Pub Date : 2024-05-16DOI: 10.3390/machines12050346
Aselimhe Oreavbiere, Muhammad Khan
Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction.
{"title":"Mathematical Complexities in Modelling Damage in Spur Gears","authors":"Aselimhe Oreavbiere, Muhammad Khan","doi":"10.3390/machines12050346","DOIUrl":"https://doi.org/10.3390/machines12050346","url":null,"abstract":"Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.3390/machines12050348
Changlong Ye, Zun Wang, Suyang Yu, Chunying Jiang
Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.
{"title":"An Image-Based Interactive Training Method of an Upper Limb Rehabilitation Robot","authors":"Changlong Ye, Zun Wang, Suyang Yu, Chunying Jiang","doi":"10.3390/machines12050348","DOIUrl":"https://doi.org/10.3390/machines12050348","url":null,"abstract":"Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3390/machines12050341
Lian Guo, Yongguo Wang
In the manufacturing sector, tool wear substantially affects product quality and production efficiency. While traditional sequential deep learning models can handle time-series tasks, their neglect of complex temporal relationships in time-series data often leads to errors accumulating in continuous predictions, which reduces their forecasting accuracy for tool wear. For addressing these limitations, the parallel convolutional and recurrent neural networks with attention-modulated residual learning (ParaCRN-AMResNet) model is introduced. Compared with conventional deep learning models, ParaCRN-AMResNet markedly enhances the efficiency and precision of feature extraction from time-series data through its innovative parallel architecture. The model adeptly combines dilated convolution neural network and bidirectional gated recurrent units, effectively addressing distance dependencies and enriching the quantity and dimensions of extracted features. The strength of ParaCRN-AMResNet lies in its refined ability to capture the complex dynamics of time-series data, significantly boosting the model’s accuracy and generalization capability. The model’s efficacy was validated through comprehensive milling experiments and vibration signal analyses, showcasing ParaCRN-AMResNet’s superior performance. In evaluation metrics, the model achieved a MAE of 2.6015, MSE of 15.1921, R2 of 0.9897, and MAPE of 2.7997%, conclusively proving its efficiency and accuracy in the precise prediction of tool wear.
{"title":"Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach","authors":"Lian Guo, Yongguo Wang","doi":"10.3390/machines12050341","DOIUrl":"https://doi.org/10.3390/machines12050341","url":null,"abstract":"In the manufacturing sector, tool wear substantially affects product quality and production efficiency. While traditional sequential deep learning models can handle time-series tasks, their neglect of complex temporal relationships in time-series data often leads to errors accumulating in continuous predictions, which reduces their forecasting accuracy for tool wear. For addressing these limitations, the parallel convolutional and recurrent neural networks with attention-modulated residual learning (ParaCRN-AMResNet) model is introduced. Compared with conventional deep learning models, ParaCRN-AMResNet markedly enhances the efficiency and precision of feature extraction from time-series data through its innovative parallel architecture. The model adeptly combines dilated convolution neural network and bidirectional gated recurrent units, effectively addressing distance dependencies and enriching the quantity and dimensions of extracted features. The strength of ParaCRN-AMResNet lies in its refined ability to capture the complex dynamics of time-series data, significantly boosting the model’s accuracy and generalization capability. The model’s efficacy was validated through comprehensive milling experiments and vibration signal analyses, showcasing ParaCRN-AMResNet’s superior performance. In evaluation metrics, the model achieved a MAE of 2.6015, MSE of 15.1921, R2 of 0.9897, and MAPE of 2.7997%, conclusively proving its efficiency and accuracy in the precise prediction of tool wear.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"51 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3390/machines12050343
Yipeng Wu, Teng Wang, Tao Song, Wenxiao Guo
To address the problem of problematic spray design inside mining anchor-digging equipment, a switching seal using a permanent magnet eddy current drive is initially presented here. The layer model of the permanent magnet eddy current structure is established, the subdomain analysis model is introduced, the permanent magnet eddy current structure is divided into six regions along the axial direction, and the boundary equations are established at the interfaces of each region. The vector magnetic potential equations in each region are deduced, along with the electromagnetic torque and axial force equations. The computational results are compared and analyzed with the results of finite element simulation, verifying the accuracy of the theoretical model. The design of experiments is used to verify the feasibility of the switching seal using the permanent magnet eddy current structure.
