Purpose The purpose of this paper is to establish a tolerance optimization method based on manufacturing difficulty computation using the genetic algorithm (GA) method. This proposal is among the authors’ perspectives of accomplished previous research work to cooperative optimal tolerance allocation approach for concurrent engineering area. Design/methodology/approach This study introduces the proposed GA modeling. The objective function of the proposed GA is to minimize total cost constrained by the equation of functional requirements tolerances considering difficulty coefficients. The manufacturing difficulty computation is based on tools for the study and analysis of reliability of the design or the process, as the failure mode, effects and criticality analysis (FMECA) and Ishikawa diagram. Findings The proposed approach, based on difficulty coefficient computation and GA optimization method [genetic algorithm optimization using difficulty coefficient computation (GADCC)], has been applied to mechanical assembly taken from the literature and compared to previous methods regarding tolerance values and computed total cost. The total cost is the summation of manufacturing cost and quality loss. The proposed approach is economic and efficient that leads to facilitate the manufacturing of difficult dimensions by increasing their tolerances and reducing the rate of defect parts of the assembly. Originality/value The originality of this new optimal tolerance allocation method is to make a marriage between GA and manufacturing difficulty. The computation of part dimensions difficulty is based on incorporating FMECA tool and Ishikawa diagram This comparative study highlights the benefits of the proposed GADCC optimization method. The results lead to obtain optimal tolerances that minimize the total cost and respect the functional, quality and manufacturing requirements.
{"title":"Associated tolerance optimization approach using manufacturing difficulty coefficients and genetic algorithm","authors":"Maroua Ghali, Sami Elghali, N. Aifaoui","doi":"10.1108/aa-02-2022-0024","DOIUrl":"https://doi.org/10.1108/aa-02-2022-0024","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to establish a tolerance optimization method based on manufacturing difficulty computation using the genetic algorithm (GA) method. This proposal is among the authors’ perspectives of accomplished previous research work to cooperative optimal tolerance allocation approach for concurrent engineering area.\u0000\u0000\u0000Design/methodology/approach\u0000This study introduces the proposed GA modeling. The objective function of the proposed GA is to minimize total cost constrained by the equation of functional requirements tolerances considering difficulty coefficients. The manufacturing difficulty computation is based on tools for the study and analysis of reliability of the design or the process, as the failure mode, effects and criticality analysis (FMECA) and Ishikawa diagram.\u0000\u0000\u0000Findings\u0000The proposed approach, based on difficulty coefficient computation and GA optimization method [genetic algorithm optimization using difficulty coefficient computation (GADCC)], has been applied to mechanical assembly taken from the literature and compared to previous methods regarding tolerance values and computed total cost. The total cost is the summation of manufacturing cost and quality loss. The proposed approach is economic and efficient that leads to facilitate the manufacturing of difficult dimensions by increasing their tolerances and reducing the rate of defect parts of the assembly.\u0000\u0000\u0000Originality/value\u0000The originality of this new optimal tolerance allocation method is to make a marriage between GA and manufacturing difficulty. The computation of part dimensions difficulty is based on incorporating FMECA tool and Ishikawa diagram This comparative study highlights the benefits of the proposed GADCC optimization method. The results lead to obtain optimal tolerances that minimize the total cost and respect the functional, quality and manufacturing requirements.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44446156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fengwei Jing, Meng-Jia Zhang, Jiefeng Li, Guozheng Xu, Jing Wang
Purpose Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm. Design/methodology/approach Aiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition. Findings The performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability. Originality/value This research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.
{"title":"Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN model","authors":"Fengwei Jing, Meng-Jia Zhang, Jiefeng Li, Guozheng Xu, Jing Wang","doi":"10.1108/aa-07-2022-0179","DOIUrl":"https://doi.org/10.1108/aa-07-2022-0179","url":null,"abstract":"\u0000Purpose\u0000Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm.\u0000\u0000\u0000Design/methodology/approach\u0000Aiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition.\u0000\u0000\u0000Findings\u0000The performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability.\u0000\u0000\u0000Originality/value\u0000This research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43639292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance. Design/methodology/approach A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy. Findings The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases. Originality/value The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.
