Pub Date : 2024-09-17DOI: 10.1177/09544062241271744
Ruijun Liang, Junwei Wang, Yang Li, Wenhua Ye, Sheng Leng
Most working scenes of industrial robots are static scenes and from a previous static scene to a current static scene is a multi-scene. Finding optimal paths with limited time is difficult for motion planning in a high-dimensional space or in multiple scenes. The low efficiency of motion planning of rapidly-exploring random tree star in high-dimensional spaces, low adaptability of global replanning to multiple scenes are addressed by proposing the local replanning based on goal dynamically-guiding rapidly-exploring random tree star (LR-GD-RRT*). The algorithm contributes to fast path tree exploration and multi-scene motion planning. For path tree exploration, sampling points heuristically generating and new nodes growth by goal dynamically-guiding are proposed to reduce the blind and ineffective searches. Moreover, dynamic adjustment of size of new node neighborhood according to density of the obstacles is proposed to search for more neighbor nodes to optimize the path and also to reduce ineffective computation to improve efficiency. For multi-scene, three steps of trimming, re-exploration, and reconnection for local replanning are proposed. For path tree exploration, simulations in 2D plane, 3D space, and the manipulator show that GD-RRT* improves convergence speed, shortens path length and search time, compared with RRT*. For multi-scene, simulations in 3D space and with the manipulator show that the local replanning of the current scene has both lower path cost and higher planning efficiency compared with the global replanning of the previous scene. Motion of the six-degree-of-freedom robot end in a real scene also verifies the effectiveness of the LR-GD-RRT*.
{"title":"Rapid motion planning of manipulator in three-dimensional space under multiple scenes","authors":"Ruijun Liang, Junwei Wang, Yang Li, Wenhua Ye, Sheng Leng","doi":"10.1177/09544062241271744","DOIUrl":"https://doi.org/10.1177/09544062241271744","url":null,"abstract":"Most working scenes of industrial robots are static scenes and from a previous static scene to a current static scene is a multi-scene. Finding optimal paths with limited time is difficult for motion planning in a high-dimensional space or in multiple scenes. The low efficiency of motion planning of rapidly-exploring random tree star in high-dimensional spaces, low adaptability of global replanning to multiple scenes are addressed by proposing the local replanning based on goal dynamically-guiding rapidly-exploring random tree star (LR-GD-RRT*). The algorithm contributes to fast path tree exploration and multi-scene motion planning. For path tree exploration, sampling points heuristically generating and new nodes growth by goal dynamically-guiding are proposed to reduce the blind and ineffective searches. Moreover, dynamic adjustment of size of new node neighborhood according to density of the obstacles is proposed to search for more neighbor nodes to optimize the path and also to reduce ineffective computation to improve efficiency. For multi-scene, three steps of trimming, re-exploration, and reconnection for local replanning are proposed. For path tree exploration, simulations in 2D plane, 3D space, and the manipulator show that GD-RRT* improves convergence speed, shortens path length and search time, compared with RRT*. For multi-scene, simulations in 3D space and with the manipulator show that the local replanning of the current scene has both lower path cost and higher planning efficiency compared with the global replanning of the previous scene. Motion of the six-degree-of-freedom robot end in a real scene also verifies the effectiveness of the LR-GD-RRT*.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"102 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/09544062241277315
Gang Wang, Jiankang Guo, Yimeng Yao, Qihui Zhang, Fei Yang, Honghao Yue, Kun Wang
In this paper, the dynamic interaction between the locking system and the space manipulator is analyzed, and a misalignment tolerance capability analysis method for the distributed locking system of the space manipulator is proposed. Based on the structure and layout characteristics of the locking mechanism, the collision dynamics analysis between the manipulator and the locking system during the locking process is carried out, and the factors for the locking failure caused by the escape of the locking frame on the passive end of the locking mechanism are obtained. Aiming at the factors of escape of the locking frame and improving the misalignment tolerance of the locking system, the locking action matching of the active end of the locking system is carried out, which lays a foundation for the design of a collaborative locking strategy. The collaborative locking strategy model of the locking system was established to match the locking speed of the hook in the locking stage. Using the proposed misalignment tolerance capability analysis method combined with simulation software to simulate the conventional locking strategy and the collaborative locking strategy. By comparing the simulation results, the large misalignment tolerance capability of the collaborative locking strategy is verified.
