In this paper, a simple and effective self-adaptive material interpolation scheme is proposed to solve the numerical instability problem, which may occur in topology optimization considering geometrical nonlinearity when using density-based method. The primary concept of the proposed method revolves around enhancing the deformation resistance of minimum-density or intermediatedensity elements, thus avoiding numerical instability due to excessive distortion of these elements. The proposed self-adaptive material interpolation scheme is based on the power law method, and the stiffness of minimum-density or intermediate-density elements can be adjusted by a single parameter, α. During the optimization process, the parameter α will be changed according to an adaptive adjustment strategy to ensure that elements within the design domain are not excessively distorted, while the mechanical behavior of the structure can be approximated with acceptable accuracy. Numerical examples of minimizing compliance and maximizing displacement of structure are given to prove the validity of the proposed self-adaptive material interpolation scheme.
{"title":"Topology Optimization of Geometrically Nonlinear Structures Based on a Self-Adaptive Material Interpolation Scheme","authors":"Junwen Liang, Xianmin Zhang, Benliang Zhu, Rixin Wang, Chaoyu Cui, Hongchuan Zhang","doi":"10.3390/machines11121047","DOIUrl":"https://doi.org/10.3390/machines11121047","url":null,"abstract":"In this paper, a simple and effective self-adaptive material interpolation scheme is proposed to solve the numerical instability problem, which may occur in topology optimization considering geometrical nonlinearity when using density-based method. The primary concept of the proposed method revolves around enhancing the deformation resistance of minimum-density or intermediatedensity elements, thus avoiding numerical instability due to excessive distortion of these elements. The proposed self-adaptive material interpolation scheme is based on the power law method, and the stiffness of minimum-density or intermediate-density elements can be adjusted by a single parameter, α. During the optimization process, the parameter α will be changed according to an adaptive adjustment strategy to ensure that elements within the design domain are not excessively distorted, while the mechanical behavior of the structure can be approximated with acceptable accuracy. Numerical examples of minimizing compliance and maximizing displacement of structure are given to prove the validity of the proposed self-adaptive material interpolation scheme.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"112 6","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-24DOI: 10.3390/machines11121048
Nils Gräbner, Dominik Schmid, U. von Wagner
Brake squeal—an audible high-frequency noise phenomenon in the range between 1 kHz and 15 kHz resulting from self-excited vibrations—is one of the main cost drivers while developing brake systems. Increasing damping is often a crucial factor in the context of self-excited vibrations. Countermeasures applied for preventing brake squeal have been investigated particularly for disk brakes in the past. However, in recent years, drum brakes have once again become more important, partly because of the issue of particle emissions. Concerning noise problems, drum brakes have a decisive advantage compared to disk brake systems in that the outer drum surface is freely accessible for applying damping devices. This paper focuses on the fundamental proving and evaluation of passive damping measures on a simplex drum brake system. To obtain a detailed understanding of the influence of additional damping on the squealing behavior of drum brakes, extensive experimental investigations are performed on a brake with an intentionally introduced high squealing tendency in the initial configuration. This made it possible to investigate the influence of different types of damping measures on their effectiveness. Techniques from the field of big data analysis and machine learning are tested to detect squeal in measured time series data. These techniques were remarkably reliable and made it possible to detect squeal efficiently even in data that was not generated on a traditional costly NVH brake dynamometer. To investigate whether the simulation method usually used for the simulation of brake squeal is applicable to depicting the influence of additional damping in drum brakes, a complex eigenvalue analysis was performed with Abaqus, and the results were compared with those from the experiments.