{"title":"Fusion event-triggered model predictive control based on shrinking prediction horizon","authors":"Qun Cao, Yuanqing Xia, Zhongqi Sun, Li Dai","doi":"10.1108/aa-02-2022-0022","DOIUrl":"https://doi.org/10.1108/aa-02-2022-0022","url":null,"abstract":"\u0000Purpose\u0000This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance.\u0000\u0000\u0000Design/methodology/approach\u0000A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.\u0000\u0000\u0000Findings\u0000The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases.\u0000\u0000\u0000Originality/value\u0000The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44779112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose Aiming at the problem that the transmission mechanism of the assembly error of the multi-stage rotor with saddle surface type is not clear, the purpose of this paper is to propose a deep belief network to realize the prediction of the coaxiality and perpendicularity of the multi-stage rotor. Design/methodology/approach First, the surface type of the aero-engine rotor is classified. The rotor surface profile sampling data is converted into image structure data, and a rotor surface type classifier based on convolutional neural network is established. Then, for the saddle surface rotor, a prediction model of coaxiality and perpendicularity based on deep belief network is established. To verify the effectiveness of the coaxiality and perpendicularity prediction method proposed in this paper, a multi-stage rotor coaxiality and perpendicularity assembly measurement experiment is carried out. Findings The results of this paper show that the accuracy rate of face type classification using convolutional neural network is 99%, which meets the requirements of subsequent assembly process. For the 80 sets of test samples, the average errors of the coaxiality and perpendicularity of the deep belief network prediction method are 0.1 and 1.6 µm, respectively. Originality/value Therefore, the method proposed in this paper can be used not only for rotor surface classification but also to guide the assembly of aero-engine multi-stage rotors.
{"title":"Coaxiality and perpendicularity prediction of saddle surface rotor based on deep belief networks","authors":"Chuanzhi Sun, Yin Chu Wang, Qing Lu, Yongmeng Liu, Jiubin Tan","doi":"10.1108/aa-06-2022-0163","DOIUrl":"https://doi.org/10.1108/aa-06-2022-0163","url":null,"abstract":"\u0000Purpose\u0000Aiming at the problem that the transmission mechanism of the assembly error of the multi-stage rotor with saddle surface type is not clear, the purpose of this paper is to propose a deep belief network to realize the prediction of the coaxiality and perpendicularity of the multi-stage rotor.\u0000\u0000\u0000Design/methodology/approach\u0000First, the surface type of the aero-engine rotor is classified. The rotor surface profile sampling data is converted into image structure data, and a rotor surface type classifier based on convolutional neural network is established. Then, for the saddle surface rotor, a prediction model of coaxiality and perpendicularity based on deep belief network is established. To verify the effectiveness of the coaxiality and perpendicularity prediction method proposed in this paper, a multi-stage rotor coaxiality and perpendicularity assembly measurement experiment is carried out.\u0000\u0000\u0000Findings\u0000The results of this paper show that the accuracy rate of face type classification using convolutional neural network is 99%, which meets the requirements of subsequent assembly process. For the 80 sets of test samples, the average errors of the coaxiality and perpendicularity of the deep belief network prediction method are 0.1 and 1.6 µm, respectively.\u0000\u0000\u0000Originality/value\u0000Therefore, the method proposed in this paper can be used not only for rotor surface classification but also to guide the assembly of aero-engine multi-stage rotors.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42732024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Bianchini, A. Ceruti, A. D'Anniballe, J. Rossi, Giorgio Zompi
Purpose In the redesign process of assembly components that need adaptation to robotic assembly, designers can find support from structured methodologies for innovation, such as the theory of inventive problem solving (TRIZ). This paper aims to illustrate the authors’ methodology for redesigning gas hobs components for adaptation to robotic assembly. Design/methodology/approach A designer approaching a redesign task of an assembly component of any kind for adaptation to robotic assembly must consider, first of all, the features and limitations of existing robotic assembly systems; the generation of new design ideas that best fit the requirements may result to be a very challenging task. Here, the TRIZ methodology has proven useful for generating design ideas and finding the best solution. Findings The authors’ methodology approaches the challenges of redesign tasks for robotic assembly adaptation, which exploits knowledge of automatic and robotic assembly systems and the TRIZ method for innovation; it has proven useful in the redesign, checks and prototyping of gas hobs components. Originality/value This paper shows how the TRIZ methodology can be integrated into the redesign process and its impact on an industrial environment. The work’s main value is to provide a set of steps to help the designers change their design components approach that is necessary but not still implemented to optimize the use of the automation.
