is established. Next, under the premise of only knowing the compliance matrix’s estimate, a peg-in-hole controller based on force/position hybrid control is proposed. Findings The experiment results show that the plug contact F/T can be tracked well. This verifies the validity and correctness of the characterization algorithm and peg-in-hole controller for CDSR-CWs in this paper. Originality/value First, to the authors’ knowledge, there is no relevant work about the peg-in-hole assembly task using a CDSR-CW. Besides, the proposed characterization algorithm for the elastic F/T makes the peg-in-hole controller get rid of the dependence on the F/T sensor, which expands the application scenarios of the peg-in-hole controller. Finally, the controller does not require an accurate compliance matrix, which also increases its applicability.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shanlin Zhong ◽  
Ziyu Chen ◽  
Junjie Zhou

Purpose Human-like musculoskeletal robots can fulfill flexible movement and manipulation with the help of multi joints and actuators. However, in general, sophisticated structures, accurate sensors and well-designed control are all necessary for a musculoskeletal robot to achieve high-precision movement. How to realize the reliable and accurate movement of the robot under the condition of limited sensing and control accuracy is still a bottleneck problem. This paper aims to improve the movement performance of musculoskeletal system by bio-inspired method. Design/methodology/approach Inspired by two kinds of natural constraints, the convergent force field found in neuroscience and attractive region in the environment found in information science, the authors proposed a structure transforming optimization algorithm for constructing constraint force field in musculoskeletal robots. Due to the characteristics of rigid-flexible coupling and variable structures, a constraint force field can be constructed in the task space of the musculoskeletal robot by optimizing the arrangement of muscles. Findings With the help of the constraint force field, the robot can complete precise and robust movement with constant control signals, which brings in the possibility to reduce the requirement of sensing feedback during the motion control of the robot. Experiments are conducted on a musculoskeletal model to evaluate the performance of the proposed method in movement accuracy, noise robustness and structure sensitivity. Originality/value A novel concept, constraint force field, is proposed to realize high-precision movements of musculoskeletal robots. It provides a new theoretical basis for improving the performance of robotic manipulation such as assembly and grasping under the condition that the accuracy of control and sensory are limited.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hui Zhai ◽  
Wei Xiong ◽  
Fujin Li ◽  
Jie Yang ◽  
Dongyan Su ◽  
...  

Purpose The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises. Design/methodology/approach In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result. Findings The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963. Originality/value A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.


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