Academic literature on the topic 'Key process parameters'
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Journal articles on the topic "Key process parameters"
Bernacchi, Sébastien, Michaela Weissgram, Walter Wukovits, and Christoph Herwig. "Process efficiency simulation for key process parameters in biological methanogenesis." AIMS Bioengineering 1, no. 1 (2014): 53–71. http://dx.doi.org/10.3934/bioeng.2014.1.53.
Full textWett, B., S. Murthy, I. Takács, M. Hell, G. Bowden, A. Deur, and M. O'Shaughnessy. "Key Parameters for Control of DEMON Deammonification Process." Water Practice 1, no. 5 (November 12, 2007): 1–11. http://dx.doi.org/10.2175/193317707x257017.
Full textWett, B., S. Murthy, I. Tak´cs, M. Hell, G. Bowden, A. Deur, and M. O'Shaughnessy. "KEY PARAMETERS FOR CONTROL OF DEMON DEAMMONIFICATION PROCESS." Proceedings of the Water Environment Federation 2007, no. 2 (January 1, 2007): 424–36. http://dx.doi.org/10.2175/193864707787977181.
Full textMaiti, Syamal, Dipayan Das, and Kushal Sen. "Electrochemical Polymerization of Pyrrole: Key Process Control Parameters." Journal of The Electrochemical Society 159, no. 9 (2012): E154—E158. http://dx.doi.org/10.1149/2.050209jes.
Full textKrystynik, Pavel, and Duarte Novaes Tito. "Key process parameters affecting performance of electro-coagulation." Chemical Engineering and Processing: Process Intensification 117 (July 2017): 106–12. http://dx.doi.org/10.1016/j.cep.2017.03.022.
Full textMohammed, Seydaliyev Ilham. "Key Parameters Survey of the Magnetic System of the Electromagnetic Thickness Converter in the Process of Winding." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 944–52. http://dx.doi.org/10.5373/jardcs/v12sp1/20201145.
Full textCotton, D., A. Maillard, and J. Kaufmann. "Improved coining force calculations through incorporation of key process parameters." IOP Conference Series: Materials Science and Engineering 967 (November 19, 2020): 012003. http://dx.doi.org/10.1088/1757-899x/967/1/012003.
Full textHoffman, R. D., S. E. Woosley, and Y. Z. Qian. "Model independent r-process nucleosynthesis — Constraints on the key parameters." Nuclear Physics A 621, no. 1-2 (August 1997): 397–400. http://dx.doi.org/10.1016/s0375-9474(97)00278-9.
Full textMeng, Wen, and Guo-qun Zhao. "Effects of Key Simulation Parameters on Conical Ring Rolling Process." Procedia Engineering 81 (2014): 286–91. http://dx.doi.org/10.1016/j.proeng.2014.09.165.
Full textPan, Ri, Bo Zhong, Zhenzhong Wang, Shuting Ji, Dongju Chen, and Jinwei Fan. "Influencing mechanism of the key parameters during bonnet polishing process." International Journal of Advanced Manufacturing Technology 94, no. 1-4 (August 15, 2017): 643–53. http://dx.doi.org/10.1007/s00170-017-0870-4.
Full textDissertations / Theses on the topic "Key process parameters"
D'Costa, Aspen. "Characterization of key process parameters in injection blow molding for improving quality /." Available to subscribers only, 2007. http://proquest.umi.com/pqdweb?did=1456293971&sid=9&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Full textFenollosa, Artés Felip. "Contribució a l'estudi de la impressió 3D per a la fabricació de models per facilitar l'assaig d'operacions quirúrgiques de tumors." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667421.
