Assembly AutomationTable of Contents for Assembly Automation. List of articles from the current issue, including Just Accepted (EarlyCite)https://www.emerald.com/insight/publication/issn/0144-5154/vol/42/iss/6?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestAssembly AutomationEmerald Publishing LimitedAssembly AutomationAssembly Automationhttps://www.emerald.com/insight/proxy/containerImg?link=/resource/publication/journal/3b98e2dffc6cb06a89dcb0d5c60a0206/urn:emeraldgroup.com:asset:id:binary:aa.cover.jpghttps://www.emerald.com/insight/publication/issn/0144-5154/vol/42/iss/6?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestFusion event-triggered model predictive control based on shrinking prediction horizonhttps://www.emerald.com/insight/content/doi/10.1108/AA-02-2022-0022/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis 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. A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy. 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. 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.Fusion event-triggered model predictive control based on shrinking prediction horizon
Qun Cao, Yuanqing Xia, Zhongqi Sun, Li Dai
Assembly Automation, Vol. 42, No. 6, pp.721-729

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.

A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.

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.

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.

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Fusion event-triggered model predictive control based on shrinking prediction horizon10.1108/AA-02-2022-0022Assembly Automation2022-10-13© 2022 Emerald Publishing LimitedQun CaoYuanqing XiaZhongqi SunLi DaiAssembly Automation4262022-10-1310.1108/AA-02-2022-0022https://www.emerald.com/insight/content/doi/10.1108/AA-02-2022-0022/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Reinforcement learning control for a flapping-wing micro aerial vehicle with output constrainthttps://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0140/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to study the application of reinforcement learning (RL) in the control of an output-constrained flapping-wing micro aerial vehicle (FWMAV) with system uncertainty. A six-degrees-of-freedom hummingbird model is used without consideration of the inertial effects of the wings. A RL algorithm based on actor–critic framework is applied, which consists of an actor network with unknown policy gradient and a critic network with unknown value function. Considering the good performance of neural network (NN) in fitting nonlinearity and its optimum characteristics, an actor–critic NN optimization algorithm is designed, in which the actor and critic NNs are used to generate a policy and approximate the cost functions, respectively. In addition, to ensure the safe and stable flight of the FWMAV, a barrier Lyapunov function is used to make the flight states constrained in predefined regions. Based on the Lyapunov stability theory, the stability of the system is analyzed, and finally, the feasibility of RL in the control of a FWMAV is verified through simulation. The proposed RL control scheme works well in ensuring the trajectory tracking of the FWMAV in the presence of output constraint and system uncertainty. A novel RL algorithm based on actor–critic framework is applied to the control of a FWMAV with system uncertainty. For the stable and safe flight of the FWMAV, the output constraint problem is considered and solved by barrier Lyapunov function-based control.Reinforcement learning control for a flapping-wing micro aerial vehicle with output constraint
Haifeng Huang, Xiaoyang Wu, Tingting Wang, Yongbin Sun, Qiang Fu
Assembly Automation, Vol. 42, No. 6, pp.730-741

This paper aims to study the application of reinforcement learning (RL) in the control of an output-constrained flapping-wing micro aerial vehicle (FWMAV) with system uncertainty.

A six-degrees-of-freedom hummingbird model is used without consideration of the inertial effects of the wings. A RL algorithm based on actor–critic framework is applied, which consists of an actor network with unknown policy gradient and a critic network with unknown value function. Considering the good performance of neural network (NN) in fitting nonlinearity and its optimum characteristics, an actor–critic NN optimization algorithm is designed, in which the actor and critic NNs are used to generate a policy and approximate the cost functions, respectively. In addition, to ensure the safe and stable flight of the FWMAV, a barrier Lyapunov function is used to make the flight states constrained in predefined regions. Based on the Lyapunov stability theory, the stability of the system is analyzed, and finally, the feasibility of RL in the control of a FWMAV is verified through simulation.

The proposed RL control scheme works well in ensuring the trajectory tracking of the FWMAV in the presence of output constraint and system uncertainty.

A novel RL algorithm based on actor–critic framework is applied to the control of a FWMAV with system uncertainty. For the stable and safe flight of the FWMAV, the output constraint problem is considered and solved by barrier Lyapunov function-based control.

