Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology最新文献

{"title":"Generation of cubic Hermite spline-based trochoidal milling toolpath by introducing a coefficient factor in machining curved slots","authors":"Liping Wang ,&nbsp;Huiqing Gu ,&nbsp;Kean Guo","doi":"10.1016/j.precisioneng.2024.08.001","DOIUrl":"10.1016/j.precisioneng.2024.08.001","url":null,"abstract":"<div><p>Trochoidal milling has become popular in high-speed milling due to its ability to reduce cutting force, enhance thermal dissipation, and prolong tool life. Among other toolpath patterns, the cubic Hermite spline offers significant advantages in generating trochoidal milling toolpath in machining complex and curved slots. However, determining those two different relation coefficients in the polynomial function of cubic Hermite spline remains complicated and empirical. This paper introduces a coefficient factor to simplify the determination process, which only requires the position and tangent vector parameters of each endpoint pair. The coefficient factor combines an instantaneous in-process slot width (IISW)-related width factor and a tangent vector direction-related direction factor. Two complex-curved slots are used to validate the performance of the proposed method. The results show that this method is straightforward and effective in generating trochoidal milling toolpaths for machining complex-curved slots while matching the slot geometry. Additionally, compared with a direct 5-axis trochoidal milling (one-step slotting strategy) in trochoidal milling of 3D slots on both serial and hybrid machining tools, a two-step slotting strategy (3-axis trochoidal milling and a successive 5-axis perpetual milling) is preferable in improving machining efficiency.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"90 ","pages":"Pages 38-55"},"PeriodicalIF":3.5,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tool inclination angle designing for low-deformation and high-efficiency machining of thin-wall blade based on edge-workpiece-engagement","authors":"Dongju Chen ,&nbsp;Shuiyuan Wu ,&nbsp;Jianqiang Wu ,&nbsp;Ri Pan ,&nbsp;Jinwei Fan ,&nbsp;Yuhang Tang","doi":"10.1016/j.precisioneng.2024.07.015","DOIUrl":"10.1016/j.precisioneng.2024.07.015","url":null,"abstract":"<div><p>The maximum flexibility direction(MFD) is solved for establishing the deformation estimation criterion of the blade during machining process, and the larger the milling force in MFD, the bigger the deformation of the blade during machining process. A new milling force calculation algorithm based on edge-workpiece-engagement(EWE) is proposed by analyzing the contact process between the ball-end milling tool and the chip. The chip thickness of each tool element of each edge curve at any rotation angle is calculated, the milling force of MFD and SLDs under different inclination angles are calculated. The average absolute milling force in MFD reaches the minimum value of 0.65126N when the lead/tilt angle is 30°/-45°, and the maximum machining efficiency is reached when the lead/tilt angle is 45°/-45°.The optimized tool inclination angle, which is 30°/-45° for lead/tilt angle, is obtained by solving the multi-objective optimization problem of machining deformation represented by relative deformation and machining efficiency represented by relative stable machining area. The effectiveness of the proposed scheme is verified by comparison of simulation and experiment for blade machining error under two kinds of tool orientations.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"90 ","pages":"Pages 21-37"},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting error for machining thin-walled blades considering initial error","authors":"Rui Zhang ,&nbsp;Junxue Ren ,&nbsp;Jinhua Zhou ,&nbsp;Tong Han ,&nbsp;Pei Wang","doi":"10.1016/j.precisioneng.2024.07.010","DOIUrl":"10.1016/j.precisioneng.2024.07.010","url":null,"abstract":"<div><p>A multistage machining process is employed to machine the blades with low stiffness. Nonetheless, machining errors can be transferred and accumulate throughout the multistage machining process, complicating the precise prediction of the final accuracy of thin-walled blades. Consequently, this paper introduces a machining accuracy prediction model for thin-walled blades that takes into account initial error. The machining error prediction model of thin-walled blades is developed using Gaussian process regression optimized by the sparrow search algorithm (SSA-GPR) with the initial contour error, depth of cut, feed per tooth, and spindle speed as inputs, and the machining error as the output. And the results show that the prediction accuracy of the SSA-GPR is 6.73 % higher than that of the <span>Gaussian</span> process regression (GPR), 13.73 % higher than that of the back propagation neural network (BPNN), and 32.32 % higher than that of the support vector machine regression (SVR). The influence of the initial error and milling parameters on the machining error is analyzed through the length-scales of the Gaussian kernel function. The findings indicate that the depth of cut, feed per tooth and initial error significantly affect the machining error, whereas the spindle speed has a minor impact on the machining error. Furthermore, the 3D graph based on the SSA-GPR shows that the increase of the initial error will increase the machining error of thin-walled blades. This research provides a theoretical foundation for the process optimization of thin-walled blades.