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Relevant bibliographies by topics / Geometric accuracy of the machine

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Academic literature on the topic 'Geometric accuracy of the machine'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Geometric accuracy of the machine"

1

Morimoto, Yoshitaka, Keisuke Nakato, and Motoshi Gontani. "Accuracy Evaluation of 5-Axis Machining Center Based on Measurements of Machined Workpiece – Evaluation of Accuracy of 5-Axis Controlled Machining Center –." International Journal of Automation Technology 6, no. 5 (September 5, 2012): 675–81. http://dx.doi.org/10.20965/ijat.2012.p0675.

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A new method for evaluating the geometrical accuracy of a 5-axis Machining Center (MC) based on the measurement results of the machined workpiece has been developed. The strategy behind our method is to utilize, because of its accuracy, a Coordinates Measuring Machine (CMM) as a master gauge. Thus, the machine operator machines the workpiece and a technologist of precise measurement takes the measurements. In our study, non-rotational machining is utilized to copy and trace the machine trajectory on the workpiece, minimizing the machining error. The profiles of the machined workpiece are measured and evaluated by a CMM, and the geometric errors of the machining center are extracted. Themeasurement results are sufficiently accurate compared to those taken using the square master gauge calibrated by CMM previously. In this report, the machining setup, including workpiece design, tool design, and cutting conditions, is proposed, and the experimental procedures and results of the evaluation are given.

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Holub, Michal, and Jiri Rosenfeld. "Geometric Accuracy of Large Machine Tools." Acta Mechanica Slovaca 24, no. 3 (September 22, 2020): 56–62. http://dx.doi.org/10.21496/ams.2020.036.

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Hoang, Trung Kien, and Nguyen Minh Duc Ta. "Machining Based Geometric Error Estimation Method for 3-Axis CNC Machine." Applied Mechanics and Materials 889 (March 2019): 469–74. http://dx.doi.org/10.4028/www.scientific.net/amm.889.469.

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Computer numerical control (CNC) machine tool plays an extremely significant role in any manufacturing industry due to its automation and high accuracy. Keeping the CNC machine tool at its highest performance to meet the demand of high accuracy machining is always significant. To maintain the accuracy of a machine tool over the time, it is important to measure and compensate the geometric error, one of the main error source of machine tool, especially when the machine get old. There are totally 21 geometrical errors in a 3-axis machine tool including three translational errors and three rotational errors for each axis and three perpendicular error (Squareness) within three axes of the machine. This paper presents an economical and simple method for measuring the geometric error of a 3-axis CNC machine tool based on the machining of actual samples. Three samples for each axis will be machined following a design cutting path. The samples will then be measured using a coordinate measuring machine (CMM). The collect data will be used for estimating the geometric errors. The volumetric errors will be then computed and verified through machining of 3D geometries.

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Jian, Yi, Qian Qian Li, Hong Cheng, Bin Wu Lai, and Jian Fei Zhang. "Research on Geometric Error Compensating Technique of CNC P3G Grinding Machine." Advanced Materials Research 462 (February 2012): 287–94. http://dx.doi.org/10.4028/www.scientific.net/amr.462.287.

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Kinematic accuracy is a key reason which influence workpiece's geometric error precision on traditional working process of precisely CNC（Computerized Numerical Control）P3G（polygon profile with 3 lobes） grinding machine. A systematic geometric error model has been presented for CNC P3G grinding machine， proposed multi-body system theory integrate with the structure of CNC P3G grinding machine tools, researched on the machine's space geometric errors. By means of separate geometric errors from the machine tools, build geometric mathematical error model. Then, identify 21 error parameters through method of 9 lines, analysis and calculate the total space geometric errors of the workpiece and wheel. Finally, formed a parameter-list and applied software error compensational technique ， achieved real-time control to the motions of workpiece and wheel. Experimental results shown that the geometrical error modeling technique is accurate and efficient, and the precision of CNC P3G grinding machine is highly raised 70%.

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Czerech, Łukasz. "SELECTION OF OPTIMAL MACHINING STRATEGY IN THE MANUFACTURE OF ELEMENTS BOUNDED BY CURVILINEAR SURFACES." Acta Mechanica et Automatica 7, no. 1 (March 1, 2013): 5–10. http://dx.doi.org/10.2478/ama-2013-0001.