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Reinforcement learning control for a flapping-wing micro aerial vehicle with output constraint10.1108/AA-05-2022-0140Assembly Automation2022-10-27© 2022 Emerald Publishing LimitedHaifeng HuangXiaoyang WuTingting WangYongbin SunQiang FuAssembly Automation4262022-10-2710.1108/AA-05-2022-0140https://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0140/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
A detailed review and analysis of assembly line rebalancing problemshttps://www.emerald.com/insight/content/doi/10.1108/AA-02-2022-0031/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestVarious approaches and algorithms have been proposed since the 1950s to solve the assembly line (AL) balancing problem. These methods have established an AL configuration from the beginning. However, a prebalanced AL may have to be rebalanced in real life for many reasons, such as changes in the cycle time, production demand, product features or task operation times. This problem has increasingly attracted the interest of scientists in recent years. This study aims to offer a detailed review of the assembly line rebalancing problems (ALRBPs) to provide a better insight into the theoretical and practical applications of ALRBPs. A structured database search was conducted, and 41 ALRBP papers published between 2005 and 2022 were classified based on the problem structure, objective functions, problem constraints, reasons for rebalancing, solution approaches and type of data used for solution evaluation. Finally, future research directions were identified and recommended. Single model, straight lines with deterministic task times were the most studied type of the ALRBPs. Eighteen percent of the studies solved worker assignment problems together with ALRBP. Product demand and cycle time changes were the leading causes of the rebalancing need. Furthermore, seven future research opportunities were suggested. Although there are many review studies on AL balancing problems, to the best of the authors’ knowledge, there have been no attempts to review the studies on ALRBPs.A detailed review and analysis of assembly line rebalancing problems
Tolga Çimen, Adil Baykasoğlu, Sebnem Demirkol Akyol
Assembly Automation, Vol. 42, No. 6, pp.742-760

Various approaches and algorithms have been proposed since the 1950s to solve the assembly line (AL) balancing problem. These methods have established an AL configuration from the beginning. However, a prebalanced AL may have to be rebalanced in real life for many reasons, such as changes in the cycle time, production demand, product features or task operation times. This problem has increasingly attracted the interest of scientists in recent years. This study aims to offer a detailed review of the assembly line rebalancing problems (ALRBPs) to provide a better insight into the theoretical and practical applications of ALRBPs.

A structured database search was conducted, and 41 ALRBP papers published between 2005 and 2022 were classified based on the problem structure, objective functions, problem constraints, reasons for rebalancing, solution approaches and type of data used for solution evaluation. Finally, future research directions were identified and recommended.

Single model, straight lines with deterministic task times were the most studied type of the ALRBPs. Eighteen percent of the studies solved worker assignment problems together with ALRBP. Product demand and cycle time changes were the leading causes of the rebalancing need. Furthermore, seven future research opportunities were suggested.

Although there are many review studies on AL balancing problems, to the best of the authors’ knowledge, there have been no attempts to review the studies on ALRBPs.

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A detailed review and analysis of assembly line rebalancing problems10.1108/AA-02-2022-0031Assembly Automation2022-10-21© 2022 Emerald Publishing LimitedTolga ÇimenAdil BaykasoğluSebnem Demirkol AkyolAssembly Automation4262022-10-2110.1108/AA-02-2022-0031https://www.emerald.com/insight/content/doi/10.1108/AA-02-2022-0031/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Coaxiality and perpendicularity prediction of saddle surface rotor based on deep belief networkshttps://www.emerald.com/insight/content/doi/10.1108/AA-06-2022-0163/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestAiming 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. 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. 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. 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.Coaxiality and perpendicularity prediction of saddle surface rotor based on deep belief networks
Chuanzhi Sun, Yin Chu Wang, Qing Lu, Yongmeng Liu, Jiubin Tan
Assembly Automation, Vol. 42, No. 6, pp.761-772

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.

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.

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.

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.

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Coaxiality and perpendicularity prediction of saddle surface rotor based on deep belief networks10.1108/AA-06-2022-0163Assembly Automation2022-10-11© 2022 Emerald Publishing LimitedChuanzhi SunYin Chu WangQing LuYongmeng LiuJiubin TanAssembly Automation4262022-10-1110.1108/AA-06-2022-0163https://www.emerald.com/insight/content/doi/10.1108/AA-06-2022-0163/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN modelhttps://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0179/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestCoil 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. 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. 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. 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.Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN model
Fengwei Jing, Mengyang Zhang, Jie Li, Guozheng Xu, Jing Wang
Assembly Automation, Vol. 42, No. 6, pp.773-781

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.

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.

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.

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.