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"89 ","pages":"Pages 492-503"},"PeriodicalIF":3.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional long-short term memory predictor for material removal rate in computer-controlled optical surfacing","authors":"Ke Chen ,&nbsp;Bo Xiao ,&nbsp;XueLian Liu ,&nbsp;ChunYang Wang ,&nbsp;ShuNing Liang","doi":"10.1016/j.precisioneng.2024.07.006","DOIUrl":"10.1016/j.precisioneng.2024.07.006","url":null,"abstract":"<div><p>In Computer-Controlled Optical Surfacing technology, the precision of the removal function directly affects the accuracy of computer-aided processing software predictions, which in turn influences subsequent polishing machine processing. A key parameter for constructing the removal function is the material removal rate, which is often challenging to obtain accurately. Currently, the Preston equation is widely used to describe the principles of material removal. However, as a linear equation that omits many factors, it struggles to accurately model the removal function in complex machining scenarios. Therefore, this paper proposes a hybrid neural network model combining Convolutional Neural Networks and Bidirectional Long Short-Term Memory to predict the material removal rate. The model's parameters are optimized using an improved Grey Wolf Optimization algorithm, ultimately establishing a removal function closely consistent with an actual removal function. We first tested our method on the PHM2016 Data Challenge dataset, achieving a mean squared error of 6.19 and an R² of 0.9949, outperforming other mainstream neural network prediction models developed in recent years. Additionally, we further validated the performance of the neural network using a small grinding head polishing dataset, achieving MSE and R² values of 1.9035 and 0.99902, respectively. Finally, we applied this method to construct the removal function on an actual small grinding head production line. Compared to the traditional Preston equation-based removal function, the predicted residual surface's PV and RMS errors were reduced from 28.24 % to 35.58 %–4.563 % and 4.86 %, respectively. These validation results demonstrate that the proposed method not only facilitates easier acquisition of the removal function model but also significantly enhances the accuracy of computer-aided processing software predictions, thereby better guiding ultra-precision machining processes.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"89 ","pages":"Pages 473-491"},"PeriodicalIF":3.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-factor coupling analysis of electric field distribution in electrochemical machining with electrolyte suction tool","authors":"Chengwei Shen,&nbsp;Ji Wang,&nbsp;Ping Zhou,&nbsp;Ying Yan,&nbsp;Dongming Guo","doi":"10.1016/j.precisioneng.2024.07.011","DOIUrl":"10.1016/j.precisioneng.2024.07.011","url":null,"abstract":"<div><p>Electrochemical machining (ECM) technology holds significant potential for applications in high accuracy engineering and manufacturing. To confine the electrolyte region during ECM and mitigate the effects of reaction products and heat generation, an electrolyte suction tool has been proposed. However, the complex electric field distribution on the anode surface during the application of pulse voltage, limits the further utilization of ECM with suction tool. A multi-physics simulation model is established to calculate the region of electrolyte confinement by the suction tool, current density distribution within the electrolyte region, and material removal. The model considers the combined effects of polarization and double layer, and all key parameters are calibrated through classical electrochemical testing experiments rather than fitted based on the predicted results. This study elucidates the transient response of current density on the anode surface during the application of pulse voltage. The simulation accuracy is validated by comparing experimental and simulated current waveforms, as well as material removal profiles under different pulse parameters and electrode shapes. This research is of great significance for improving surface precision and controllability of complex structures in ECM with suction tool, promoting its further application in the field of precision machining.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"89 ","pages":"Pages 504-516"},"PeriodicalIF":3.