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Abstract Increasing machining accuracy realized on CNC machine tools causes that the more frequently surfaces machined with this technique are not subject to further finishing processing and directly affects on the final quality of the product. Achieving geometric accuracy established by the constructor is the problem that modern technologists and CAD/CAM programmers have to faced with. The paper presents the influence of toolpath tolerance and machining strategy available in CAD/CAM software on the constituting process of technological surface layer for elements limited with curvilinear surfaces. The impact of the above mentioned parameters on the location and direction of geometrical deviations were also analyzed. Following article is part of research of the impact of selected technological parameters on the freeform surfaces geometric structure manufactured on CNC machines

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IBARAKI, Soichi. "How Accurate is the Motion? : Geometric Accuracy Measurement for Machine Tools." Journal of the Society of Mechanical Engineers 118, no. 1164 (2015): 672–75. http://dx.doi.org/10.1299/jsmemag.118.1164_672.

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Yang, Lin, Jin Yuan Wang, Rui Nan Wu, and Wei Wu. "Machine Slideway Wear to the Precision of the Whole Machine Impact." Applied Mechanics and Materials 229-231 (November 2012): 2474–77. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2474.

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This paper studies the influence of machine slideway precision on the overall accuracy of machine tools, and puts forward the root causes of the decline in accuracy of machine tool guide is rail wear, the decline of rail accuracy will impact on the adjacent unit cell, thus decline the accuracy of the machined parts; Finally takes floor-type milling & boring machine as research object, bases on geometric error models of the theory of multi-body system to describe the spindle box rail wear trends and establish relationship between the spindle box rail wear and the machine precision.

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Cheng, Qiang, Hongwei Zhao, Zhifeng Liu, Cui Zhang, and Peihua Gu. "Robust geometric accuracy allocation of machine tools to minimize manufacturing costs and quality loss." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 15 (August 9, 2016): 2728–44. http://dx.doi.org/10.1177/0954406215600398.

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With increasing demands of machining accuracy, designing of machine tools for satisfactory performance using cost-effective geometric accuracy configurations is becoming a complex problem to the machine tool manufacturers. In this paper, a novel robust accuracy allocation method is proposed for multi-axis machine tools based on multi-objective quality and cost trade-offs. To model the volumetric accuracy of machine tool based on geometric errors, the multi-body system theory was introduced. A manufacturing cost model for the machine tool components with a significant effect on geometric errors was established based on the machining features. The quality loss of the machine tool was also integrated into a single optimization objective. After identifying the relationship between the accuracy grade parameters of the feeding components and the geometric errors, the maximum in the Euclidean norm of all the accuracy parameters was defined as another optimization objective. The robust accuracy allocation was performed using Isight software and the Non-Dominated Sorting Genetic Algorithm-II built in the MATLAB. The optimization results for a four-axis horizontal machining center showed that the proposed method can realize the optimization of geometric accuracy and can determine the optimal accuracy grade of the feeding components satisfying the machining accuracy requirements.

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KIKKAWA, Koichi, Masaki HIROSAWA, Hidekazu KIKUCHI, Yoshio MIZUGAKI, and Hisanobu TERAI. "3362 Dependency of Working Accuracy on Location of Truncated Conical Workpiece Machined by 5-axis Controlled Machine Tool with Geometric Error." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2011.6 (2011): _3362–1_—_3362–4_. http://dx.doi.org/10.1299/jsmelem.2011.6._3362-1_.

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Li, Dianxin, Pingfa Feng, Jianfu Zhang, Dingwen Yu, and Zhijun Wu. "An identification method for key geometric errors of machine tool based on matrix differential and experimental test." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 17 (March 11, 2014): 3141–55. http://dx.doi.org/10.1177/0954406214527272.

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This paper presents a key geometric errors identification method for machine tools based on matrix differential and experimental test. An error model for a machine tool was established by regarding the three-axis machining center as a multi-body system. The sensitivity coefficients of the machining error with respect to the geometric errors were determined using the matrix differential method, and the degree of influence of the geometric errors on the machining accuracy under ideal conditions was discussed. Using the 12-line method, 21 geometric errors of the machine tool were identified, allowing the three-dimensional volumetric error distributions of the machine tool to be mapped. Experimental results allow the degree of influence of the geometric errors on the machining accuracy under actual conditions to be confirmed. Finally, the key geometric errors affecting the machining accuracy were identified by a combination of matrix differential and experimental test. This paper provides guidance for the machine tool configuration design, machining technology determination, and geometric error compensation.