{"title":"Research on collaborative locking strategy for distributed locking system of space manipulator","authors":"Gang Wang, Jiankang Guo, Yimeng Yao, Qihui Zhang, Fei Yang, Honghao Yue, Kun Wang","doi":"10.1177/09544062241277315","DOIUrl":"https://doi.org/10.1177/09544062241277315","url":null,"abstract":"In this paper, the dynamic interaction between the locking system and the space manipulator is analyzed, and a misalignment tolerance capability analysis method for the distributed locking system of the space manipulator is proposed. Based on the structure and layout characteristics of the locking mechanism, the collision dynamics analysis between the manipulator and the locking system during the locking process is carried out, and the factors for the locking failure caused by the escape of the locking frame on the passive end of the locking mechanism are obtained. Aiming at the factors of escape of the locking frame and improving the misalignment tolerance of the locking system, the locking action matching of the active end of the locking system is carried out, which lays a foundation for the design of a collaborative locking strategy. The collaborative locking strategy model of the locking system was established to match the locking speed of the hook in the locking stage. Using the proposed misalignment tolerance capability analysis method combined with simulation software to simulate the conventional locking strategy and the collaborative locking strategy. By comparing the simulation results, the large misalignment tolerance capability of the collaborative locking strategy is verified.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"16 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/09544062241276347
Saswat Khatai, Ashok Kumar Sahoo, Ramanuj Kumar, Amlana Panda
“Green manufacturing” is often referred as a sustainable manufacturing process where ecofriendly cutting fluids are used to achieve social, economic, and environmental goals, which mitigates the soil, air, and water contamination and enhances operator’s health and safety. In this current study, mineral oil based ZrO2 and GO nano-cutting fluids are utilized for the cooling and lubrication purpose during the machining of hardened AISI D2 steel (55 ± 1 HRC). Machinability characteristics such as tool wear, surface roughness, cutting temperature, power consumption, and the span of the tool has been investigated in this study along with the sustainability aspects such as machining cost, carbon, and noise emissions during the machining process. Machining under GO nano-cutting fluid coupled with dual nozzle assisted MQL outperformed other environments such as ZrO2 NFMQL, pure mineral oil MQL and dry conditions in all aspects. The maximum tool life was achieved under GO NFMQL condition as 136.16 min followed by 94.41, 65.52, 20.58 min under ZrO2 NFMQL, pure oil MQL, and dry conditions. A substantial impact has been noticed on the cost savings under GO-NFMQL condition and cost saving have been reduced by 2.07%, 5.19%, and 5.48%, compared to ZrO2-NFMQL, MQL and dry environments respectively. Further, GO NFMQL environment performed better in both machinability and sustainability aspects than other environments as it gets the highest score of 11 in Pugh matrix sustainability assessment approach.