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Coil shape defects prediction algorithm for hot strip rolling based on Siamese semi-supervised DAE-CNN model10.1108/AA-07-2022-0179Assembly Automation2022-10-17© 2022 Emerald Publishing LimitedFengwei JingMengyang ZhangJie LiGuozheng XuJing WangAssembly Automation4262022-10-1710.1108/AA-07-2022-0179https://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0179/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Associated tolerance optimization approach using manufacturing difficulty coefficients and genetic algorithmhttps://www.emerald.com/insight/content/doi/10.1108/AA-02-2022-0024/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe 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. 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. 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. 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.Associated tolerance optimization approach using manufacturing difficulty coefficients and genetic algorithm
Maroua Ghali, Sami Elghali, Nizar Aifaoui
Assembly Automation, Vol. 42, No. 6, pp.782-795

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.

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.

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.

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.

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Associated tolerance optimization approach using manufacturing difficulty coefficients and genetic algorithm10.1108/AA-02-2022-0024Assembly Automation2022-10-19© 2022 Emerald Publishing LimitedMaroua GhaliSami ElghaliNizar AifaouiAssembly Automation4262022-10-1910.1108/AA-02-2022-0024https://www.emerald.com/insight/content/doi/10.1108/AA-02-2022-0024/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
An industrial heterogeneous data based quality management KPI visualization system for product quality controlhttps://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0139/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestQuality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality. Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process. Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system. To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.An industrial heterogeneous data based quality management KPI visualization system for product quality control
Ruihan Zhao, Liang Luo, Pengzhong Li, Jinguang Wang
Assembly Automation, Vol. 42, No. 6, pp.796-808

Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality.

Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process.

Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system.

To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.

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An industrial heterogeneous data based quality management KPI visualization system for product quality control10.1108/AA-05-2022-0139Assembly Automation2022-11-09© 2022 Emerald Publishing LimitedRuihan ZhaoLiang LuoPengzhong LiJinguang WangAssembly Automation4262022-11-0910.1108/AA-05-2022-0139https://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0139/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Geometric modeling analysis method for stacking assembly deviation of aero engine rotorhttps://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0130/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestAssembly errors of aeroengine rotor must be controlled to improve the aeroengine efficiency. However, current method cannot truly reflect assembly errors of the rotor in working state owing to difficulties in error analysis. Therefore, the purpose of this study is to establish an optimization method for aeroengine rotor stacking assembly. The assembly structure of aeroengine rotor is featured. Rotor eccentricity is optimized based on Jacobian–Torsor model. Then, an optimization method for assembly work is proposed. The assembly process of the high-pressure compressor rotor and the high-pressure turbine rotor as the rotor core assembly is mainly considered. An aeroengine rotor is assembled to verify the method. The results show that the predicted eccentricity differed from the measured eccentricity by 6.1%, with a comprehensive error of 8.1%. Thus, the optimization method has certain significance for rotor assembly error analysis and assembly process optimization. In view of the error analysis in the stacking assembly of aeroengine rotor, an innovative optimization method is proposed. The method provides a novel approach for the aeroengine rotor assembly optimization and is applicable for the assembly of high-pressure compressor rotor and high-pressure turbine rotor as the rotor core assembly.Geometric modeling analysis method for stacking assembly deviation of aero engine rotor
Zesheng Wang, Dongbo Wu, Hui Wang, Jiawei Liang, Jingguang Peng
Assembly Automation, Vol. 42, No. 6, pp.809-816

Assembly errors of aeroengine rotor must be controlled to improve the aeroengine efficiency. However, current method cannot truly reflect assembly errors of the rotor in working state owing to difficulties in error analysis. Therefore, the purpose of this study is to establish an optimization method for aeroengine rotor stacking assembly.

The assembly structure of aeroengine rotor is featured. Rotor eccentricity is optimized based on Jacobian–Torsor model. Then, an optimization method for assembly work is proposed. The assembly process of the high-pressure compressor rotor and the high-pressure turbine rotor as the rotor core assembly is mainly considered.

An aeroengine rotor is assembled to verify the method. The results show that the predicted eccentricity differed from the measured eccentricity by 6.1%, with a comprehensive error of 8.1%. Thus, the optimization method has certain significance for rotor assembly error analysis and assembly process optimization.

In view of the error analysis in the stacking assembly of aeroengine rotor, an innovative optimization method is proposed. The method provides a novel approach for the aeroengine rotor assembly optimization and is applicable for the assembly of high-pressure compressor rotor and high-pressure turbine rotor as the rotor core assembly.