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Dissertations / Theses on the topic "Geometric accuracy of the machine"

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Cross, P. M. "An analysis of the geometric instability of steady supported grinding." Thesis, University of Bristol, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384445.

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Fletcher, Simon. "Computer aided system for intelligent implementation of machine tool error reduction methodologies." Thesis, University of Huddersfield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368312.

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Suchomel, Kamil. "Vlivy výrobních technologií na geometrickou a rozměrovou přesnost obrobků." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-319263.

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The aim of the diploma thesis is to describe influences of selected production technologies on geometrical and dimensional accuracy of workpieces. Specific technologies - turning, milling and grinding and the machines on which these technologies will be implemented - a vertical lathe, a portal milling machine and a center grinder are described in this thesis. Additionally a procedure is created for adding geometric errors to a tri-axial machine tool to describe the entire working area of the machine in terms of errors. Subsequently an analysis of each geometric error is created for each machine and their influence on the resulting workpiece is determined.

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Vala, Michal. "Testování technologie on-the-fly přístrojem LaserTRACER." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-319262.

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This diploma thesis describes realization and testing on-the-fly measurement using tracking interferometer. This measurement enables to achieve higher amount of measured points and decrease time of machine tool calibration. Calibration and verification values of machine parameters, as well as time of measurement and other details of this method are compared with results of conventional method called trigger. To evaluate the quality of volumetric compensation created from calibration values, we use verification measurement of circular interpolation.

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Velič, Samuel. "Měření obrobků na obráběcích strojích pomocí obrobkové sondy." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417728.

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This diploma thesis deals with the influence of electronic compensation on accuracy of the measurement using the touch probe. The theoretical part concerns touch probes, specifically their classification, principles of working and applications. This part also includes devices used later in the experiment, particularly double ballbar, laser interferometer and LaserTRACER. The Practical part describes an experiment, demonstrating the correlation between the accuracy improvement of the touch probe and the electronic compensation’s level. All measurements were executed in accordance with the norm VDA 5 and were evaluated by the Matlab software. The graphs of reliance tolerance of capability of measurement on the length of the benchmark. At the end of this diploma thesis there are partial conclusions deduced. The deduction is based on generated graphs. The partial conclusions synoptically compare the influence of individual machine´s configurations on the accuracy of the touch probe.

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Pokorný, Zdeněk. "Zpracování dat z měření na obráběcích strojích." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382195.

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The Master’s thesis deals with the precision of machine tools, especially with the three-axis vertical milling machine. It is structurally divided into a theoretical and practical part. While the theoretical part focuses on problems of geometric errors and measuring devices, the practical one is devoted to the machine tool being tested. In this case, the data processing and the proposed statistical analysis are essential, and at the end of the thesis another possible way of developing the data processing is outlined.

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Únar, Jan. "Posouzení geometrické přesnosti obráběcího centra pomocí digitálních inklinometrů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444307.

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This diploma thesis deals with assessment of geometric accuracy of machining center MCV 754 QUICK. BlueSYSTEM digital inclinometers from WYLER AG, XL-80 laser interferometer from RENISHAW, Ballbar QC20-W from RENISHAW and LaserTRACER self-guiding laser interferometer from ETALON AG were used to measure accuracy. Error of the X straightness in the direction of the Z axis was assessed. The first part of the paper describes the geometric accuracy of the machine, currently available instruments for measuring geometric accuracy and an explanation of straightness. The second part consists of the design of measurements, experiment, evaluation and comparison of results and recommendations for teaching.

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Mišún, Filip. "Návrh automatického cyklu pro posuzování způsobilosti měření obrobkovou sondou na obráběcím centru." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443252.

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This diploma thesis describes the procedures of individual electronic compensations for CNC machines used to increase the accuracy of machines. This part is followed by the determining tolerance of the measuring and the influence of individual electronic compensations based on measured data using touch probe and calibrated standards according to VDA 5 norm.

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Lintott, Andrew B. "Geometric modelling and accuracy enhancement of parallel manipulators." Thesis, University of Canterbury. Mechanical Engineering, 2000. http://hdl.handle.net/10092/6159.