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of adaptive backlash minimization in worm gear for precision positioning","authors":"Wojciech Kacalak,&nbsp;Jacek Ponomarenkow,&nbsp;Zbigniew Budniak,&nbsp;Monika Szada-Borzyszkowska,&nbsp;Katarzyna Tandecka","doi":"10.1016/j.precisioneng.2024.07.012","DOIUrl":"10.1016/j.precisioneng.2024.07.012","url":null,"abstract":"<div><p>The paper introduces innovative solutions for worm gears, aiming at adaptive reduction of side clearance to improve accuracy. It addresses manufacturing challenges affecting tooth dimensions and deviations in shape, examining the impact of adjustment settings on clearance components. The document outlines patented solutions and identifies the causes of inaccuracies in worm gears, with a specific focus on the efficiency of a compliant worm in reducing side clearance. Comprehensive analyses were conducted using Matlab, CAD, and empirical data to investigate the impact of adjustments on clearance statistics, correlation, and spectral analyses before and after corrections. It establishes correlations between clearance parameters and assesses positioning accuracy using a rolling screw system. By demonstrating significant improvements in accuracy without the need for gear disassembly during operation, the developed gear compensates for manufacturing deviations. The paper also suggests assembly procedures for precision worm gears in small-scale production, taking into account various criteria for accuracy assessment.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"89 ","pages":"Pages 517-530"},"PeriodicalIF":3.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0141635924001648/pdfft?md5=33702058ac9edb903f43b884023df033&pid=1-s2.0-S0141635924001648-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools","authors":"Guodong Sa ,&nbsp;Zhengyang Jiang ,&nbsp;Zhenyu Liu ,&nbsp;Jiacheng Sun ,&nbsp;Chan Qiu ,&nbsp;Liang He ,&nbsp;Jianrong Tan","doi":"10.1016/j.precisioneng.2024.07.013","DOIUrl":"10.1016/j.precisioneng.2024.07.013","url":null,"abstract":"<div><p>Thermal error significantly influences the accuracy of precision computer numerical control (CNC) machine tools. The key to compensating thermal error lies in selecting appropriate temperature measurement points and establishing an accurate error prediction model. Traditional methods separate measurement points selection and prediction modeling, that is, selecting temperature measurement points first and then establishing the prediction model using these points. These methods are difficult to achieve optimal matching between measurement points and the prediction model, resulting in shortcomings in modeling accuracy. To address these challenges, an integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools is proposed. A dual-stage attention-based long short-term memory combined with convolutional neural network (DA-CLSTM) error modeling method is proposed to accurately predict the thermal error for different numbers of temperature measurement points. Then, an integrated method of virtual-real temperature measurement points layout and error modeling is proposed, which ensures the temperature measurement points and the error prediction model are closely matched, offering rational sensors layout and high-accuracy prediction. Finally, experiments are conducted on the spindle system test bench to validate the method proposed in this research. Through integrated optimization of measurement points layout and the prediction model, the proposed method achieves high-accuracy thermal error prediction across various sensor counts and maintains reliable prediction ability when the number of sensors is reduced. This method is applied to a digital twin system of MKL7150 grinding machine, and effectively improves the accuracy of actual machining.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"90 ","pages":"Pages 1-11"},"PeriodicalIF":3.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of point cloud filtering on optical inspection of additive manufactured metal parts","authors":"Sara Giganto ,&nbsp;Susana Martínez-Pellitero ,&nbsp;Víctor Meana ,&nbsp;Eduardo Cuesta ,&nbsp;Joaquín Barreiro","doi":"10.1016/j.precisioneng.2024.07.014","DOIUrl":"10.1016/j.precisioneng.2024.07.014","url":null,"abstract":"<div><p>The characteristics of additively manufactured parts (topological optimisation, freedom of design …) present a challenge from a metrological point of view. In this respect, non-contact inspection is of great importance for the rapid verification of this type of parts. However, their particular characteristics (stair-effect, high roughness, surface finish) can contribute to measurement errors and deviations when using different optical inspection equipment. In relation to this, 3D scanning point-clouds filtering can improve the results by increasing precision during inspection. In particular, this paper evaluates the influence of applying filters to digitize additive manufactured metal parts, which are scanned using different optical inspection systems commonly used in industry (based on laser triangulation, conoscopic holography and structured light techniques). The main objective is to establish the most suitable inspection procedure depending on the inspection system used. According to the dimensional results achieved, it can be concluded that the filtering does not have significant influence, regardless of the used sensor. However, the geometrical results are strongly influenced by the point-cloud quality; consequently, it is recommended to apply filters for most of the evaluated optical systems. One of the main contributions of this study is the definition of the filtering process methodology prior to non-contact inspection, depending on both the used optical system and the part to be evaluated, in order to define the most suitable filtering parameter to achieve a precise optical inspection.