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Geometric modeling analysis method for stacking assembly deviation of aero engine rotor10.1108/AA-05-2022-0130Assembly Automation2022-11-08© 2022 Emerald Publishing LimitedZesheng WangDongbo WuHui WangJiawei LiangJingguang PengAssembly Automation4262022-11-0810.1108/AA-05-2022-0130https://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0130/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Tolerance analysis of planar parts with skin modeling considering spatial distribution characteristics of surface morphology and local surface deformationshttps://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0198/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process. First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach. Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes. The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.Tolerance analysis of planar parts with skin modeling considering spatial distribution characteristics of surface morphology and local surface deformations
Tuan-Hui Shen, Cong Lu
Assembly Automation, Vol. 42, No. 6, pp.817-834

This paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process.

First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach.

Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes.

The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.

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Tolerance analysis of planar parts with skin modeling considering spatial distribution characteristics of surface morphology and local surface deformations10.1108/AA-07-2022-0198Assembly Automation2022-11-10© 2022 Emerald Publishing LimitedTuan-Hui ShenCong LuAssembly Automation4262022-11-1010.1108/AA-07-2022-0198https://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0198/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Automatic tolerance analyses by generation of assembly graph and mating edges from STEP AP 242 file of mechanical assemblyhttps://www.emerald.com/insight/content/doi/10.1108/AA-11-2021-0155/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to propose a method to automate the tolerance analyses of mechanical assembly using STandard for the Exchange of Product model data-Application Protocol Part 242 (STEP AP 242) files derived from the 3-D computer-aided design (CAD) models. Product manufacturing information and mating information available in ISO 10303 STEP AP242 files resulting from the 3-D CAD model of mechanical assembly are extracted. The extracted geometric attributes, geometric dimensioning and tolerancing (GD&T) and mating information are used to automatically generate assembly graph and mating edges required for the tolerance analyses of the mechanical assembly by using the matrix approach. The feasibility of the proposed method is verified through two mechanical assembly case studies. The results of manual calculations and tolerance values computed by the automated method are very closely matching. Tolerance analysis is an integral part of product development that directly influences the cost and performance of a product. Apart from the academic interest, the work is expected to have positive implications for the digital design and smart manufacturing industry that involve in the development of solutions for automation of design and manufacturing system functions. The approach presented in the paper that aids the automation of tolerance analyses of mechanical assembly is an innovative application of the STEP AP 242 file. The automation of tolerance analyses would improve the productivity and efficiency of the product realization process.Automatic tolerance analyses by generation of assembly graph and mating edges from STEP AP 242 file of mechanical assembly
Mukunthan S., Manu R., Deepak Lawrence K.
Assembly Automation, Vol. 42, No. 6, pp.835-850

This paper aims to propose a method to automate the tolerance analyses of mechanical assembly using STandard for the Exchange of Product model data-Application Protocol Part 242 (STEP AP 242) files derived from the 3-D computer-aided design (CAD) models.

Product manufacturing information and mating information available in ISO 10303 STEP AP242 files resulting from the 3-D CAD model of mechanical assembly are extracted. The extracted geometric attributes, geometric dimensioning and tolerancing (GD&T) and mating information are used to automatically generate assembly graph and mating edges required for the tolerance analyses of the mechanical assembly by using the matrix approach.

The feasibility of the proposed method is verified through two mechanical assembly case studies. The results of manual calculations and tolerance values computed by the automated method are very closely matching.

Tolerance analysis is an integral part of product development that directly influences the cost and performance of a product. Apart from the academic interest, the work is expected to have positive implications for the digital design and smart manufacturing industry that involve in the development of solutions for automation of design and manufacturing system functions.

The approach presented in the paper that aids the automation of tolerance analyses of mechanical assembly is an innovative application of the STEP AP 242 file. The automation of tolerance analyses would improve the productivity and efficiency of the product realization process.