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The geometric analysis and accuracy enhancement of parallel topology robots is of theoretical interest as well as having potential for application in industrial settings where parallel robots are used for tasks in which positional accuracy is important. This work presents new techniques for modelling of closed loop mechanisms and applies these experimentally in the calibration of a real parallel topology robot: The Delta robot. This work extends the current literature on serial robot calibration into the realm of parallel robots and presents a systematic approach which does not require the model or solution technique to be adjusted for the particular geometry of the robot under investigation. The geometric modelling technique is quite general although in its current implementation it is only capable of modelling mechanisms that have rotary joints. Theory is presented for modelling of prismatic joints and the model can be adapted to handle joints of any type. The model analyses the structure as if it were a tree of bodies, each connected by a rotary or prismatic joint. A method of calculating the derivatives of the body frame positions with respect to the geometric parameters is also given. A defining characteristic of parallel topology mechanisms is that the kinematic chains form closed loops. Finding the joint configuration that has all loops properly closed is a non-linear minimisation problem referred to as the closure problem. This is solved using the Levenberg-Marquardt technique. For analysis of errors in a robot that is already assembled, an experimental calibration procedure is necessary. This procedure compares measured endpoint positions with those predicted by the geometric model and attempts to find a set of parameters that minimises these differences. The calibration procedure that was developed was tested on a number of simulated robots and a working Delta robot, which was designed and built specially for the calibration experiment. The mechanical design of the robot, software and hardware design of the robot controller, and the software implementation of the modelling and calibration procedures are described. It was found that the modelling, identification, and implementation methods worked successfully on the robots examined, but that the implementation process was too slow for use in a practical controller because of the need to perform multiple direct geometric solutions. The computational effort required for the implementation procedure was considerable, but use of a compiled computer language and optimised code would provide significant improvement.

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Dimitriadis, Alexis. "Assessing the dosimetric and geometric accuracy of stereotactic radiosurgery." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/813888/.

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Stereotactic radiosurgery (SRS) is a non-invasive treatment predominantly used for the management of malignant and benign brain tumours. The treatment can be delivered by various platforms in a single fraction where a high dose of radiation is delivered to the target whilst the surrounding healthy tissue is spared. This requires a high degree of accuracy in terms of the dose level delivered but also in terms of geometric precision. The purpose of this work was to identify the variations of SRS practice in the UK and develop a novel method compatible with all practices, capable of assessing the accuracy of delivery. The motivation behind this e↵ort was to contribute to safety in SRS delivery, provide confidence through a quality assurance audit and form a basis to support standardisation in SRS. A national survey was performed to investigate SRS practices in the UK and to help guide the methodology of this thesis. This resulted to the development of a method for an end-to-end audit of SRS. This was based on an anthropomorphic head phantom with a medium sized target located centrally in the brain, in close proximity to the brainstem. This realistic patient scenario was presented to all 26 radiosurgery centres in the UK who were asked to treat it with SRS. The dose delivered was assessed using two novel commercially available radiation detectors, a plastic scintillator and radiochromic film. These detectors were characterised for measuring the dose delivered in SRS. Another established dosimetry system, alanine, was also used alongside these detectors to assess the accuracy of each delivery. The results allowed the assessment of SRS practices in the UK and the comparison of all centres that participated in the audit. The results were also used to evaluate the performance of the dosimeters used for the purposes of quality assurance measurements and audit.

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Books on the topic "Geometric accuracy of the machine"

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T, Portman V., and Dunaevsky V. V, eds. Accuracy of machine tools. New York: ASME Press, 1988.

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Kanatani, Kenʼichi. Geometric computation for machine vision. Oxford: Clarendon Press, 1993.

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Song, Soh Gim, ed. Geometric design of linkages. 2nd ed. New York: Springer, 2010.

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Marciniak, Krzysztof. Geometric modelling for numerically controlled machining. Oxford [England]: Oxford University Press, 1991.

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Postlethwaite, Scott R. Electronic based accuracy enhancement of CNC machine tools. Huddersfield: The Polytechnic, 1992.

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Institution, British Standards. Accuracy of machine tools and methods of test. London: BSI, 1988.

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Institution, British Standards. Accuracy of machine tools and methods of test. London: BSI, 1988.