{"title":"Electromagnetic Characterization of Permanent Magnet Eddy Current Structures Based on Backplane Distance Adjustment","authors":"Yipeng Wu, Teng Wang, Tao Song, Wenxiao Guo","doi":"10.3390/machines12050343","DOIUrl":"https://doi.org/10.3390/machines12050343","url":null,"abstract":"To address the problem of problematic spray design inside mining anchor-digging equipment, a switching seal using a permanent magnet eddy current drive is initially presented here. The layer model of the permanent magnet eddy current structure is established, the subdomain analysis model is introduced, the permanent magnet eddy current structure is divided into six regions along the axial direction, and the boundary equations are established at the interfaces of each region. The vector magnetic potential equations in each region are deduced, along with the electromagnetic torque and axial force equations. The computational results are compared and analyzed with the results of finite element simulation, verifying the accuracy of the theoretical model. The design of experiments is used to verify the feasibility of the switching seal using the permanent magnet eddy current structure.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"128 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3390/machines12050342
Yong Liu, Jiaqi Liu, Han Wang, Mingshun Yang, Xinqin Gao, Shujuan Li
In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.
{"title":"A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory","authors":"Yong Liu, Jiaqi Liu, Han Wang, Mingshun Yang, Xinqin Gao, Shujuan Li","doi":"10.3390/machines12050342","DOIUrl":"https://doi.org/10.3390/machines12050342","url":null,"abstract":"In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"58 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.3390/machines12050340
Ziwen Zhao, Jian Mao, Xingchi Wei
Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the “nine-line method” is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the “S-shaped specimen” to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method.
{"title":"Geometric Error-Based Multi-Source Error Identification and Compensation Strategy for Five-Axis Side Milling","authors":"Ziwen Zhao, Jian Mao, Xingchi Wei","doi":"10.3390/machines12050340","DOIUrl":"https://doi.org/10.3390/machines12050340","url":null,"abstract":"Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the “nine-line method” is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the “S-shaped specimen” to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"32 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.3390/machines12050338
Xingfu Zhao, Z. Yue, Jianjun Qu, Marmysh Dzianis, Yanzhong Wang
This study focuses on a marine two-stage tandem hybrid planetary system. Natural frequencies and vibration modes are determined using a translational–torsional coupled dynamic model. Based on the motion characteristics of the transmission system, free vibration is categorized into three typical modes. The parameter sensitivity of natural frequencies is computed, and the effects of structural parameters such as unequally spaced planet, mesh stiffness, planet mass and rotational inertia on the natural frequencies are analyzed. Utilizing the coupling factor, the mode transition criterion for the natural frequencies response to parameters is formulated. The results demonstrate that the vibration modes of the two-stage tandem hybrid planetary system can be classified as the fixed-axis train vibration mode, the differential train vibration mode, and the coupled vibration mode. Unequally spaced planet primarily disrupts vibration modes without significantly affecting natural frequencies. In contrast, the effects of mesh stiffness, planet mass and rotational inertia on the modes are opposite to those of unequally spaced planets. The validity of the parameter sensitivity and mode transition criterion is substantiated through illustrative examples.
{"title":"Natural Characteristics of a Marine Two-Stage Tandem Hybrid Planetary System","authors":"Xingfu Zhao, Z. Yue, Jianjun Qu, Marmysh Dzianis, Yanzhong Wang","doi":"10.3390/machines12050338","DOIUrl":"https://doi.org/10.3390/machines12050338","url":null,"abstract":"This study focuses on a marine two-stage tandem hybrid planetary system. Natural frequencies and vibration modes are determined using a translational–torsional coupled dynamic model. Based on the motion characteristics of the transmission system, free vibration is categorized into three typical modes. The parameter sensitivity of natural frequencies is computed, and the effects of structural parameters such as unequally spaced planet, mesh stiffness, planet mass and rotational inertia on the natural frequencies are analyzed. Utilizing the coupling factor, the mode transition criterion for the natural frequencies response to parameters is formulated. The results demonstrate that the vibration modes of the two-stage tandem hybrid planetary system can be classified as the fixed-axis train vibration mode, the differential train vibration mode, and the coupled vibration mode. Unequally spaced planet primarily disrupts vibration modes without significantly affecting natural frequencies. In contrast, the effects of mesh stiffness, planet mass and rotational inertia on the modes are opposite to those of unequally spaced planets. The validity of the parameter sensitivity and mode transition criterion is substantiated through illustrative examples.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"29 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.3390/machines12050339
Han Zhao, Jing Li, Xiaochen Zhang, Bin Xiong, Chenyi Zhao, Yixiao Ruan, H. Wang, Jing Zhang, Zhouwei Lan, Xiaoyan Huang, He Zhang
The demand for high-power density motors has been increasing due to their remarkable output capability and compact construction. To achieve a significant improvement in motor power density, lightweight design methods have been recognized as an effective enabler. Therefore, extensive investigations have been conducted to reduce motor mass and achieve lightweight configurations through the exploration of lightweight materials, structures and manufacturing techniques. This article provides a comprehensive review and summary of state-of-the-art lightweight implementation methods for electrical machines, including the utilization of lightweight materials, structural lightweight design, and incorporation of advanced manufacturing technologies, such as additive manufacturing techniques. The advantages and limitations of each approach are also discussed in this paper. Furthermore, some comments and forecasts on potential future methodologies for motor lightweighting are also provided.
