Relevant bibliographies by topics / Process machine

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Process machine'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Process machine"

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Agrisa, Herick Henci. "An Overview of process CNC Machining." Journal of Mechanical Science and Engineering 6, no. 2 (July 8, 2020): 029–33. http://dx.doi.org/10.36706/jmse.v6i2.32.

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This paper discusses the pre and process of running a computer numerical control machine (CNC) using computer-aided design (CAD) software commonly used to design products to be produced and computer-aided manufacture (CAM) software used to control machines during the manufacturing process. Some types of CNC machines in general, namely CNC lathe machine and CNC milling machine. The history of the development of the CNC Machine was begun in 1952 by John Pearseon of the Massachusetts Institute of Technology on behalf of the United States Air Force, which aims to make complicated special workpieces. In addition, this paper also discusses the basic numerical code types used in CNC machines.
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Mekid, Samir. "Micro Machining Issues: Design and Machining Process." Advanced Materials Research 739 (August 2013): 238–44. http://dx.doi.org/10.4028/www.scientific.net/amr.739.238.

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Numerical controlled high speed micromachining on desktop machines is known to induce inherently new types of errors and machining issues at micro scale. The sources of these errors are either not known, or difficult to be modeled. Some of these errors are due to the downscaling effect of machine elements to a small micro-machine. This paper attempts to explore key issues proper to micro machines that are different from standard scale NC machine tools. Experimental observations are shown to support the discussion. The knowledge gained from these phenomena is used to nurture the design methodologies of micro-scale machines, to plan a suitable tool path and hence to improve the micromachining quality. This will also ascertain the statement that direct downscaling of current machine tools is worthless.
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MATSUBARA, Atsushi, Motoyuki SUGIHARA, Ahmed A. D. SARHAN, Hidenori SARAIE, Soichi IBARAKI, and Yoshiaki KAKINO. "Research on Spindle and Machining Process Monitoring for Intelligent Machine Tools(Advanced machine tool)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.2 (2005): 469–74. http://dx.doi.org/10.1299/jsmelem.2005.2.469.

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Toska, Ferit. "Strict process machine complexity." Archive for Mathematical Logic 53, no. 5-6 (March 20, 2014): 525–38. http://dx.doi.org/10.1007/s00153-014-0378-7.

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MORITA, Shin-ya, Takuya HOSOBATA, Masahiro TAKEDA, Hao DUAN, Ryoshu FURUTANI, and Yutaka YAMAGATA. "1514 Development of on-machine measurement system by in-process position monitoring of ultraprecision machine tool." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1514–1_—_1514–4_. http://dx.doi.org/10.1299/jsmelem.2015.8._1514-1_.

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Gajera, Vijay M., and Chetan Swarrop. "Process Modeling and Simulation of Abrasive Jet Machine-A Review." Indian Journal of Applied Research 4, no. 6 (October 1, 2011): 1–3. http://dx.doi.org/10.15373/2249555x/june2014/186.

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Dr. K, Nagi Reddy. "PRIOR PREDICTION AND IMPEDIMENT OF CANCER USING MACHINE LEARNING PROCESS." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 28, 2020): 278–89. http://dx.doi.org/10.37200/ijpr/v24i4/pr201008.

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Rahman, M., A. B. M. A. Asad, Takeshi Masaki, Y. S. Wong, and H. S. Lim. "A08 A MULTI-PROCESS MACHINE TOOL FOR HYBRID MICRO MACHINING." Proceedings of The Manufacturing & Machine Tool Conference 2008.7 (2008): 31–32. http://dx.doi.org/10.1299/jsmemmt.2008.7.31.

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Kuznetsova, Elena, Ludmila Tipner, and Alexey Ershov. "Restrictions of process machine retooling at machine-building enterprises." SHS Web of Conferences 35 (2017): 01081. http://dx.doi.org/10.1051/shsconf/20173501081.

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Scantamburlo, Teresa. "Machine learning in decisional process." ACM SIGCAS Computers and Society 45, no. 3 (January 5, 2016): 218–24. http://dx.doi.org/10.1145/2874239.2874270.

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Dissertations / Theses on the topic "Process machine"

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Wilmot, Wessley. "Process and machine improvements and process condition monitoring for a deep-hole internal milling machine." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/process-and-machine-improvements-and-process-condition-monitoring-for-a-deephole-internal-milling-machine(2bb87f60-aa39-4fff-a82a-9360ce36b74c).html.