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Institution, British Standards. Accuracy of machine tools and methods of test. London: BSI, 1988.

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Jywe, W. A computer-aided accuracy testing device for machine tools. Manchester: UMIST, 1992.

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Robinson, Mark John. Limits to the accuracy of dimensional measurement using machine vision. Manchester: University of Manchester, 1994.

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Book chapters on the topic "Geometric accuracy of the machine"

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Holub, Michal. "Geometric Accuracy of Machine Tools." In Materials Forming, Machining and Tribology, 89–112. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03822-9_3.

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Regus, Michal, Bartosz Gapinski, Piotr Czajka, and Piotr Jablonski. "Studies of Geometric Accuracy of Polygons Machined by Polygonal Turning Technique." In Lecture Notes in Mechanical Engineering, 777–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68619-6_75.

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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics, 367–400. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Accuracy." In Encyclopedia of Machine Learning, 9–10. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_3.

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Kanatani, Kenichi, Yasuyuki Sugaya, and Yasushi Kanazawa. "Accuracy of Geometric Estimation." In Guide to 3D Vision Computation, 213–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48493-8_14.

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Moroni, Giovanni, and Stefano Petrò. "Coordinate Measuring Machine Measurement Planning." In Geometric Tolerances, 111–58. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-311-4_4.

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Liang, Steven Y., and Albert J. Shih. "Machine Tool Accuracy and Metrology." In Analysis of Machining and Machine Tools, 95–111. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7645-1_6.

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Sra, Suvrit, and Reshad Hosseini. "Geometric Optimization in Machine Learning." In Algorithmic Advances in Riemannian Geometry and Applications, 73–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45026-1_3.

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Dobkin, David, and Dimitrios Gunopulos. "Geometric problems in machine learning." In Applied Computational Geometry Towards Geometric Engineering, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0014490.

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Dai, Junfei, Wei Luo, Shing-Tung Yau, and Xianfeng David Gu. "Geometric Accuracy Analysis for Discrete Surface Approximation." In Geometric Modeling and Processing - GMP 2006, 59–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802914_5.

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Conference papers on the topic "Geometric accuracy of the machine"

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Holub, Michal, and Josef Knobloch. "Geometric accuracy of CNC machine tools." In 2014 16th International Conference on Mechatronics - Mechatronika (ME). IEEE, 2014. http://dx.doi.org/10.1109/mechatronika.2014.7018268.

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Etesami, Faryar, and Tim Griffin. "Characterizing the Accuracy of FDM Rapid Prototyping Machines for Machine Design Applications." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64972.

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This paper presents experiment-based formulas that predict the geometric accuracy of Fused Deposition Modeling (FDM) rapid prototyping (RP) machines in a form suitable for fit and function applications. The proposed method establishes the accuracy of several base-level geometry characteristics such as line straightness and circularity through direct measurements. Statistical analyses are used to establish reliable prediction formulas for the base-level geometry characteristics. The base-level accuracy measures are then used to develop models for higher-level geometric accuracies such as orientation, position, or profile accuracy. The objective of the methods proposed in this paper is to help FDM manufacturers develop reliable measures of accuracy for their machines in a form that is directly applicable to mechanical design applications. The proposed procedure is simple enough that companies that own and use FDM machines can quickly develop in-house accuracy measures that are specifically relevant to their machines. This study does not make any claims regarding the absolute accuracy of FDM machines in general. Rather, the study suggests a methodology for characterizing the accuracy of existing machines or production models in a manner useful to fit and function design work. The methodology applies to other rapid prototyping technologies as well.

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Liu, Xinyu. "Experimental Investigation of Micro-Milling Accuracy Using On-Machine Measurement System." In ASME 2010 International Manufacturing Science and Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/msec2010-34245.

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In this paper, we proposed an error decomposition method, in which some major error components can be experimentally estimated using an on-machine measurement (OMM) system. Unlike the conventional machining, the geometric error of a micro-tool, i.e., the deviation of the effective tool diameter from a nominal value, becomes a dominant factor affecting the machining accuracy. The error stems from both the tool fabrication error and the dynamic runout of the spindle system under high rpm. An on-machine measurement system based on a non-contact confocal laser sensor was developed that can accurately and efficiently acquire the effective tool diameter. To compensate the error due to the tool geometric error, the effective tool diameter was used to replace the nominal tool diameter to generate the tool path. The experimental results showed that the tool geometric error contributes over 50% to the total machining error. After compensation, the machining accuracy was significantly improved. The machine contour error has negligible influence on the dimension error of the machined feature, but it affects the form error, such as circularity of a machined hole. The process induced surface location errors were estimated from both experiments and model simulations, and good match was achieved.