{"title":"Inventive redesign for automatic assembly in the household appliances industry","authors":"A. Bianchini, A. Ceruti, A. D'Anniballe, J. Rossi, Giorgio Zompi","doi":"10.1108/aa-01-2022-0010","DOIUrl":"https://doi.org/10.1108/aa-01-2022-0010","url":null,"abstract":"\u0000Purpose\u0000In the redesign process of assembly components that need adaptation to robotic assembly, designers can find support from structured methodologies for innovation, such as the theory of inventive problem solving (TRIZ). This paper aims to illustrate the authors’ methodology for redesigning gas hobs components for adaptation to robotic assembly.\u0000\u0000\u0000Design/methodology/approach\u0000A designer approaching a redesign task of an assembly component of any kind for adaptation to robotic assembly must consider, first of all, the features and limitations of existing robotic assembly systems; the generation of new design ideas that best fit the requirements may result to be a very challenging task. Here, the TRIZ methodology has proven useful for generating design ideas and finding the best solution.\u0000\u0000\u0000Findings\u0000The authors’ methodology approaches the challenges of redesign tasks for robotic assembly adaptation, which exploits knowledge of automatic and robotic assembly systems and the TRIZ method for innovation; it has proven useful in the redesign, checks and prototyping of gas hobs components.\u0000\u0000\u0000Originality/value\u0000This paper shows how the TRIZ methodology can be integrated into the redesign process and its impact on an industrial environment. The work’s main value is to provide a set of steps to help the designers change their design components approach that is necessary but not still implemented to optimize the use of the automation.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49491025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose Steam turbine final assembly is a dynamic process, in which various interference events occur frequently. Currently, data transmission relies on oral presentation, while scheduling depends on the manual experience of managers. This mode has low information transmission efficiency and is difficult to timely respond to emergencies. Besides, it is difficult to consider various factors when manually adjusting the plan, which reduces assembly efficiency. The purpose of this paper is to propose a knowledge-based real-time scheduling system under cyber-physical system (CPS) environment which can improve the assembly efficiency of steam turbines. Design/methodology/approach First, an Internet of Things based CPS framework is proposed to achieve real-time monitoring of turbine assembly and improve the efficiency of information transmission. Second, a knowledge-based real-time scheduling system consisting of three modules is designed to replace manual experience for steam turbine assembly scheduling. Findings Experiments show that the scheduling results of the knowledge-based scheduling system outperform heuristic algorithms based on priority rules. Compared with manual scheduling, the delay time is reduced by 43.9%. Originality/value A knowledge-based real-time scheduling system under CPS environment is proposed to improve the assembly efficiency of steam turbines. This paper provides a reference paradigm for the application of the knowledge-based system and CPS in the assembly control of labor-intensive engineering-to-order products.
{"title":"A knowledge-based real-time scheduling system for steam turbine assembly under CPS environment","authors":"Teng Wang, Xiaofeng Hu, Yahui Zhang","doi":"10.1108/aa-04-2022-0111","DOIUrl":"https://doi.org/10.1108/aa-04-2022-0111","url":null,"abstract":"\u0000Purpose\u0000Steam turbine final assembly is a dynamic process, in which various interference events occur frequently. Currently, data transmission relies on oral presentation, while scheduling depends on the manual experience of managers. This mode has low information transmission efficiency and is difficult to timely respond to emergencies. Besides, it is difficult to consider various factors when manually adjusting the plan, which reduces assembly efficiency. The purpose of this paper is to propose a knowledge-based real-time scheduling system under cyber-physical system (CPS) environment which can improve the assembly efficiency of steam turbines.\u0000\u0000\u0000Design/methodology/approach\u0000First, an Internet of Things based CPS framework is proposed to achieve real-time monitoring of turbine assembly and improve the efficiency of information transmission. Second, a knowledge-based real-time scheduling system consisting of three modules is designed to replace manual experience for steam turbine assembly scheduling.\u0000\u0000\u0000Findings\u0000Experiments show that the scheduling results of the knowledge-based scheduling system outperform heuristic algorithms based on priority rules. Compared with manual scheduling, the delay time is reduced by 43.9%.\u0000\u0000\u0000Originality/value\u0000A knowledge-based real-time scheduling system under CPS environment is proposed to improve the assembly efficiency of steam turbines. This paper provides a reference paradigm for the application of the knowledge-based system and CPS in the assembly control of labor-intensive engineering-to-order products.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41815581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose When a robot comanipulates an object with a human user, damping is a useful function. This is achieved by programming the robot to exhibit a viscous field. For some specific applications, the viscosity is required to change according to the manipulation velocity. A reported method is programming the viscosity varying inversely to the velocity. In this paper, this method is experimentally shown to distort human’s natural motion performance. This paper aims to propose a solution to solve this instability problem. Design/methodology/approach The authors performed a point-to-point targeting movement, where it was observed that the instability results from a sudden reduction of robot’s resistance to motion, which further results from the abrupt viscosity drop when the subject tries to accelerate. Therefore, the authors propose a solution where a first-order linear filter is added to the viscosity coefficient so as to slow down its variation. Findings The experimental results confirm that the proposition is effective, with the ability to stabilize the comanipulated dynamics and to restore the human’s natural behavior. Originality/value This paper concerns applications of comanipulation where the viscosity coefficient is designed to decrease as the velocity increases. An instability problem, which was of vital importance in terms of safety and performance but unreported in the literature, was experimentally studied through human–robot experiments. A solution was proposed by including a secondary dynamics in the variations of the viscosity. Its effectiveness was supported by the practical point-to-point motion experiments.