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"90 ","pages":"Pages 12-20"},"PeriodicalIF":3.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the dynamic interaction between machine tools and their foundations","authors":"Paweł Dunaj ,&nbsp;Andreas Archenti","doi":"10.1016/j.precisioneng.2024.07.009","DOIUrl":"10.1016/j.precisioneng.2024.07.009","url":null,"abstract":"<div><p>The performance of a machine tool is directly influenced by the characteristics of the floor, subsoil, and their interaction with the installed machine. Installing a machine tool in its operational environment poses a distinct challenge that bridges mechanical and civil engineering disciplines. This interdisciplinary issue is often overlooked within the individual separate disciplines. However, effectively addressing this challenge requires a comprehensive understanding of mechanical and civil engineering principles. To address this problem, the present study proposes a method for improved modeling of the dynamic properties of the machine tool by considering the foundation and the subsoil on which it is installed. The method is based on finite element modeling. Linear models of the system components and the connections between them were used. These, supplemented with damping expressed by complex stiffness, made it possible to determine the natural frequencies, mode shapes, and frequency response functions (based on which the transmissibilities were obtained). Based on the experimentally verified models of vertical and horizontal lathes, the sensitivity analysis aimed at estimating the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation was conducted. Thus, it was possible to identify those machine tool-support-foundation-subsoil system parameters that had the most significant impacts on the vibration's transmissibility. After analyzing the cases discussed, it became evident that the transmissibility of vibrations is primarily influenced by two key factors. First and foremost, the properties of the structural loop of the machine tool played a significant role. Additionally, the characteristics of the subsoil on which the foundation was situated emerged as a crucial determinant in the observed vibration transmissibility.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"89 ","pages":"Pages 451-472"},"PeriodicalIF":3.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0141635924001612/pdfft?md5=9179952041e453163fefb490bb221b04&pid=1-s2.0-S0141635924001612-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal displacement prediction of high-speed electric spindles based on BWO-BiLSTM","authors":"Yaonan Cheng ,&nbsp;Shenhua Jin ,&nbsp;Kezhi Qiao ,&nbsp;Shilong Zhou ,&nbsp;Jing Xue","doi":"10.1016/j.precisioneng.2024.07.007","DOIUrl":"10.1016/j.precisioneng.2024.07.007","url":null,"abstract":"<div><p>To accurately, efficiently and stably predict the thermal displacement of the spindle, a prediction model based on the beluga whale algorithm (BWO) optimized bi-directional long and short-term memory neural network (BiLSTM) is introduced in this paper. Firstly, the thermal characterization and simulation analysis of the spindle are carried out, and the temperature and thermal displacement change characteristics of the spindle are obtained. Then the thermal deformation experiment of the spindle is carried out, and the temperature and displacement sensors are set up reasonably according to the temperature and thermal displacement change characteristics of the spindle, and the experimental data are collected and analyzed. The adaptive and globally convergent BWO is selected to optimize network parameters of BiLSTM, and the BWO-BiLSTM prediction model is constructed by learning the nonlinear correlation characteristics between spindle temperature and axial thermal displacement. The constructed BWO-BiLSTM prediction model is compared with other prediction models, and it is found through analysis that the prediction results output from the BWO-BiLSTM model have better accuracy and stability. The results of the study can provide a certain theoretical basis and technical support in predicting the spindle thermal displacement, which can help to promote the precision machining production of electric spindles.</p></div>","PeriodicalId":54589,"journal":{"name":"Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology","volume":"89 ","pages":"Pages 438-450"},"PeriodicalIF":3.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}