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Automatic tolerance analyses by generation of assembly graph and mating edges from STEP AP 242 file of mechanical assembly10.1108/AA-11-2021-0155Assembly Automation2022-11-17© 2022 Emerald Publishing LimitedMukunthan S.Manu R.Deepak Lawrence K.Assembly Automation4262022-11-1710.1108/AA-11-2021-0155https://www.emerald.com/insight/content/doi/10.1108/AA-11-2021-0155/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
The application of robotics and artificial intelligence in embroidery: challenges and benefitshttps://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0183/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestEmbroidery as a textile embellishment technique plays an important role in people's daily life. Esthetic embroidery artworks possess cultural values. With the development of robotics and artificial intelligence (AI), these technologies have been studied and applied in the embroidery process. This study aims to survey how these technologies facilitate embroidery from different aspects. This paper surveys how the technologies of robotics and AI are applied in the embroidery field. The applications are mainly reviewed from three aspects: computerized robotic embroidery systems has been widely used for the mass production of embroidered textiles, the advanced technological systems and techniques have greatly facilitated the development of smart textiles and the artificial intelligence plays an important role in the inheritance, innovation and protection of traditional handicraft artwork of embroidery. The programmable robotic embroidery machines have greatly improved the production efficiency of embroidered textiles and promoted the development of electronic textiles. The AI, mainly the deep learning technology, brings significant benefits to esthetic embroidery creation. Technology-based embroidery has become a hot research topic in the field of textiles. This paper summarizes the application of robotics and AI technologies in the field of embroidery, which provides readers a comprehensive and systematic understanding about the research progress of modern technology-oriented embroidery. This helps readers gain inspiration from the technology perspectives.The application of robotics and artificial intelligence in embroidery: challenges and benefits
Ling Chen, Zhi Su, Xiaotong He, Xiang Chen, Lin Dong
Assembly Automation, Vol. 42, No. 6, pp.851-868

Embroidery as a textile embellishment technique plays an important role in people's daily life. Esthetic embroidery artworks possess cultural values. With the development of robotics and artificial intelligence (AI), these technologies have been studied and applied in the embroidery process. This study aims to survey how these technologies facilitate embroidery from different aspects.

This paper surveys how the technologies of robotics and AI are applied in the embroidery field. The applications are mainly reviewed from three aspects: computerized robotic embroidery systems has been widely used for the mass production of embroidered textiles, the advanced technological systems and techniques have greatly facilitated the development of smart textiles and the artificial intelligence plays an important role in the inheritance, innovation and protection of traditional handicraft artwork of embroidery.

The programmable robotic embroidery machines have greatly improved the production efficiency of embroidered textiles and promoted the development of electronic textiles. The AI, mainly the deep learning technology, brings significant benefits to esthetic embroidery creation. Technology-based embroidery has become a hot research topic in the field of textiles.

This paper summarizes the application of robotics and AI technologies in the field of embroidery, which provides readers a comprehensive and systematic understanding about the research progress of modern technology-oriented embroidery. This helps readers gain inspiration from the technology perspectives.

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The application of robotics and artificial intelligence in embroidery: challenges and benefits10.1108/AA-07-2022-0183Assembly Automation2022-11-25© 2022 Emerald Publishing LimitedLing ChenZhi SuXiaotong HeXiang ChenLin DongAssembly Automation4262022-11-2510.1108/AA-07-2022-0183https://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0183/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantizationhttps://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0126/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization. By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example. Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed. The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization
Zhongwen Cao, Liang Zhang, Adil M. Ahmad, Fawaz E. Alsaadi, Madini O. Alassafi
Assembly Automation, Vol. 42, No. 6, pp.869-880

This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization.

By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example.

Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed.

The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.

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Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization10.1108/AA-05-2022-0126Assembly Automation2022-11-25© 2022 Emerald Publishing LimitedZhongwen CaoLiang ZhangAdil M. AhmadFawaz E. AlsaadiMadini O. AlassafiAssembly Automation4262022-11-2510.1108/AA-05-2022-0126https://www.emerald.com/insight/content/doi/10.1108/AA-05-2022-0126/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
Online modeling of environmental constraint region for complex-shaped parts assemblyhttps://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0180/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe paper aims to propose a method to build environmental constraint region online in complex-shaped peg-in-hole assembly tasks. Compared with conventional way which using computer-aided design (CAD) models of assembly parts to construct the environmental constraint region offline, the paper provides an online approach that consists of three aspects: modeling assembly parts through visual recognition, decomposing complex shapes into multiple primitive convex shapes and a numerical algorithm to simulate the peg-in-hole insertion contact. Besides, a contrast experiment is performed to validate the feasibility and effectiveness of the method. The experiment result indicates that online construction takes less time than the offline way under the same task conditions. Furthermore, due to the CAD models of the parts are not required to be known, the method proposed in the paper has a broader application in most assembly scenarios. With the improvement of customization and complexity of manufactured parts, the assembly of complex-shaped parts has drawn greater attention of many researchers. The assembly methods based on attractive region in environment (ARIE) have shown great performance to achieve high-precision manipulation with low-precision systems. The construction of environmental constraint region serves as an essential part of ARIE-based theory, directly affect the formulation and application of assembly strategies.Online modeling of environmental constraint region for complex-shaped parts assembly
Shuai Gan, Yang Liu, Ziyu Chen
Assembly Automation, Vol. 42, No. 6, pp.881-890

The paper aims to propose a method to build environmental constraint region online in complex-shaped peg-in-hole assembly tasks.