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Milling is a widely used cutting process, most commonly applied to machining external surfaces of workpieces. When machining operations are required within hard to reach areas of components, or deep within the bore of components, alternative methods of metal removal are generally employed. Typically when milling at extended reaches, difficulties may increase exponentially when trying to achieve distances several meters into a component. Essentially every topic of the milling process becomes difficult and more convoluted. Firstly to generate a stable cutting condition, and ultimately for an operator to be able to understand the cutting conditions, when all normal senses to interpret the machining stability are removed. The aim for the research is, to enable the operation of high slenderness ratio internal milling operations to become a viable technology, by detailing the measures required, to obtain a stable cutting condition. The process needs to be monitored for degradation of the tooling due to wear, and to prevent catastrophic machine damage from tool breakage or machine component failure. This research addresses the lack of knowledge available for milling with extended reaches, and the knowledge gained to overcome the real difficulties that exist for this process. Initial experiments are conducted on a prototype machine to gain experience of the internal machining operation and the many issues that it faced. Establishing requirements of the process via investigation of the tooling and necessary auxiliary equipment, it becomes possible to consider countermeasures to address the errors generated by torsional twisting of the milling arm. A system for applying a counter torque to reduce torsional deflection errors has been employed to successfully reduce the unavoidable issue over such long distances. For the process to become manageable for an industrial operator without a high level of specialist knowledge, the application of tool condition monitoring (TCM) and process condition monitoring (PCM) had to be applied. This addresses a void in available literature and research with respect to internal machining, and enables the process to become practical for an industrial environment. For this reason the research project will concentrate on the application of TCM and PCM onto the machining system. The completion of the research resulted in the process becoming satisfyingly stable, and with a resulting accuracy that satisfies the requirements of the component. Performance of the final system rivalled or achieved better results than had been experienced by the project sponsor.
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Hack, Byron Wallis John 1963. "Man to machine, machine to machine and machine to instrument interfaces for teleoperation of a fluid handling laboratory." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276764.

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The purpose of this thesis is the design of the software necessary for teleoperation of a fluid handling laboratory. It does not include the implementation of this software. The laboratory for which it is designed is being developed at the University of Arizona, and is a model of the fluid handling laboratory aboard Space Station. The software includes man/machine, machine/machine, and machine/instrument interfaces. The man/machine interface is menu driven and consists of high level commands which are independent of the devices in the laboratory. The machine/machine interface is also device independent. It consists of intermediary commands and maps the commands of the man/machine interface into the low level, device dependent, commands and programs of the machine/instrument interface. Although the software is primarily designed for the model laboratory, the needs of a remotely operated fluid handling laboratory aboard Space Station have been considered.
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Habli, Nada. "Nonparametric Bayesian Modelling in Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34267.

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Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced by Ferguson (1983), its conjugacy property allows a tractable posterior inference which has lately given rise to a significant developments in applications related to machine learning. Another yet widespread prior used in nonparametric Bayesian inference is the Beta process and its extensions. It has originally been introduced by Hjort (1990) for applications in survival analysis. It is a prior on the space of cumulative hazard functions and it has recently been widely used as a prior on an infinite dimensional space for latent feature models. Our contribution in this thesis is to collect many diverse groups of nonparametric Bayesian tools and explore algorithms to sample from them. We also explore machinery behind the theory to apply and expose some distinguished features of these procedures. These tools can be used by practitioners in many applications.
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Weyrauch, Thomas. "The Paperboard Testing-Machine : Development Process." Thesis, KTH, Hållfasthetslära (Inst.), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-92639.

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The design of a paperboard testing machine, developed in order to analyse the mechanical behaviour of paperboard during the combined of out-of-plane shear and compressive loading as in the deepdrawing process, is presented. The methodology to design a paperboard testing machine is discussed and the most appropriate concepts are compared and evaluated. The design process is presented in detail, and some pilot tests are performed to give an overview about the functionality of the manufactured prototype.
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Davis, Tyler A. "Flexible machine tool control for direct, in-process dimensional part inspection /." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd478.pdf.

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UTTAM, SANGEET. "OPTIMAL TOLERANCE SYNTHESIS FOR PROCESS PLANNING WITH MACHINE SELECTION." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990648438.

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Zhang, Weijie. "Machine-human Cooperative Control of Welding Process." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/45.