{"title":"Smoothly adapting viscosity to velocity during comanipulation pointing movements","authors":"Lin Dong, Florian Richer, A. Roby-Brami, G. Morel","doi":"10.1108/aa-05-2022-0142","DOIUrl":"https://doi.org/10.1108/aa-05-2022-0142","url":null,"abstract":"\u0000Purpose\u0000When a robot comanipulates an object with a human user, damping is a useful function. This is achieved by programming the robot to exhibit a viscous field. For some specific applications, the viscosity is required to change according to the manipulation velocity. A reported method is programming the viscosity varying inversely to the velocity. In this paper, this method is experimentally shown to distort human’s natural motion performance. This paper aims to propose a solution to solve this instability problem.\u0000\u0000\u0000Design/methodology/approach\u0000The authors performed a point-to-point targeting movement, where it was observed that the instability results from a sudden reduction of robot’s resistance to motion, which further results from the abrupt viscosity drop when the subject tries to accelerate. Therefore, the authors propose a solution where a first-order linear filter is added to the viscosity coefficient so as to slow down its variation.\u0000\u0000\u0000Findings\u0000The experimental results confirm that the proposition is effective, with the ability to stabilize the comanipulated dynamics and to restore the human’s natural behavior.\u0000\u0000\u0000Originality/value\u0000This paper concerns applications of comanipulation where the viscosity coefficient is designed to decrease as the velocity increases. An instability problem, which was of vital importance in terms of safety and performance but unreported in the literature, was experimentally studied through human–robot experiments. A solution was proposed by including a secondary dynamics in the variations of the viscosity. Its effectiveness was supported by the practical point-to-point motion experiments.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45661320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose This study aims to promote the anti-disturbance and tracking accuracy of optoelectronic stabilized platforms, which ensure that optical detectors accurately track targets and acquire high-quality images. Design/methodology/approach An improved active disturbance rejection control (ADRC) strategy based on model-assisted double extended state observers (MDESOs) is proposed in this paper. First, by establishing an auxiliary model, the total disturbances are separated into two parts: inner and external disturbances. Then, MDESOs are designed to estimate the two parts by separately using two parallel ESOs, by which the controlled plant is adjusted to the ideal pure integral series. Simultaneously, combined with the nonlinear state error feedback, an overall control strategy is established. Findings Compared with the conventional ADRC and proportional derivative, the improved ADRC (IADRC) has stronger robustness and adaptability and effectively reduces the requirements for model accuracy and the gain of the ESO. The error of the auxiliary model is tolerated to exceed 50%, and the parameter values of the MDESOs are reduced by 90%. Originality/value The total disturbance rejection rate of the proposed strategy is only 3.11% under multiple disturbances, which indicates that the IADRC strategy significantly promotes anti-disturbance performance.