{"title":"On Drum Brake Squeal—Assessment of Damping Measures by Time Series Data Analysis of Dynamometer Tests and Complex Eigenvalue Analyses","authors":"Nils Gräbner, Dominik Schmid, U. von Wagner","doi":"10.3390/machines11121048","DOIUrl":"https://doi.org/10.3390/machines11121048","url":null,"abstract":"Brake squeal—an audible high-frequency noise phenomenon in the range between 1 kHz and 15 kHz resulting from self-excited vibrations—is one of the main cost drivers while developing brake systems. Increasing damping is often a crucial factor in the context of self-excited vibrations. Countermeasures applied for preventing brake squeal have been investigated particularly for disk brakes in the past. However, in recent years, drum brakes have once again become more important, partly because of the issue of particle emissions. Concerning noise problems, drum brakes have a decisive advantage compared to disk brake systems in that the outer drum surface is freely accessible for applying damping devices. This paper focuses on the fundamental proving and evaluation of passive damping measures on a simplex drum brake system. To obtain a detailed understanding of the influence of additional damping on the squealing behavior of drum brakes, extensive experimental investigations are performed on a brake with an intentionally introduced high squealing tendency in the initial configuration. This made it possible to investigate the influence of different types of damping measures on their effectiveness. Techniques from the field of big data analysis and machine learning are tested to detect squeal in measured time series data. These techniques were remarkably reliable and made it possible to detect squeal efficiently even in data that was not generated on a traditional costly NVH brake dynamometer. To investigate whether the simulation method usually used for the simulation of brake squeal is applicable to depicting the influence of additional damping in drum brakes, a complex eigenvalue analysis was performed with Abaqus, and the results were compared with those from the experiments.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"67 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-23DOI: 10.3390/machines11121042
Min-Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim, Jong-Ho Shin
Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.
{"title":"Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network","authors":"Min-Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim, Jong-Ho Shin","doi":"10.3390/machines11121042","DOIUrl":"https://doi.org/10.3390/machines11121042","url":null,"abstract":"Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"89 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A macro–micro dual-drive motion platform is a class of key system utilized in ultra-precision instruments and equipment for realizing ultra-high-precision positioning, which relates to the fields of semiconductor manufacturing, ultra-precision testing and machining, etc. Aiming at the ultra-high-precision positioning control problem of macro–micro dual-drive systems containing mechanical backlash, this paper analyzes the combined effect of mechanical coupling and backlash, and proposes a macro–micro compound control strategy. Firstly, the system dynamic model, including mechanical coupling, is established, and a quasi-linear backlash model is also proposed. Secondly, based on the above model, a stepwise nonlinear identification method is proposed to obtain the backlash characteristic online, which is the basis of accurate backlash compensation. Then, for the macro–micro structure containing the backlash, a macro decoupling control method, combined with a micro adaptive integral sliding mode control method and backlash compensation, are designed coordinately to guarantee that the large-stroke macro–micro cooperative motion reaches micron-level accuracy. Moreover, the boundary of the positioning error is adjustable by tuning the controller parameters. Finally, both the simulation and experimental results demonstrate that the proposed identification method can estimate the time-varying backlash precisely in finite time, and the system positioning accuracy can achieve an average 20 μm with long stroke and backlash influence, which is much higher than that using the traditional method and provides theoretical guidance for high-precision positioning control of a class of dual-drive motion platform.
{"title":"Nonlinear Identification and Decoupling Sliding Mode Control of Macro-Micro Dual-Drive Motion Platform with Mechanical Backlash","authors":"Shuo Kang, Buyang Zhang, Xing Huang, Rijin Zhong, Shengzhao Huang","doi":"10.3390/machines11121044","DOIUrl":"https://doi.org/10.3390/machines11121044","url":null,"abstract":"A macro–micro dual-drive motion platform is a class of key system utilized in ultra-precision instruments and equipment for realizing ultra-high-precision positioning, which relates to the fields of semiconductor manufacturing, ultra-precision testing and machining, etc. Aiming at the ultra-high-precision positioning control problem of macro–micro dual-drive systems containing mechanical backlash, this paper analyzes the combined effect of mechanical coupling and backlash, and proposes a macro–micro compound control strategy. Firstly, the system dynamic model, including mechanical coupling, is established, and a quasi-linear backlash model is also proposed. Secondly, based on the above model, a stepwise nonlinear identification method is proposed to obtain the backlash characteristic online, which is the basis of accurate backlash compensation. Then, for the macro–micro structure containing the backlash, a macro decoupling control method, combined with a micro adaptive integral sliding mode control method and backlash compensation, are designed coordinately to guarantee that the large-stroke macro–micro cooperative motion reaches micron-level accuracy. Moreover, the boundary of the positioning error is adjustable by tuning the controller parameters. Finally, both the simulation and experimental results demonstrate that the proposed identification method can estimate the time-varying backlash precisely in finite time, and the system positioning accuracy can achieve an average 20 μm with long stroke and backlash influence, which is much higher than that using the traditional method and provides theoretical guidance for high-precision positioning control of a class of dual-drive motion platform.