{"title":"Hard turning performance assessment of AISI D2 steel under dual nozzle MQL assisted ZrO2 and GO nano-cutting fluids: A sustainability approach","authors":"Saswat Khatai, Ashok Kumar Sahoo, Ramanuj Kumar, Amlana Panda","doi":"10.1177/09544062241276347","DOIUrl":"https://doi.org/10.1177/09544062241276347","url":null,"abstract":"“Green manufacturing” is often referred as a sustainable manufacturing process where ecofriendly cutting fluids are used to achieve social, economic, and environmental goals, which mitigates the soil, air, and water contamination and enhances operator’s health and safety. In this current study, mineral oil based ZrO<jats:sub>2</jats:sub> and GO nano-cutting fluids are utilized for the cooling and lubrication purpose during the machining of hardened AISI D2 steel (55 ± 1 HRC). Machinability characteristics such as tool wear, surface roughness, cutting temperature, power consumption, and the span of the tool has been investigated in this study along with the sustainability aspects such as machining cost, carbon, and noise emissions during the machining process. Machining under GO nano-cutting fluid coupled with dual nozzle assisted MQL outperformed other environments such as ZrO2 NFMQL, pure mineral oil MQL and dry conditions in all aspects. The maximum tool life was achieved under GO NFMQL condition as 136.16 min followed by 94.41, 65.52, 20.58 min under ZrO2 NFMQL, pure oil MQL, and dry conditions. A substantial impact has been noticed on the cost savings under GO-NFMQL condition and cost saving have been reduced by 2.07%, 5.19%, and 5.48%, compared to ZrO<jats:sub>2</jats:sub>-NFMQL, MQL and dry environments respectively. Further, GO NFMQL environment performed better in both machinability and sustainability aspects than other environments as it gets the highest score of 11 in Pugh matrix sustainability assessment approach.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"74 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/09544062241275383
Yuhui Liu, Guanglei Wu, Longxiang Li
Flexible manipulators have been widely applied in various tasks involving grasping and manipulation, and their unique characteristics in terms of lightweight design and adaptability make them particularly suitable for tackling complex spatial pick-and-place operations. In this work, a Triglide flexible manipulator is presented and developed, based on the parallel Biglide mechanism and dual leaf springs in parallelogram structure, which features adjustable stiffness by changing the transverse configuration of the leaf springs. To analyze the deflections of the leaf springs efficiently, a segmented approach is deployed, to calculate large deflection within the plane of the leaf-springs parallelogram structure. The adopted approach uses classical bending deformation formulas from mechanics of materials. Consequently, the static models of the manipulator are derived for analyzing bending deformation and workspace by means of numerical calculations. Finite element analysis is adopted to investigate the mechanical characteristics of the manipulator. A prototype of the variable stiffness manipulator is built, and an experimental setup is established for static testing. The obtained results align with the previously observed mechanical characteristics. The main advantages of the flexible robotic manipulator lie in its simple structure, small footprint, and simple kinematic model for control.
{"title":"Design and development of a flexible-rigid Triglide sorting manipulator","authors":"Yuhui Liu, Guanglei Wu, Longxiang Li","doi":"10.1177/09544062241275383","DOIUrl":"https://doi.org/10.1177/09544062241275383","url":null,"abstract":"Flexible manipulators have been widely applied in various tasks involving grasping and manipulation, and their unique characteristics in terms of lightweight design and adaptability make them particularly suitable for tackling complex spatial pick-and-place operations. In this work, a Triglide flexible manipulator is presented and developed, based on the parallel Biglide mechanism and dual leaf springs in parallelogram structure, which features adjustable stiffness by changing the transverse configuration of the leaf springs. To analyze the deflections of the leaf springs efficiently, a segmented approach is deployed, to calculate large deflection within the plane of the leaf-springs parallelogram structure. The adopted approach uses classical bending deformation formulas from mechanics of materials. Consequently, the static models of the manipulator are derived for analyzing bending deformation and workspace by means of numerical calculations. Finite element analysis is adopted to investigate the mechanical characteristics of the manipulator. A prototype of the variable stiffness manipulator is built, and an experimental setup is established for static testing. The obtained results align with the previously observed mechanical characteristics. The main advantages of the flexible robotic manipulator lie in its simple structure, small footprint, and simple kinematic model for control.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"52 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1177/09544062241277727
Rajasekar Vignesh, David Ray George, Bragadeshwaran Ashok, Vijayakumar Thulasi, Dariusz Szpica
The focus of this research is to develop the resilient powertrain to combat the enforcement of stringent emission regulations and elevate its feasibility to power with the sustainable fuels to support the fuel crisis. The current solution in the reliable and adaptability point of view is the low temperature combustion engine-based hybrid electric powertrain powered with sustainable fuel is the better choice. The present work proposes the concept of an ethanol-biodiesel fuel based RCCI plugin parallel hybrid electric vehicles (PHEVs). The significant examination of this work in the aspects of optimal torque structure and injector control parameter calibration. The calibration of the RCCI engine torque structure operation is performed based on the multi-objective constraints of variance of indicated mean effective pressure and the maximum rate of pressure rise for the combustion stability. While the equivalence ratio and combustion temperature with emphasis on limiting the amount of NOx and soot emissions. The maximum ethanol energy share proportion of 66.98% has been achieved. The RCCI engine is most frequently operating in the torque zone of 10 Nm and then 17.81 Nm while the BLDC motor is most frequently operated in the range of 55 Nm even at max torque of 60 Nm. When comparing to a standard diesel hybrid setup, the ethanol-biodiesel fuelled RCCI setup has a 18%–19% reduction in harmful NOx emissions, with an equivalent decrease of 22.7% in fuel economy. This is achieved by only having a 13.4% more CO emission while a minimal increase in soot emission throughout the overall course of the driving cycle. The result of this work shows evidence that implementing an ethanol-biodiesel fuelled RCCI engine in a parallel hybrid configuration achieves a significant reduction in NOx emissions while achieving the performance targets and managing the fuel and battery energy utilization over a driving cycle efficiently.