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An innovative auxiliary control system is developed to cooperate with an unskilled welder in a manual GTAW in order to obtain a consistent welding performance. In the proposed system, a novel mobile sensing system is developed to non-intrusively monitor a manual GTAW by measuring three-dimensional (3D) weld pool surface. Specifically, a miniature structured-light laser amounted on torch projects a dot matrix pattern on weld pool surface during the process; Reflected by the weld pool surface, the laser pattern is intercepted by and imaged on the helmet glass, and recorded by a compact camera on it. Deformed reflection pattern contains the geometry information of weld pool, thus is utilized to reconstruct its $3$D surface. An innovative image processing algorithm and a reconstruction scheme have been developed for (3D) reconstruction. The real-time spatial relations of the torch and the helmet is formulated during welding. Two miniature wireless inertial measurement units (WIMU) are mounted on the torch and the helmet, respectively, to detect their rotation rates and accelerations. A quaternion based unscented Kalman filter (UKF) has been designed to estimate the helmet/torch orientations based on the data from the WIMUs. The distance between the torch and the helmet is measured using an extra structure-light low power laser pattern. Furthermore, human welder's behavior in welding performance has been studied, e.g., a welder`s adjustments on welding current were modeled as response to characteristic parameters of the three-dimensional weld pool surface. This response model as a controller is implemented both automatic and manual gas tungsten arc welding process to maintain a consistent full penetration.
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Loustaunau, Matthieu. "Industrial process error estimation by machine learning." Thesis, KTH, Numerisk analys, NA, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-178069.

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Performing a set-up on a complex machine may be difficult. This problem arises frequently for the industry, especially when the relation between input and output data cannot be defined precisely. Heavy methods of optimization may be used to perform a set-up. This master thesis investigate the possibility to use a machine learning approach on a specific machine. We study the structure of the relation between input and output data. We show the variations are smooth. We define a set of tests to evaluate future models. We design and test several models on simulation data, and select the best one. We design a strategy to use data in the best possible way. The selected model is then tested on actual data in order to be optimized.
Att justera ett komplext maskineri kan vara mödosamt. Detta problem uppkommer ofta i industrin när relationen mellan in- och utdata inte kan definieras. Tunga optimeringsmetoder kan användas för justeringprocessen. Dennauppsats undersöker möjligheten att använda en s.k. machine learning approach med ett specifikt maskineri. Vi studerar datastrukturen och relationen mellan in- och utdata. Vi visar på att variationer är släta. Vi definierar en rad av tester för att värdera framtida modeller. Vi formger och testar flera modeller med simulerade data och välja den bästa. Vi designar en strategi för att använda data på bästa sätt, för att sedan testa den utvalda modellen på verklig data för att optimeras.
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Hecker, Rogelio Lorenzo. "Power feedback control in cylindrical grinding process." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/16619.

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El-Mounayri, Hazim A. "Generic solid modelling based machining process simulation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ30083.pdf.

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Books on the topic "Process machine"

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Denkena, Berend, and Ferdinand Hollmann, eds. Process Machine Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32448-2.

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Dietrich, Edgar. Statistical procedures for machine and process qualification. 6th ed. Cincinnati: Munich Hanser Publications, 2010.

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Dietrich, Edgar. Statistical procedures for machine and process qualification. 6th ed. Cincinnati: Munich Hanser Publications, 2010.

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Elia, Alberto. Human-machine interface design for process control. Research Triangle Park, NC: Instrumentation, Systems, and Automation Society, 2009.

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Human-machine interface design for process control. Research Triangle Park, NC: Instrumentation, Systems, and Automation Society, 2009.

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Ullman, David G. The mechanicaldesign process. New York: McGraw-Hill, 1992.

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Baeten, J. C. M. Process algebra. Cambridge: Cambridge University Press, 1990.

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The mechanical design process. 3rd ed. Boston, Mau: McGraw-Hill, 2003.

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Ullman, David G. The mechanical design process. New York: McGraw-Hill, 1992.

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The mechanical design process. 4th ed. Dubuque, IA: McGraw-Hill, 2009.

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Book chapters on the topic "Process machine"

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Shen, Wei-Min. "The Process of Discovery." In Machine Discovery, 233–51. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-2124-0_4.

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dirichlet Process." In Encyclopedia of Machine Learning, 280–87. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_219.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, Zhao Xu, Yaakov Engel, Claude Sammut, Mark Reid, et al. "Gaussian Process." In Encyclopedia of Machine Learning, 428–39. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_324.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Markov Process." In Encyclopedia of Machine Learning, 646. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_516.

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Lim, Yongseob, Ravinder Venugopal, and A. Galip Ulsoy. "Machine Control." In Process Control for Sheet-Metal Stamping, 41–51. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-6284-1_4.