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"75 ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-23DOI: 10.3390/machines11121043
Qitao Tan, Mohd Ariffanan Mohd Basri
This paper proposes a B-spline trajectory algorithm to realize multi-axis trajectory interpolation and analyzes the operating accuracy in an embedded system. However, the existing trajectory generation method needs to use computer-aided manufacturing (CAM) software to convert the interpolating trajectory into G code and download the code into the computer numerical control (CNC) system for processing. In this paper, the method of third-degree B-spline interpolation is proposed to generate a curved surface trajectory, and the trajectory generated by this algorithm can be run directly into a CNC system. The precision analysis of the ISO parameter segmentation interpolation algorithm and the theory of constant velocity motion is also presented. The significance of this project is that it designs a complete set of embedded systems, including hardware circuit design and software logic design, and uses low-cost STM32 architecture to realize a B-spline constant-speed interpolation algorithm, which is verified on CNC polishing equipment. A simulation conducted with the MATLAB software and the B-spline curve interpolation experiments performed on a multi-axis polishing machine tool demonstrate the effectiveness and accuracy of the optimized third-degree B-spline algorithm.
本文提出了一种实现多轴轨迹插补的 B-样条轨迹算法，并分析了其在嵌入式系统中的运行精度。然而，现有的轨迹生成方法需要使用计算机辅助制造（CAM）软件将插补轨迹转换为 G 代码，并将代码下载到计算机数控（CNC）系统中进行处理。本文提出了三度 B 样条插值生成曲面轨迹的方法，该算法生成的轨迹可直接运行到数控系统中。此外，还介绍了 ISO 参数分割插补算法的精度分析和匀速运动理论。本项目的意义在于设计了一套完整的嵌入式系统，包括硬件电路设计和软件逻辑设计，并采用低成本的 STM32 架构实现了 B 样条恒速插补算法，并在数控抛光设备上进行了验证。利用 MATLAB 软件进行的仿真和在多轴抛光机床上进行的 B-样条曲线插补实验证明了优化的三度 B-样条算法的有效性和准确性。
{"title":"Hardware–Software Embedded System for Real-Time Trajectory Planning of Multi-Axis Machine Using B-Spline Curve Interpolation Algorithm","authors":"Qitao Tan, Mohd Ariffanan Mohd Basri","doi":"10.3390/machines11121043","DOIUrl":"https://doi.org/10.3390/machines11121043","url":null,"abstract":"This paper proposes a B-spline trajectory algorithm to realize multi-axis trajectory interpolation and analyzes the operating accuracy in an embedded system. However, the existing trajectory generation method needs to use computer-aided manufacturing (CAM) software to convert the interpolating trajectory into G code and download the code into the computer numerical control (CNC) system for processing. In this paper, the method of third-degree B-spline interpolation is proposed to generate a curved surface trajectory, and the trajectory generated by this algorithm can be run directly into a CNC system. The precision analysis of the ISO parameter segmentation interpolation algorithm and the theory of constant velocity motion is also presented. The significance of this project is that it designs a complete set of embedded systems, including hardware circuit design and software logic design, and uses low-cost STM32 architecture to realize a B-spline constant-speed interpolation algorithm, which is verified on CNC polishing equipment. A simulation conducted with the MATLAB software and the B-spline curve interpolation experiments performed on a multi-axis polishing machine tool demonstrate the effectiveness and accuracy of the optimized third-degree B-spline algorithm.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"278 ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-23DOI: 10.3390/machines11121045
Judith Schmidt, Romy Müller
In complex work domains, not all possible faults can be anticipated by designers or handled by automation. Humans therefore play an important role in fault diagnosis. To support their diagnostic reasoning, it is necessary to understand the requirements that diagnosticians face. While much research has dealt with identifying domain-general aspects of fault diagnosis, the present exploratory study examined domain-specific influences on the requirements for diagnosticians. Scenario-based interviews were conducted with nine experts from two domains: the car domain and the packaging machine domain. The interviews revealed several factors that influence the requirements for successful fault diagnosis. These factors were summarized in five categories, namely domain background, technical system, typical faults, diagnostic process, and requirements. Based on these factors, we developed the Domain Requirements Model to predict requirements for diagnosticians (e.g., the need for empirical knowledge) from domain characteristics (e.g., the degree to which changes in inputs are available as domain knowledge) or characteristics of the diagnostic process (e.g., the extent of support). The model is discussed considering the psychological literature on fault diagnosis, and first insights are provided that show how the model can be used to predict requirements of diagnostic reasoning beyond the two domains studied here.