{"title":"Active disturbance rejection control for optoelectronic stabilized platform based on model-assisted double extended state observers","authors":"Peng Gao, Xiuqin Su, Wenbo Zhang","doi":"10.1108/aa-01-2022-0018","DOIUrl":"https://doi.org/10.1108/aa-01-2022-0018","url":null,"abstract":"\u0000Purpose\u0000This study aims to promote the anti-disturbance and tracking accuracy of optoelectronic stabilized platforms, which ensure that optical detectors accurately track targets and acquire high-quality images.\u0000\u0000\u0000Design/methodology/approach\u0000An improved active disturbance rejection control (ADRC) strategy based on model-assisted double extended state observers (MDESOs) is proposed in this paper. First, by establishing an auxiliary model, the total disturbances are separated into two parts: inner and external disturbances. Then, MDESOs are designed to estimate the two parts by separately using two parallel ESOs, by which the controlled plant is adjusted to the ideal pure integral series. Simultaneously, combined with the nonlinear state error feedback, an overall control strategy is established.\u0000\u0000\u0000Findings\u0000Compared with the conventional ADRC and proportional derivative, the improved ADRC (IADRC) has stronger robustness and adaptability and effectively reduces the requirements for model accuracy and the gain of the ESO. The error of the auxiliary model is tolerated to exceed 50%, and the parameter values of the MDESOs are reduced by 90%.\u0000\u0000\u0000Originality/value\u0000The total disturbance rejection rate of the proposed strategy is only 3.11% under multiple disturbances, which indicates that the IADRC strategy significantly promotes anti-disturbance performance.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44461385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose The tightening operations are one of the most critical operations in automotive assembly lines because of its direct impact on customer safety. This study aims to evaluate the major complexity drivers for manual tightening operations, correlate with real tightening failure data and propose mitigations to improve the complexity. Design/methodology/approach In the first stage, the complexity drivers for manual tightening operations were identified. Then, the relative importance of the risk attributes was defined by using pairwise comparisons questionnaire. Further, failure mode effect analysis–analytic hierarchy process (FMEA–AHP) and AHP ratings methods were applied to 20 manual tightening operations in automotive assembly lines. Finally, the similarities between the revealed results and the real failure rates of a Turkish automotive factory were examined and a sensitivity analysis was conducted. Findings The correlation between the proposed methods and manual tightening failure data was calculated as 83%–86%. On the other hand, the correlation between FMEA–AHP and AHP ratings was found as 92%. Poor ergonomics, operator competency and training, operator concentration-loose attention fatigue, manual mouthing before the tightening operation, frequent task changes, critical tightening sequence, positioning of the part and/or directional assembly were found relatively critical for the selected 20 tightening operations. Originality/value This is a unique study for the evaluation of the attributes for manual tightening complexity in automotive assembly lines. The output of this study can be used to improve manual tightening failures in manual assembly lines and to create low complexity assembly lines in new model launches.
{"title":"Evaluation of failure risks for manual tightening operations in automotive assembly lines","authors":"A. Altinisik, Utku Yildirim, Y. Topcu","doi":"10.1108/aa-05-2022-0120","DOIUrl":"https://doi.org/10.1108/aa-05-2022-0120","url":null,"abstract":"\u0000Purpose\u0000The tightening operations are one of the most critical operations in automotive assembly lines because of its direct impact on customer safety. This study aims to evaluate the major complexity drivers for manual tightening operations, correlate with real tightening failure data and propose mitigations to improve the complexity.\u0000\u0000\u0000Design/methodology/approach\u0000In the first stage, the complexity drivers for manual tightening operations were identified. Then, the relative importance of the risk attributes was defined by using pairwise comparisons questionnaire. Further, failure mode effect analysis–analytic hierarchy process (FMEA–AHP) and AHP ratings methods were applied to 20 manual tightening operations in automotive assembly lines. Finally, the similarities between the revealed results and the real failure rates of a Turkish automotive factory were examined and a sensitivity analysis was conducted.\u0000\u0000\u0000Findings\u0000The correlation between the proposed methods and manual tightening failure data was calculated as 83%–86%. On the other hand, the correlation between FMEA–AHP and AHP ratings was found as 92%. Poor ergonomics, operator competency and training, operator concentration-loose attention fatigue, manual mouthing before the tightening operation, frequent task changes, critical tightening sequence, positioning of the part and/or directional assembly were found relatively critical for the selected 20 tightening operations.\u0000\u0000\u0000Originality/value\u0000This is a unique study for the evaluation of the attributes for manual tightening complexity in automotive assembly lines. The output of this study can be used to improve manual tightening failures in manual assembly lines and to create low complexity assembly lines in new model launches.\u0000","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43309703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.
{"title":"An iterative learning control with learnable band extension for the nanopositioning stage","authors":"Chengsi Huang, Zhi-Heng Yang, Jiedong Li","doi":"10.1108/aa-03-2022-0070","DOIUrl":"https://doi.org/10.1108/aa-03-2022-0070","url":null,"abstract":"Purpose Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.","PeriodicalId":55448,"journal":{"name":"Assembly Automation","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62010248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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