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Utgoff, Paul E., James Cussens, Stefan Kramer, Sanjay Jain, Frank Stephan, Luc De Raedt, Ljupčo Todorovski, et al. "Inductive Process Modeling." In Encyclopedia of Machine Learning, 537. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_397.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Process-Based Modeling." In Encyclopedia of Machine Learning, 805. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_671.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, Zhao Xu, Yaakov Engel, Claude Sammut, Mark Reid, et al. "Gaussian Process Reinforcement Learning." In Encyclopedia of Machine Learning, 439–47. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_325.

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Quadrianto, Novi, Kristian Kersting, and Zhao Xu. "Gaussian Process." In Encyclopedia of Machine Learning and Data Mining, 1–13. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_108-1.

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Quadrianto, Novi, Kristian Kersting, and Zhao Xu. "Gaussian Process." In Encyclopedia of Machine Learning and Data Mining, 535–48. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_108.

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Conference papers on the topic "Process machine"

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Yip-Hoi, Derek, and Debasish Dutta. "Issues in Computer-Aided Process Planning for Parallel Machine Tools." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0303.

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Abstract Parallel machines represent a new generation of machine tool. Through reducing the number of setups both the efficiency and the accuracy of the machining process is increased within the part domain. While Flexible Manufacturing Systems (FMSs) and Machining Cells (MCs) are said to be agile, the parallel machine is the first stand-alone machine which can claim to have this property. This makes them ideally suited for machining small batch sizes and for rapid prototyping. Unfortunately like FMSs and MCs these machines will be largely underutilized if agile data generation, processing and transfer mechanisms are not incorporated into CAD/CAM systems. One major hurdle to achieving this objective is the development of an automatic process planning system for parallel machines. This presents new challenges beyond those encountered in process planning for sequential machining. In this paper we discuss two aspects of parallel machines which impact on process planning. These are (1) the part domain for parallel machines and (2) the machine configuration.
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Sheu, D. Daniel, and Chao-En Cheng. "Time-based machine-process grouping." In Industrial Engineering (CIE39). IEEE, 2009. http://dx.doi.org/10.1109/iccie.2009.5223735.

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Oziemski, Stanislaw. "Process Automation versus Machine Dependability." In 12th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 1995. http://dx.doi.org/10.22260/isarc1995/0043.

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Gansemer, Sebastian, Jonas Sell, Simon Schulz, Bettina Horster, Thomas Horster-Moller, Christian Rusch, Albert Pauls, Uwe Grobmann, and Evren Eren. "Machine-to-machine communication for process optimization in agriculture." In 2014 2nd International Symposium on Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS-SWS). IEEE, 2014. http://dx.doi.org/10.1109/idaacs-sws.2014.6954622.

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Otsubo, Tatsuki, Takanori Yazawa, Jinhui Wang, and Tomonori Kato. "Diamond Fly Cutting Applied to Improve Curved Surface Machining by In-Process Measurement and Control on an Ordinary Milling Machine." In JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/lemp2020-8590.

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Abstract To improve the accuracy of the machined surface produced by an ordinary milling machine, a system called workpiece-referred form accuracy control (WORFAC) was developed and confirmed in diamond turning. However, non-rotational symmetric surface structures, such as V-grooves, pyramid structures, F-theta lenses, and other free form surface cannot be machined by diamond turning. We proposed to improve the form accuracy of a machined surface produced by an ordinary milling machine by diamond fly cutting using controlled cutting with reference surface (CCRS), an in-process measurement and control method. Fly cutting is usually used to manufacture ultra-precision microstructures with nanometric surface roughness and submicrometric form accuracy, without the need for subsequent polishing. Nevertheless, a high level of accuracy has only recently been achieved on ultra-precision milling machines. In this study, we verified the effectiveness of fly cutting with CCRS on an ordinary milling machine. CCRS improves machined surface accuracy by controlling the relative displacement between the tool and workpiece. Diamond fly cutting using CCRS was demonstrated to reduce the table motion error on an ordinary milling machine. The experiments of curved surface machining by uncontrolled machining and control machining were conducted, and the effectiveness of improving the circular are machining accuracy of the general-purpose milling machine was confirmed.
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Harker, Roger G. "Understanding Machinery Management — Using Machine Condition and Process Information for Maximum Benefit." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0059.