{"title":"State-of-the-Art Lightweight Implementation Methods in Electrical Machines","authors":"Han Zhao, Jing Li, Xiaochen Zhang, Bin Xiong, Chenyi Zhao, Yixiao Ruan, H. Wang, Jing Zhang, Zhouwei Lan, Xiaoyan Huang, He Zhang","doi":"10.3390/machines12050339","DOIUrl":"https://doi.org/10.3390/machines12050339","url":null,"abstract":"The demand for high-power density motors has been increasing due to their remarkable output capability and compact construction. To achieve a significant improvement in motor power density, lightweight design methods have been recognized as an effective enabler. Therefore, extensive investigations have been conducted to reduce motor mass and achieve lightweight configurations through the exploration of lightweight materials, structures and manufacturing techniques. This article provides a comprehensive review and summary of state-of-the-art lightweight implementation methods for electrical machines, including the utilization of lightweight materials, structural lightweight design, and incorporation of advanced manufacturing technologies, such as additive manufacturing techniques. The advantages and limitations of each approach are also discussed in this paper. Furthermore, some comments and forecasts on potential future methodologies for motor lightweighting are also provided.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"41 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.3390/machines12050336
Michael Baranowski, Johannes Scholz, F. Kößler, Jürgen Fleischer
Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques reliant on resin and extrusion entail extensive and costly post-processing procedures to eliminate support structures, constraining design versatility and complicating small-scale production endeavours. In contrast, laser sintering (LS) emerges as a promising avenue for industrial application. It facilitates the efficient and cost-effective manufacturing of resilient parts without needing support structures. However, the current state of research and technological capabilities has yet to yield an LS machine that integrates the benefits of continuous fibre reinforcement with the inherent advantages of the LS process. This paper describes the systematic development process according to VDI 2221 of a new type of LS machine with automated continuous fibre integration while keeping the advantages of the LS process. The resulting physical prototype of the machine is also presented. Furthermore, this study presents an approach to integrate the cost and Product Carbon Footprint of the process in the product design. For this purpose, a machine state model was developed, and the costs and Product Carbon footprint of a part were analysed based on the model. The promising potential for future lightweight products is demonstrated through the production of CCFRP parts.
在增材制造工艺中加入连续碳纤维增强聚合物（CCFRP）部件，是制造坚固轻质部件的一大进步，尤其适用于航空航天、工程和各种工业领域。然而，目前用于 CCFRP 部件的增材制造方法存在明显的局限性。依赖树脂和挤压的技术需要大量昂贵的后处理程序来消除支撑结构，从而限制了设计的多功能性，并使小规模生产变得更加复杂。与此相反，激光烧结（LS）成为一种很有前途的工业应用途径。它无需支撑结构，就能高效、经济地制造弹性部件。然而，就目前的研究和技术能力而言，还没有一台 LS 设备能将连续纤维加固的优势与 LS 工艺的固有优势融为一体。本文介绍了根据 VDI 2221 标准系统开发新型 LS 设备的过程，该设备可自动集成连续纤维，同时保持 LS 工艺的优势。本文还介绍了该机器的物理原型。此外，本研究还提出了一种在产品设计中整合工艺成本和产品碳足迹的方法。为此，开发了一个机器状态模型，并根据该模型分析了零件的成本和产品碳足迹。通过生产 CCFRP 部件，展示了未来轻质产品的巨大潜力。
{"title":"Systematic Development of a Novel Laser-Sintering Machine with Roving Integration and Sustainability Evaluation","authors":"Michael Baranowski, Johannes Scholz, F. Kößler, Jürgen Fleischer","doi":"10.3390/machines12050336","DOIUrl":"https://doi.org/10.3390/machines12050336","url":null,"abstract":"Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques reliant on resin and extrusion entail extensive and costly post-processing procedures to eliminate support structures, constraining design versatility and complicating small-scale production endeavours. In contrast, laser sintering (LS) emerges as a promising avenue for industrial application. It facilitates the efficient and cost-effective manufacturing of resilient parts without needing support structures. However, the current state of research and technological capabilities has yet to yield an LS machine that integrates the benefits of continuous fibre reinforcement with the inherent advantages of the LS process. This paper describes the systematic development process according to VDI 2221 of a new type of LS machine with automated continuous fibre integration while keeping the advantages of the LS process. The resulting physical prototype of the machine is also presented. Furthermore, this study presents an approach to integrate the cost and Product Carbon Footprint of the process in the product design. For this purpose, a machine state model was developed, and the costs and Product Carbon footprint of a part were analysed based on the model. The promising potential for future lightweight products is demonstrated through the production of CCFRP parts.