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Qi, Baobao, Qiang Cheng, Zhifeng Liu, and Dongyang Sun. "To Identify Key Geometric Errors of 3-Axis NC Machine Tool by Machining Accuracy Failure Mode Analysis." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97016.

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Abstract Machine tools usually cut two or more surfaces after the work piece clamped on work table. In order to improve the machining accuracy and optimize accuracy design, it is hoped that the geometric errors that influence the accuracy of machined surface prominently can be known beforehand, so the adjustment will be carried out with a definite objective rather than without any clue. Because the machining accuracy of each direction in 3-D space is different value, in this paper, machining accuracy failure mode was defined as the various combination of the machining accuracy of each direction according to whether it is up to the reserved objective value or not. A three-axis machine tool was selected as an example and there were 7 machining accuracy failure modes for it. Based on the generalized correlation analysis, the correlation relationships between 7 machining accuracy failure modes were analyzed, and the main failure modes that affect the machining accuracy of work piece to be machined were identified. For each machining accuracy failure mode, key geometric error that had major influence on it was identified based on sensitivity analysis. Finally, four stepped work pieces were milled by a 3-axis machine tool to illustrate the analytical method proposed in this study.

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Kim, Gee-Hong, Seung-Woo Kim, and SangGun Lim. "Automatic inspection of geometric accuracy of optical fiber ferrules by machine vision." In Optics and Optoelectronic Inspection and Control: Techniques, Applications, and Instruments, edited by Shulian Zhang and Wei Gao. SPIE, 2000. http://dx.doi.org/10.1117/12.403848.

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Shaowei Zhu, Guofu Ding, Lei Jiang, Yu Peng, Shuwen Ma, and Kaiyin Yan. "Machining accuracy improvement of five-axis machine tools by geometric error compensation." In International Conference on Advanced Technology of Design and Manufacture (ATDM 2010). IET, 2010. http://dx.doi.org/10.1049/cp.2010.1321.

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Decker, Nathan, and Qiang Huang. "Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-3050.

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Abstract While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.

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Lee, Tae Hun, Jan Behrens, Sascha Gierlings, and Christian Brecher. "Approach for Zero Defect Manufacturing: Geometric Calibration of Five-Axis Machine Tools for Blisk Manufacturing Process." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-77195.

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Five-axis machining is a key technology of blisk manufacturing process. Blisks generally require high accuracy due to their high performance and safety-critical conditions. However, recent research show that the design of the blisks and turbine blades are getting more complex and require even higher accuracy. This leads also to the application of wide and rare area of movement axes of the machine. Thus, the machine accuracy has to be assured within the overall machine volume. The geometric accuracy demonstrates the base accuracy of the machine. This paper presents a geometric calibration method optimized for the axes movement area of blisk machining process. The accurate calibration of the five-axis machine tool is challenging and hardly possible due to limited error measurement of standard measurement devices. Some measurement methods enable complete calibration of the machine but with complex, time-consuming process and expensive measurement devices. Also, due to the rare axes travel, there is no standard calibration method for the blisk machining process. The calibration method in this paper is developed based on so called ‘R-test’ method. The machine and the errors are modelled mathematically for the measurement. An adapter is applied for the measurement of maximum axis positions. Automation units are developed for the full machine integration and automation of calibration procedure. With the developed method, the machine is calibrated from 130 μm to 10 μm in maximum measurement time of 90 minutes. The calibration quality is validated at an independent measurement position with continuous movement of the five axes.

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Lafarge, Florent, Renaud Keriven, and Mathieu Brédif. "Combining meshes and geometric primitives for accurate and semantic modeling." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.38.

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Li, Zongze, Ryuta Sato, and Keiichi Shirase. "Sensitivity Analysis of Error Motions and Geometric Errors in the Case of Sphere-Shaped Workpiece." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9610.