Compared with conventional way which using computer-aided design (CAD) models of assembly parts to construct the environmental constraint region offline, the paper provides an online approach that consists of three aspects: modeling assembly parts through visual recognition, decomposing complex shapes into multiple primitive convex shapes and a numerical algorithm to simulate the peg-in-hole insertion contact. Besides, a contrast experiment is performed to validate the feasibility and effectiveness of the method.

The experiment result indicates that online construction takes less time than the offline way under the same task conditions. Furthermore, due to the CAD models of the parts are not required to be known, the method proposed in the paper has a broader application in most assembly scenarios.

With the improvement of customization and complexity of manufactured parts, the assembly of complex-shaped parts has drawn greater attention of many researchers. The assembly methods based on attractive region in environment (ARIE) have shown great performance to achieve high-precision manipulation with low-precision systems. The construction of environmental constraint region serves as an essential part of ARIE-based theory, directly affect the formulation and application of assembly strategies.

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Online modeling of environmental constraint region for complex-shaped parts assembly10.1108/AA-07-2022-0180Assembly Automation2022-11-25© 2022 Emerald Publishing LimitedShuai GanYang LiuZiyu ChenAssembly Automation4262022-11-2510.1108/AA-07-2022-0180https://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0180/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited
The welding tracking technology of an underwater welding robot based on sliding mode active disturbance rejection controlhttps://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0171/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestA welding robot is a complicated system with uncertainty, time-varying, strong coupling and non-linear system. It is more complicated if it is used in an underwater environment. It is difficult to establish an accurate dynamic model for an underwater welding robot. Aiming at the tracking control of an underwater welding robot, it is difficult to achieve the control performance requirements by the classical proportional integral derivative control method to realize automatic tracking of the seam. The purpose of this paper is to suggest a novel method to deal with these issues. To combine the advantages of active disturbance rejection control (ADRC) and sliding mode control (SMC) to improve the shortcomings of a single control method, a hybrid control method for an underwater welding robot trajectory tracking based on SMC_ADRC is proposed in this research work. The simulation experiment of the proposed approach is carried out by Matlab/Simulink, and the welding experiment is recorded. The seam gets plumper and smoother, with better continuity and no undercut phenomenon. The proposed approach is effective and reliable, and the system’s tracking performance is stable, which can effectively reduce chattering and improve system robustness.The welding tracking technology of an underwater welding robot based on sliding mode active disturbance rejection control
Shengqian Li, Xiaofan Zhang
Assembly Automation, Vol. 42, No. 6, pp.891-900

A welding robot is a complicated system with uncertainty, time-varying, strong coupling and non-linear system. It is more complicated if it is used in an underwater environment. It is difficult to establish an accurate dynamic model for an underwater welding robot. Aiming at the tracking control of an underwater welding robot, it is difficult to achieve the control performance requirements by the classical proportional integral derivative control method to realize automatic tracking of the seam. The purpose of this paper is to suggest a novel method to deal with these issues.

To combine the advantages of active disturbance rejection control (ADRC) and sliding mode control (SMC) to improve the shortcomings of a single control method, a hybrid control method for an underwater welding robot trajectory tracking based on SMC_ADRC is proposed in this research work.

The simulation experiment of the proposed approach is carried out by Matlab/Simulink, and the welding experiment is recorded. The seam gets plumper and smoother, with better continuity and no undercut phenomenon.

The proposed approach is effective and reliable, and the system’s tracking performance is stable, which can effectively reduce chattering and improve system robustness.

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The welding tracking technology of an underwater welding robot based on sliding mode active disturbance rejection control10.1108/AA-07-2022-0171Assembly Automation2022-11-30© 2022 Emerald Publishing LimitedShengqian LiXiaofan ZhangAssembly Automation4262022-11-3010.1108/AA-07-2022-0171https://www.emerald.com/insight/content/doi/10.1108/AA-07-2022-0171/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2022 Emerald Publishing Limited