{"title":"Assessing the potential and energy distribution calibration of ethanol-biodiesel based RCCI mode of compression ignition engine in a plugin parallel hybrid electric vehicle","authors":"Rajasekar Vignesh, David Ray George, Bragadeshwaran Ashok, Vijayakumar Thulasi, Dariusz Szpica","doi":"10.1177/09544062241277727","DOIUrl":"https://doi.org/10.1177/09544062241277727","url":null,"abstract":"The focus of this research is to develop the resilient powertrain to combat the enforcement of stringent emission regulations and elevate its feasibility to power with the sustainable fuels to support the fuel crisis. The current solution in the reliable and adaptability point of view is the low temperature combustion engine-based hybrid electric powertrain powered with sustainable fuel is the better choice. The present work proposes the concept of an ethanol-biodiesel fuel based RCCI plugin parallel hybrid electric vehicles (PHEVs). The significant examination of this work in the aspects of optimal torque structure and injector control parameter calibration. The calibration of the RCCI engine torque structure operation is performed based on the multi-objective constraints of variance of indicated mean effective pressure and the maximum rate of pressure rise for the combustion stability. While the equivalence ratio and combustion temperature with emphasis on limiting the amount of NOx and soot emissions. The maximum ethanol energy share proportion of 66.98% has been achieved. The RCCI engine is most frequently operating in the torque zone of 10 Nm and then 17.81 Nm while the BLDC motor is most frequently operated in the range of 55 Nm even at max torque of 60 Nm. When comparing to a standard diesel hybrid setup, the ethanol-biodiesel fuelled RCCI setup has a 18%–19% reduction in harmful NOx emissions, with an equivalent decrease of 22.7% in fuel economy. This is achieved by only having a 13.4% more CO emission while a minimal increase in soot emission throughout the overall course of the driving cycle. The result of this work shows evidence that implementing an ethanol-biodiesel fuelled RCCI engine in a parallel hybrid configuration achieves a significant reduction in NOx emissions while achieving the performance targets and managing the fuel and battery energy utilization over a driving cycle efficiently.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"46 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1177/09544062241278790
Ye Li, YuMei Zhang, RuiQian Wang, Zhao Tang
Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( Rw) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.