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The use of machinery protection systems is well-established in industry. These systems shut down a machine or return it to a safe or nondestructive mode of operation without human intervention. Protection systems are used to ensure personnel safety, prevent or minimize machine damage, and limit environmental impact. While these are substantial benefits, machinery protection remains inherently reactive, and the approach is still necessary, but no longer sufficient in today’s economic environment. Machinery management is increasingly recognized for its ability to indicate machinery condition before a protection system’s alarm levels are exceeded. By providing an early warning of what is happening with the machine, proactive decisions can be made about how machinery assets are run and how they affect the plant. Machinery management requires the correlation of critical process data with vibration and position data. It allows event correlation and effective root cause analysis. Information resulting from effective machinery management is actionable because it results from continuous online audits of machinery, and indicates when action is required and how best to take it. Machinery management helps answer key questions, such as: How long can this machine run? Can the process be adjusted to extend the operation and avoid a shutdown? If you want to proactively manage machinery and optimize machinery performance along with the process, you must have an effective machinery management program. This paper explores the practice of machinery management, including what it is, what is considered “best practice,” and what the resulting benefits are.
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Hooker, Kevin, Marco Guajardo, Nai-Chia Cheng, and Guangming Xiao. "Implementing Machine Learning for OPC retargeting." In Design-Process-Technology Co-optimization for Manufacturability XIV, edited by Chi-Min Yuan and Ryoung-Han Kim. SPIE, 2020. http://dx.doi.org/10.1117/12.2552402.

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Ma, Yuansheng, Feng Wang, Qian Xie, Le Hong, Joerg Mellmann, Yuyang Sun, Shaowen Gao, Sonal Singh, Panneerselvam Venkatachalam, and James Word. "Machine learning based wafer defect detection." In Design-Process-Technology Co-optimization for Manufacturability XIII, edited by Jason P. Cain and Chi-Min Yuan. SPIE, 2019. http://dx.doi.org/10.1117/12.2513232.

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Zrncic, Stjepko, Iva Bojic, Damjan Katusic, Pavle Skocir, Mario Kusek, and Gordan Jezic. "Quality-of-Service in Machine-to-Machine service provisioning process." In 2013 21st International Conference on Software, Telecommunications and Computer Networks - (SoftCOM 2013). IEEE, 2013. http://dx.doi.org/10.1109/softcom.2013.6671899.

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Bender, Harald, Mikko Kolehmainen, Gerald Lehmann, Janne Parantainen, Markus Staufer, and Marcos Tong. "Business transformation of the provisioning process for Machine-to-Machine." In 2011 15th International Conference on Intelligence in Next Generation Networks (ICIN): "From Bits to Data, from Pipes to Clouds". IEEE, 2011. http://dx.doi.org/10.1109/icin.2011.6081068.

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Reports on the topic "Process machine"

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Hinderliter, S. D. Automated screw machine process planning. Office of Scientific and Technical Information (OSTI), February 1990. http://dx.doi.org/10.2172/5039907.

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Reifman, J., G. E. Graham, T. Y. C. Wei, K. R. Brown, and R. Y. Chin. Flexible human machine interface for process diagnostics. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/224751.

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Johnston, Derek. Installation and Implementation of an In-Process Coordinate Measuring Machine (CMM). Office of Scientific and Technical Information (OSTI), June 2008. http://dx.doi.org/10.2172/952956.

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Hardy, B., A. D'Entremont, B. Garcia-Diaz, L. Roy, M. Martinez-Rodriguez, T. Clingenpeel, and J. Bakos. PROCESS IMAGE ANALYSIS USING BIG DATA, MACHINE LEARNING, AND COMPUTER VISION. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1568782.

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HARDY, BRUCE, ANNA D'ENTREMONT, MICHAEL MARTINEZ-RODRIGUEZ, BRENDA GARCIA-DIAZ, LINDSAY ROY, JASON BAKOS, TAYLOR CLINGENPEEL, et al. PROCESS IMAGE ANALYSIS USING BIG DATA, MACHINE LEARNING, AND COMPUTER VISION. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1676412.

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Brandy, Herbert T., and David E. Gilsinn. PIECS - a software program for machine tool Process-Intermittent Error Compensation. Gaithersburg, MD: National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.ir.5797.

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Bretherton, Christopher, Po-Lun Ma, and Peter Caldwell. Transforming ESM Physical Parameterization Development Using Machine Learning Trained on Global Cloud-Resolving Models and Process Observations. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769790.

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PITNER, A. L. Primary Clean Machine Process Validation for Mark 4 Fuel & Fuel in Aluminum Canisters Test Plan & Test Specification (OCRWM). Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/807484.

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Kramer, Thomas R. Process plan expression, generation, and enhancement for the Vertical Workstation milling machine in the Automated Manufacturing Research Facility at the National Bureau of Standards. Gaithersburg, MD: National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.ir.87-3678.

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Thompson, Kathleen A. Optimization of NLC machine parameters for specific physics processes. Office of Scientific and Technical Information (OSTI), October 1999. http://dx.doi.org/10.2172/15065.

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