{"title":"Diagnosing Faults in Different Technical Systems: How Requirements for Diagnosticians Can Be Revealed by Comparing Domain Characteristics","authors":"Judith Schmidt, Romy Müller","doi":"10.3390/machines11121045","DOIUrl":"https://doi.org/10.3390/machines11121045","url":null,"abstract":"In complex work domains, not all possible faults can be anticipated by designers or handled by automation. Humans therefore play an important role in fault diagnosis. To support their diagnostic reasoning, it is necessary to understand the requirements that diagnosticians face. While much research has dealt with identifying domain-general aspects of fault diagnosis, the present exploratory study examined domain-specific influences on the requirements for diagnosticians. Scenario-based interviews were conducted with nine experts from two domains: the car domain and the packaging machine domain. The interviews revealed several factors that influence the requirements for successful fault diagnosis. These factors were summarized in five categories, namely domain background, technical system, typical faults, diagnostic process, and requirements. Based on these factors, we developed the Domain Requirements Model to predict requirements for diagnosticians (e.g., the need for empirical knowledge) from domain characteristics (e.g., the degree to which changes in inputs are available as domain knowledge) or characteristics of the diagnostic process (e.g., the extent of support). The model is discussed considering the psychological literature on fault diagnosis, and first insights are provided that show how the model can be used to predict requirements of diagnostic reasoning beyond the two domains studied here.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"19 6","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-22DOI: 10.3390/machines11121040
Shaohua Niu, Bingyang Li, Bingyang Li, Pengfei Wang, Yuxi Song
For projectile impact penetration experiment, batteries or capacitors are usually used as power sources for projectile-borne recording devices. However, these power sources are easy to fail under high impact. In this paper, a small-impact magnetoelectric generator is introduced, which converts impact force into electrical energy to supply power for devices. The influence of generator structure on force–electricity conversion efficiency is analyzed. Based on the analysis, a small-impact magnetoelectric generator with double springs and two-part coils is designed. A hammer test is carried out on the generator. The test results show that this generator structure would achieve higher force–electricity conversion efficiency under small space.
{"title":"Analysis and Design of Small-Impact Magnetoelectric Generator","authors":"Shaohua Niu, Bingyang Li, Bingyang Li, Pengfei Wang, Yuxi Song","doi":"10.3390/machines11121040","DOIUrl":"https://doi.org/10.3390/machines11121040","url":null,"abstract":"For projectile impact penetration experiment, batteries or capacitors are usually used as power sources for projectile-borne recording devices. However, these power sources are easy to fail under high impact. In this paper, a small-impact magnetoelectric generator is introduced, which converts impact force into electrical energy to supply power for devices. The influence of generator structure on force–electricity conversion efficiency is analyzed. Based on the analysis, a small-impact magnetoelectric generator with double springs and two-part coils is designed. A hammer test is carried out on the generator. The test results show that this generator structure would achieve higher force–electricity conversion efficiency under small space.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"39 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139248958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-22DOI: 10.3390/machines11121039
Xiangfan Wu, Chusen Wang, Zuzhi Tian, Xiankang Huang, Qian Wang
Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.