要提高高速列车的速度，就必须对车辆进行轻量化设计，以满足此类列车的经济和生态效益要求。然而，这些目标与高速列车的车内噪声控制相冲突，因为板式结构的隔音效果遵循质量定律原理。列车地板是高速列车的主要车体结构，其隔音性能直接影响车内噪音水平，因此对车内噪音控制至关重要。由于复合地板系统的复杂性，对其隔音性能进行可靠测量和精确估算往往费时费力。针对这种情况，本研究提出了一种基于人工神经网络（ANN）的模型来预测复合地板的隔音特性。首先，基于统计能量分析（SEA）建立了复合地板的隔音模型。通过改变挤压地板的尺寸、腹板厚度、吸音材料和木地板，计算出 200 种复合地板的隔音性能，从而建立了复合地板的隔音数据库。然后，引入 ANN 模型并在隔声数据库上进行训练。将使用 ANN 模型获得的隔音预测结果与使用实验获得的预测结果进行比较，以验证其有效性。随后，采用 NSGA-II 优化方法对复合地板的隔音结构进行优化。与普通复合地板结构相比，优化后的结构使复合地板的质量减轻了 10.93 千克，隔声量（Rw）增加了 6.3 分贝。所提出的方法对车辆设计人员来说是一种有效、经济、高效的工具，有助于促进高速列车复合材料地板的隔音优化设计。
{"title":"Artificial neural network-based sound insulation optimization design of composite floor of high-speed train","authors":"Ye Li, YuMei Zhang, RuiQian Wang, Zhao Tang","doi":"10.1177/09544062241278790","DOIUrl":"https://doi.org/10.1177/09544062241278790","url":null,"abstract":"Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( R<jats:sub>w</jats:sub>) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"1 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1177/09544062241278792
Eryong Zhang, Fengshuo He, Yong Lu, Xiaotao Yang
Electromagnetic Mechanical Variable Valve Actuation (EMVVA) technology has the capability to improve engine performance and decrease pollutant emissions. A new type of EMVVA composed of electromagnetic driver and mechanical transmission is proposed to realize the flexible adjustment of valve parameters whether the valve is open or closed process. The geometric mathematical model of the mechanical transmission is developed based on the motion laws of the valve and the geometrical construction of the mechanical transmission, and the conjugate cam curve is solved. A multi-body dynamic model is constructed to calculate the driving torque and energy consumption needed by the mechanical transmission based on the mass, rotational inertia, centroid position of the parts, combining the normal contact force model and the friction model of the clearance contact pair at the same time. Based on the geometric model and multi-body dynamics model, the test platform is established. The test results demonstrated that within the specified valve lift, EMVVA system could accomplish variable valve lift, variable valve timing, and variable valve duration at maximum lift. The maximum timing error does not exceed 1° crank angle, and the maximum valve lift error does not exceed 0.25 mm at 11 mm valve lift. In addition, the error of driving torque between the multi-body dynamics model and the test is less than 0.3 [Formula: see text].Low energy optimization of mechanical transmission was completed using the NSGA-II method and multi-body model. According to the results of the optimization, energy consumption was reduced by 14%, and the peak driving torque was decreased by 54.3%.
{"title":"Multi-body dynamics modeling and energy consumption optimization of electromagnetic mechanical fully variable valve system","authors":"Eryong Zhang, Fengshuo He, Yong Lu, Xiaotao Yang","doi":"10.1177/09544062241278792","DOIUrl":"https://doi.org/10.1177/09544062241278792","url":null,"abstract":"Electromagnetic Mechanical Variable Valve Actuation (EMVVA) technology has the capability to improve engine performance and decrease pollutant emissions. A new type of EMVVA composed of electromagnetic driver and mechanical transmission is proposed to realize the flexible adjustment of valve parameters whether the valve is open or closed process. The geometric mathematical model of the mechanical transmission is developed based on the motion laws of the valve and the geometrical construction of the mechanical transmission, and the conjugate cam curve is solved. A multi-body dynamic model is constructed to calculate the driving torque and energy consumption needed by the mechanical transmission based on the mass, rotational inertia, centroid position of the parts, combining the normal contact force model and the friction model of the clearance contact pair at the same time. Based on the geometric model and multi-body dynamics model, the test platform is established. The test results demonstrated that within the specified valve lift, EMVVA system could accomplish variable valve lift, variable valve timing, and variable valve duration at maximum lift. The maximum timing error does not exceed 1° crank angle, and the maximum valve lift error does not exceed 0.25 mm at 11 mm valve lift. In addition, the error of driving torque between the multi-body dynamics model and the test is less than 0.3 [Formula: see text].Low energy optimization of mechanical transmission was completed using the NSGA-II method and multi-body model. According to the results of the optimization, energy consumption was reduced by 14%, and the peak driving torque was decreased by 54.3%.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"93 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1177/09544062241274178
Feng Xiaoliang, Zhang Zhiwei, Zhao Aiming
In this paper, the issue of cross-condition fault diagnosis of bearing is studied. During actual operation, the conditions of bearing vary due to changes in factors such as rotation speed and load, and the data distribution between different working conditions varies. Deep learning models that perform well in one condition are not ideal when applied to another condition directly. To address this problem, a novel unsupervised domain adaptation fault diagnosis method based on joint feature alignment is proposed in this paper. 1D-CNN is used as a weight-shared feature extractor to extract the features from both the source and target domains. The discrepancies in marginal and conditional distributions between the source and target domains are comprehensively considered by multi-layer multi-bandwidth Cauchy kernel maximum mean discrepancy (MB-CMMD) and mutual information (MI). The domain drift is reduced by aligning the feature representations of source and target domains. The network after feature alignment demonstrates a notable enhancement in the diagnostic accuracy of unlabeled samples within the target domain. The experimental results demonstrate that, in comparison to other domain adaptation approaches, The proposed approach can significantly enhance the accuracy of fault diagnosis while realizing feature alignment.