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Abstract Motion error of machine tool feed axes influences the machined workpiece accuracy. However, the influences of each error sources are not identical; some errors do not influence the machined surface although some error have significant influences. In addition, five-axis machine tools have more error source than conventional three-axis machine tools, and it is very tough to predict the geometric errors of the machined surface. This study proposes a method to analyze the relationships between the each error sources and the error of the machined surface. In this study, a kind of sphere-shaped workpiece is taken as a sample to explain how the sensitivity analysis makes sense in ball-end milling. The results show that the method can be applied for the axial errors, such as motion reversal errors, to make it clearer to obverse the extent of each errors. In addition, the results also show that the presented sensitivity analysis is useful to investigate that how the geometric errors influence the sphere surface accuracy. It can be proved that the presented method can help the five-axis machining center users to predict the machining errors on the designed surface of each axes error motions.

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Reports on the topic "Geometric accuracy of the machine"

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Kirka, Michael, Yousub Lee, Daniel Ryan, and David Adair. Assessment of High Gamma Prime Ni-base Superalloy Processability, Geometric Accuracy, and Component Fabricability. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1659625.

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Klingman, J., and R. Kegg. Accuracy Enhancement of High-Productivity Machine Tools Final Report CRADA No. TC-0161-91. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1430940.

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Gartlehner, Gerald, Gernot Wagner, Linda Lux, Lisa Affengruber, Andreea Dobrescu, Angela Kaminski-Hartenthaler, and Meera Viswanathan. Assessing the Accuracy of Machine-Assisted Abstract Screening With DistillerAI: A User Study. Methods Research Report. Agency for Healthcare Research and Quality (AHRQ), November 2019. http://dx.doi.org/10.23970/ahrqepcmethmachinedistiller.

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Martinez, Carianne, John P. Korbin, Kevin Matthew Potter, Emily Donahue, Jeremy David Gamet, and Matthew David Smith. Investigating Machine Learning Based X-Ray Computed Tomography Reconstruction Methods to Enhance the Accuracy of CT Scans. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1571551.

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Burkett, J. L., D. W. Newcom, Thomas J. Baas, Clint R. Schwab, and Kenneth J. Stalder. Effect of Technician, Machine, and Animal Body Composition on Accuracy of Ultrasonic Measures of Backfat and Loin Muscle Area in Swine. Ames (Iowa): Iowa State University, January 2004. http://dx.doi.org/10.31274/ans_air-180814-933.

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Gates, Allison, Michelle Gates, Shannon Sim, Sarah A. Elliott, Jennifer Pillay, and Lisa Hartling. Creating Efficiencies in the Extraction of Data From Randomized Trials: A Prospective Evaluation of a Machine Learning and Text Mining Tool. Agency for Healthcare Research and Quality (AHRQ), August 2021. http://dx.doi.org/10.23970/ahrqepcmethodscreatingefficiencies.

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Background. Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We prospectively evaluated an online machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. Methods. For 75 randomized trials published in 2017, we manually extracted and verified data for 21 unique data elements. We uploaded the randomized trials to ExaCT, an online machine learning and text mining tool, and quantified performance by evaluating the tool’s ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by ExaCT (simulating semi-automated data extraction). We summarized the relevance of the extractions for each data element using counts and proportions, and calculated the median and interquartile range (IQR) across data elements. We calculated the median (IQR) time for manual and semiautomated data extraction, and overall time savings. Results. The tool identified the reporting (reported or not reported) of data elements with median (IQR) 91 percent (75% to 99%) accuracy. Performance was perfect for four data elements: eligibility criteria, enrolment end date, control arm, and primary outcome(s). Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88 percent (83% to 99%) of cases. Performance was perfect for four data elements: funding number, registration number, enrolment start date, and route of administration. Among a median (IQR) 90 percent (86% to 96%) of relevant sentences, pertinent fragments had been highlighted by the system; exact matches were unreliable (median (IQR) 52 percent [32% to 73%]). A median 48 percent of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 hours total extraction time across 75 randomized trials). Conclusions. Using ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool’s ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required.

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Griffin, Andrew, Sean Griffin, Kristofer Lasko, Megan Maloney, S. Blundell, Michael Collins, and Nicole Wayant. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.

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Prediction of geometric-thermal machine tool errors by artificial neural networks. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5367.

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