{"title":"Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision","authors":"Xiangfan Wu, Chusen Wang, Zuzhi Tian, Xiankang Huang, Qian Wang","doi":"10.3390/machines11121039","DOIUrl":"https://doi.org/10.3390/machines11121039","url":null,"abstract":"Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"39 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-11-22DOI: 10.3390/machines11121041
Jie Li, Bo Liu, Liangliang Duan, Jinsong Bao
The rapid expansion of the global electric vehicle industry has presented significant challenges in the management of end-of-life power batteries. Retired power batteries contain valuable resources, such as lithium, cobalt, nickel, and other metals, which can be recycled and reused in various applications. The existing disassembly processes rely on manual operations that are time-consuming, labour-intensive, and prone to errors. This research proposes an intelligent augmented reality (AR)-assisted disassembly approach that aims to increase disassembly efficiency by providing scene awareness and visual guidance to operators in real-time. The approach starts by employing a deep learning-based instance segmentation method to process the Red-Green-Blue-Dept (RGB-D) data of the disassembly scene. The segmentation method segments the disassembly object instances and reconstructs their point cloud representation, given the corresponding depth information obtained from the instance masks. In addition, to estimate the pose of the disassembly target in the scene and assess their disassembly status, an iterative closed point algorithm is used to align the segmented point cloud instances with the actual disassembly objects. The acquired information is then utilised for the generation of AR instructions, decreasing the need for frequent user interaction during the disassembly processes. To verify the feasibility of the AR-assisted disassembly system, experiments were conducted on end-of-life vehicle power batteries. The results demonstrated that this approach significantly enhanced disassembly efficiency and decreased the frequency of disassembly errors. Consequently, the findings indicate that the proposed approach is effective and holds promise for large-scale industrial recycling and disassembly operations.
全球电动汽车行业的快速发展给报废动力电池的管理带来了巨大挑战。报废动力电池含有宝贵的资源，如锂、钴、镍和其他金属，可在各种应用中回收和再利用。现有的拆解流程依赖于人工操作，耗时、耗力且容易出错。本研究提出了一种智能增强现实（AR）辅助拆卸方法，旨在通过实时为操作员提供场景感知和视觉引导来提高拆卸效率。该方法首先采用基于深度学习的实例分割方法来处理拆卸场景的红绿蓝三色（RGB-D）数据。该分割方法将拆卸对象实例分割开来，并根据从实例掩模中获得的相应深度信息重建其点云表示。此外，为了估计场景中拆卸目标的姿态并评估其拆卸状态，还使用了一种迭代闭点算法，将分割的点云实例与实际拆卸物体对齐。然后利用获取的信息生成 AR 指令，从而减少拆卸过程中用户频繁交互的需要。为了验证 AR 辅助拆卸系统的可行性，对报废汽车动力电池进行了实验。结果表明，这种方法大大提高了拆卸效率，降低了拆卸错误的频率。因此，研究结果表明，所提出的方法是有效的，有望用于大规模工业回收和拆卸作业。
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Pub Date : 2023-11-21DOI: 10.3390/machines11121038
Daniel Adeleye, Mohammad Seyedi, F. Ferdowsi, Jonathan Raush, Ahmed Khattab
With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over the years. Testing also must consider the process of manufacturing. This paper focuses its efforts on the finished product and not the process of manufacturing. Ultrasonic testing is a type of nondestructive testing. The experiments performed in this study aim to explore the usefulness of ultrasonic testing in materials that are 3D printed. The two materials used in this study are steel alloy metals and aluminum blocks of the same dimensions—120 mm × 40 mm × 15 mm. These materials represent common choices in additive manufacturing processes. The chosen alloys, such as Aluminum (6063T6) and grade-304 stainless steel, possess distinct properties crucial for validating the proposed testing method. Metal 3D-printed materials play a pivotal role in diverse industries, since ensuring their structural integrity is imperative for reliability and safety. Testing is crucial to identify and mitigate defects that could compromise the functionality and longevity of the final products, especially in applications with demanding performance requirements. An ultrasonic transducer is used to scan for subsurface defects within the samples and an oscilloscope is used to analyze the signals. Furthermore, several Machine Learning (ML) techniques are used to estimate the severity of the defects. The application of Machine Learning methods in the manufacturing industry has proven advantageous in terms of detecting defects due to its practicality and wide application. Due to their distinct benefits in processing image information, convolutional neural networks (CNNs) are the preferred method when working with picture data. In order to perform binary and multi-class classification, support vector machines that employ the alternative kernel function are a viable option for processing sensor signals and picture data. The study reveals that ultrasonic tests are viable for metallic materials. The primary objective of this work is to evaluate and validate the application of ultrasonic testing for the inspection of 3D-printed steel alloy metals and aluminum blocks. The novelty lies in the integration of Machine Learning techniques to estimate defect severity, offering a comprehensive and non-invasive approach to quality assessment in 3D-printed materials. The proposed method can successfully detect the presence of internal defects in objects, as well as estimate the location and severity of the defects.