{"title":"Unsupervised domain adaptation bearing fault diagnosis method based on joint feature alignment","authors":"Feng Xiaoliang, Zhang Zhiwei, Zhao Aiming","doi":"10.1177/09544062241274178","DOIUrl":"https://doi.org/10.1177/09544062241274178","url":null,"abstract":"In this paper, the issue of cross-condition fault diagnosis of bearing is studied. During actual operation, the conditions of bearing vary due to changes in factors such as rotation speed and load, and the data distribution between different working conditions varies. Deep learning models that perform well in one condition are not ideal when applied to another condition directly. To address this problem, a novel unsupervised domain adaptation fault diagnosis method based on joint feature alignment is proposed in this paper. 1D-CNN is used as a weight-shared feature extractor to extract the features from both the source and target domains. The discrepancies in marginal and conditional distributions between the source and target domains are comprehensively considered by multi-layer multi-bandwidth Cauchy kernel maximum mean discrepancy (MB-CMMD) and mutual information (MI). The domain drift is reduced by aligning the feature representations of source and target domains. The network after feature alignment demonstrates a notable enhancement in the diagnostic accuracy of unlabeled samples within the target domain. The experimental results demonstrate that, in comparison to other domain adaptation approaches, The proposed approach can significantly enhance the accuracy of fault diagnosis while realizing feature alignment.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"61 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1177/09544062241272465
Soutrik Bose
A comparative performance analysis has been investigated on wire electrical discharge machining (WEDM) responses while machining a hybrid titanium matrix composite (TMC) varying the key input parameters like power (P), peak current (IP) and time-off (Toff). Two novel multi-objective optimization algorithms are developed namely desirable multi-objective genetic algorithm (DMOGA) and desirable multi-objective giant pacific octopus optimizer (DMOGPOO) for tackling various issues in many industries like automobile valve pins in crank and cam shafts, aerospace propeller and biomedical implants. The principal advantage of DMOGA to other algorithm is accuracy and robustness. The novelty fits in the iterative progression of growth of efficient grandee set, uttered as population congregating to a fitness function. Many techniques frequently encounter substandard solutions when evaluating MOO problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. DMOGPOO is an enthralling statistical method which mimics the octopus’s predatory behavior, performs better than other multi-objective optimization (MOO) algorithms, where the desirable objective functions is fetched in python using MOGPOO. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. DMOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all goals. Experimental investigation is accepted on material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC). Combined desirability in case of DMOGA is 0.716 which improved to 0.813 when DMOGPOO is proposed. MOO is improved with DMOGPOO of 13.547% when contrasted with DMOGA, with MRR of 3.81 mm3/min, SR of 0.79 µm, KW of 0.349 mm, OC of 0.099 mm, and combined desirability of 0.813. Improved optimality set is obtained when DMOGPOO is used. %improvement of MRR is 5.54%, SR is 75.95%, KW is 0.29%, and OC is 4.21%.