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-14DOI: 10.3390/machines12050337
Sandy-Natalie Campos-Martínez, O. Hernández-González, M. Guerrero-Sánchez, Guillermo Valencia-Palomo, B. Targui, F. López‐Estrada
This article deals with the consensus tracking problem for multi-agent systems (MAS) under the influence of unknown time-varying delays. Each agent of the MAS is a quadrotor unmanned aerial vehicle (UAV) represented as a linear continuous-time system. The main objective of this paper is the stabilization of multi-agent systems where the control input is affected by unknown time-varying delays, which are assumed to be upper-bounded, and where these bounds are not required to be known. The proposed observer-based control scheme guarantees the consensus tracking of multi-UAV systems with the desired H∞ performance, which adds a further level of mitigation of unknown delays present in MAS systems by minimizing the H∞ norm, which measures the maximum gain from the disturbance to the controlled output of the system. For each UAV agent, an unknown input observer is employed to isolate the unknown time-varying delays in the state estimation process. With the use of an unknown input observer-based consensus tracking control, sufficient conditions are derived to ensure that all follower UAVs can reach a consensus with the leader, despite the presence of distinct unknown time-varying delays. The stability of the proposed scheme is proven using Lyapunov theory for the leader and follower agents. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.
本文讨论了在未知时变延迟影响下的多代理系统（MAS）的共识跟踪问题。多代理系统的每个代理都是四旋翼无人飞行器（UAV），用线性连续时间系统表示。本文的主要目标是控制输入受未知时变延迟影响的多代理系统的稳定，这些延迟被假定为上界，而这些上界并不需要已知。所提出的基于观测器的控制方案保证了多无人机系统以理想的 H∞ 性能进行一致跟踪，通过最小化 H∞ 准则（衡量从干扰到系统受控输出的最大增益），进一步减轻了 MAS 系统中存在的未知延迟。对于每个无人飞行器代理，采用未知输入观测器来隔离状态估计过程中的未知时变延迟。通过使用基于未知输入观测器的共识跟踪控制，得出了充分的条件，以确保尽管存在不同的未知时变延迟，所有跟随者无人机都能与领导者达成共识。利用领导者和跟随者的 Lyapunov 理论证明了所提方案的稳定性。最后，还提供了数值示例来说明所提方法的有效性。
{"title":"Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays","authors":"Sandy-Natalie Campos-Martínez, O. Hernández-González, M. Guerrero-Sánchez, Guillermo Valencia-Palomo, B. Targui, F. López‐Estrada","doi":"10.3390/machines12050337","DOIUrl":"https://doi.org/10.3390/machines12050337","url":null,"abstract":"This article deals with the consensus tracking problem for multi-agent systems (MAS) under the influence of unknown time-varying delays. Each agent of the MAS is a quadrotor unmanned aerial vehicle (UAV) represented as a linear continuous-time system. The main objective of this paper is the stabilization of multi-agent systems where the control input is affected by unknown time-varying delays, which are assumed to be upper-bounded, and where these bounds are not required to be known. The proposed observer-based control scheme guarantees the consensus tracking of multi-UAV systems with the desired H∞ performance, which adds a further level of mitigation of unknown delays present in MAS systems by minimizing the H∞ norm, which measures the maximum gain from the disturbance to the controlled output of the system. For each UAV agent, an unknown input observer is employed to isolate the unknown time-varying delays in the state estimation process. With the use of an unknown input observer-based consensus tracking control, sufficient conditions are derived to ensure that all follower UAVs can reach a consensus with the leader, despite the presence of distinct unknown time-varying delays. The stability of the proposed scheme is proven using Lyapunov theory for the leader and follower agents. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.","PeriodicalId":509264,"journal":{"name":"Machines","volume":"37 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：