随着 3D 打印技术在生产领域的发展，不可避免地需要质量保证，以确保最终产品符合要求。此外，3D 打印可使用的材料种类也在逐年增加。测试还必须考虑制造过程。本文的重点是成品，而不是制造过程。超声波检测是一种无损检测。本研究中进行的实验旨在探索超声波测试在 3D 打印材料中的实用性。本研究使用的两种材料是相同尺寸的钢合金金属和铝块--120 毫米 × 40 毫米 × 15 毫米。这些材料是增材制造工艺中的常见选择。所选合金，如铝（6063T6）和 304 级不锈钢，具有对验证所提议的测试方法至关重要的独特性能。金属 3D 打印材料在各行各业中发挥着举足轻重的作用，因为确保其结构完整性对可靠性和安全性至关重要。测试对于识别和减少可能影响最终产品功能和寿命的缺陷至关重要，尤其是在性能要求苛刻的应用中。超声波传感器用于扫描样品内部的次表面缺陷，示波器用于分析信号。此外，还使用了几种机器学习（ML）技术来估计缺陷的严重程度。在制造业中应用机器学习方法已被证明在检测缺陷方面具有优势，这得益于它的实用性和广泛应用。由于卷积神经网络（CNN）在处理图像信息方面具有明显优势，因此是处理图像数据的首选方法。为了进行二元和多类分类，采用替代核函数的支持向量机是处理传感器信号和图片数据的可行选择。研究表明，超声波测试适用于金属材料。这项工作的主要目的是评估和验证超声波测试在 3D 打印钢合金金属和铝块检测中的应用。新颖之处在于整合了机器学习技术来估计缺陷严重程度，为三维打印材料的质量评估提供了一种全面、非侵入性的方法。所提出的方法可以成功检测出物体内部缺陷的存在，并估算出缺陷的位置和严重程度。
{"title":"ML-Enabled Piezoelectric-Driven Internal Defect Assessment in Metal Structures","authors":"Daniel Adeleye, Mohammad Seyedi, F. Ferdowsi, Jonathan Raush, Ahmed Khattab","doi":"10.3390/machines11121038","DOIUrl":"https://doi.org/10.3390/machines11121038","url":null,"abstract":"With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over the years. Testing also must consider the process of manufacturing. This paper focuses its efforts on the finished product and not the process of manufacturing. Ultrasonic testing is a type of nondestructive testing. The experiments performed in this study aim to explore the usefulness of ultrasonic testing in materials that are 3D printed. The two materials used in this study are steel alloy metals and aluminum blocks of the same dimensions—120 mm × 40 mm × 15 mm. These materials represent common choices in additive manufacturing processes. The chosen alloys, such as Aluminum (6063T6) and grade-304 stainless steel, possess distinct properties crucial for validating the proposed testing method. Metal 3D-printed materials play a pivotal role in diverse industries, since ensuring their structural integrity is imperative for reliability and safety. Testing is crucial to identify and mitigate defects that could compromise the functionality and longevity of the final products, especially in applications with demanding performance requirements. An ultrasonic transducer is used to scan for subsurface defects within the samples and an oscilloscope is used to analyze the signals. Furthermore, several Machine Learning (ML) techniques are used to estimate the severity of the defects. The application of Machine Learning methods in the manufacturing industry has proven advantageous in terms of detecting defects due to its practicality and wide application. Due to their distinct benefits in processing image information, convolutional neural networks (CNNs) are the preferred method when working with picture data. In order to perform binary and multi-class classification, support vector machines that employ the alternative kernel function are a viable option for processing sensor signals and picture data. The study reveals that ultrasonic tests are viable for metallic materials. The primary objective of this work is to evaluate and validate the application of ultrasonic testing for the inspection of 3D-printed steel alloy metals and aluminum blocks. The novelty lies in the integration of Machine Learning techniques to estimate defect severity, offering a comprehensive and non-invasive approach to quality assessment in 3D-printed materials. The proposed method can successfully detect the presence of internal defects in objects, as well as estimate the location and severity of the defects.","PeriodicalId":48519,"journal":{"name":"Machines","volume":"50 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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