{"title":"Novel statistical investigation on performance measures of WEDM: Optimization, microstructure and mechanical properties","authors":"Soutrik Bose","doi":"10.1177/09544062241272465","DOIUrl":"https://doi.org/10.1177/09544062241272465","url":null,"abstract":"A comparative performance analysis has been investigated on wire electrical discharge machining (WEDM) responses while machining a hybrid titanium matrix composite (TMC) varying the key input parameters like power (P), peak current (IP) and time-off (Toff). Two novel multi-objective optimization algorithms are developed namely desirable multi-objective genetic algorithm (DMOGA) and desirable multi-objective giant pacific octopus optimizer (DMOGPOO) for tackling various issues in many industries like automobile valve pins in crank and cam shafts, aerospace propeller and biomedical implants. The principal advantage of DMOGA to other algorithm is accuracy and robustness. The novelty fits in the iterative progression of growth of efficient grandee set, uttered as population congregating to a fitness function. Many techniques frequently encounter substandard solutions when evaluating MOO problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. DMOGPOO is an enthralling statistical method which mimics the octopus’s predatory behavior, performs better than other multi-objective optimization (MOO) algorithms, where the desirable objective functions is fetched in python using MOGPOO. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. DMOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all goals. Experimental investigation is accepted on material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC). Combined desirability in case of DMOGA is 0.716 which improved to 0.813 when DMOGPOO is proposed. MOO is improved with DMOGPOO of 13.547% when contrasted with DMOGA, with MRR of 3.81 mm<jats:sup>3</jats:sup>/min, SR of 0.79 µm, KW of 0.349 mm, OC of 0.099 mm, and combined desirability of 0.813. Improved optimality set is obtained when DMOGPOO is used. %improvement of MRR is 5.54%, SR is 75.95%, KW is 0.29%, and OC is 4.21%.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"188 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1177/09544062241277733
Kiran Kaware, Mangesh Kotambkar
The research outlined in this paper extends the authors previous work to explore the impact resistance enhancement of CFRP composites through numerical simulations, focusing on the incorporation of a hybrid combination of Kevlar and glass fibers. Experimental drop weight low velocity impact and advanced NDT tests on CFRP and hybrid laminates were carried out in previous work. The current work focuses on the analysis of the dynamic behavior of hybrid composite subjected to LVI by numerical simulation methods. The numerical simulations have been validated with experiments and the damage area of numerical and experimental tests were compared. CFRP and hybrid laminates were modeled using Abaqus/Explicit FEA software to investigate the damage modes and mechanisms. The history curves of simulation results such as load/displacement-time etc. compared with experimental. Results show that incorporating 25% Kevlar fibers on the outer layer of the CFRP, denoted as [01K/06C/01K] resulted in a reduction of laminate deflection by 63.64%. Additionally, the stress distribution expanded over a larger area. A good concurrence between the simulation and experimental findings has been established, indicating that the modeling approach is suitable for conducting further parametric investigations.
{"title":"Hybridization effect of Kevlar and glass fiber on carbon/epoxy composites to enhance the damage strength under low velocity impact. Part II: Numerical simulations","authors":"Kiran Kaware, Mangesh Kotambkar","doi":"10.1177/09544062241277733","DOIUrl":"https://doi.org/10.1177/09544062241277733","url":null,"abstract":"The research outlined in this paper extends the authors previous work to explore the impact resistance enhancement of CFRP composites through numerical simulations, focusing on the incorporation of a hybrid combination of Kevlar and glass fibers. Experimental drop weight low velocity impact and advanced NDT tests on CFRP and hybrid laminates were carried out in previous work. The current work focuses on the analysis of the dynamic behavior of hybrid composite subjected to LVI by numerical simulation methods. The numerical simulations have been validated with experiments and the damage area of numerical and experimental tests were compared. CFRP and hybrid laminates were modeled using Abaqus/Explicit FEA software to investigate the damage modes and mechanisms. The history curves of simulation results such as load/displacement-time etc. compared with experimental. Results show that incorporating 25% Kevlar fibers on the outer layer of the CFRP, denoted as [0<jats:sub>1</jats:sub><jats:sup>K</jats:sup>/0<jats:sub>6</jats:sub><jats:sup>C</jats:sup>/0<jats:sub>1</jats:sub><jats:sup>K</jats:sup>] resulted in a reduction of laminate deflection by 63.64%. Additionally, the stress distribution expanded over a larger area. A good concurrence between the simulation and experimental findings has been established, indicating that the modeling approach is suitable for conducting further parametric investigations.","PeriodicalId":20558,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science","volume":"5 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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