Full textLa presente tesis doctoral se ha centrado en el reto de conseguir, mediante Fabricación Aditiva (FA), modelos para ensayo quirúrgico, bajo la premisa que los equipos para obtenerlos tendrían que ser accesibles al ámbito hospitalario. El objetivo es facilitar la extensión del uso de modelos como herramienta de preparación de operaciones quirúrgicas, transformando la práctica médica actual de la misma manera que, en su momento, lo hicieron tecnologías como las que facilitaron el uso de radiografías. El motivo de utilizar FA, en lugar de tecnologías más tradicionales, es su capacidad de materializar de forma directa los datos digitales obtenidos de la anatomía del paciente mediante sistemas de escaneado tridimensional, haciendo posible la obtención de modelos personalizados. Los resultados se centran en la generación de nuevo conocimiento para conseguir equipamientos de impresión 3D multimateriales accesibles que permitan la obtención de modelos miméticos respecto a los tejidos vivos. Para facilitar la buscada extensión de la tecnología, se ha focalizado en las tecnologías de código abierto como la Fabricación por Hilo Fundido (FFF) y similares basadas en líquidos catalizables. Esta investigación se alinea dentro de la actividad de desarrollo de la FA en el CIM UPC, y en este ámbito concreto con la colaboración con el Hospital Sant Joan de Déu de Barcelona (HSJD). El primer bloque de la tesis incluye la descripción del estado del arte, detallando las tecnologías existentes y su aplicación al entorno médico. Se han establecido por primera vez unas bases de caracterización de los tejidos vivos – principalmente blandos – para dar apoyo a la selección de materiales que los puedan mimetizar en un proceso de FA, a efectos de mejorar la experiencia de ensayo de los cirujanos. El carácter rígido de los materiales mayoritariamente usados en impresión 3D los hace poco útiles para simular tumores y otras referencias anatómicas. De forma sucesiva, se tratan parámetros como la densidad, la viscoelasticidad, la caracterización de materiales blandos en la industria, el estudio del módulo elástico de tejidos blandos y vasos, la dureza de los mismos, y requerimientos como la esterilización de los modelos. El segundo bloque empieza explorando la impresión 3D mediante FFF. Se clasifican las variantes del proceso desde el punto de vista de la multimaterialidad, esencial para hacer modelos de ensayo quirúrgico, diferenciando entre soluciones multiboquilla y de mezcla en el cabezal. Se ha incluido el estudio de materiales (filamentos y líquidos) que serían más útiles para mimetizar tejidos blandos. Se constata como en los líquidos, en comparación con los filamentos, la complejidad del trabajo en procesos de FA es más elevada, y se determinan formas de imprimir materiales muy blandos. Para acabar, se exponen seis casos reales de colaboración con el HJSD, una selección de aquellos en los que el doctorando ha intervenido en los últimos años. El origen se encuentra en la dificultad del abordaje de operaciones de resección de tumores infantiles como el neuroblastoma, y en la iniciativa del Dr. Lucas Krauel. Finalmente, el Bloque 3 desarrolla numerosos conceptos (hasta 8), actividad completada a lo largo de los últimos cinco años con el apoyo de los medios del CIM UPC y de la actividad asociada a trabajos finales de estudios de estudiantes de la UPC, llegándose a materializar equipamientos experimentales para validarlos. La investigación amplia y sistemática al respecto hace que se esté más cerca de disponer de una solución de impresión 3D multimaterial de sobremesa. Se determina que la mejor vía de progreso es la de disponer de una pluralidad de cabezales independientes, a fin de capacitar la impresora 3D para integrar diversos conceptos estudiados, materializándose una posible solución. Para cerrar la tesis, se plantea cómo sería un equipamiento de impresión 3D para modelos de ensayo quirúrgico, a fin de servir de base para futuros desarrollos.
Chung, Ming-ju, and 鍾明儒. "Study on key process parameters for laser-gelling rapid prototyping." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/71255337937398717291.
Full text龍華科技大學
工程技術研究所
100
Laser-gelling stacking techniques have been used in rapid prototyping for many years and its products are popular for cases with lower quality requirement. But, its products cannot be accepted for cases with higher quality requirement now, so some key process parameters of the techniques must be set appropriately to increase the quality of products created by the techniques. Therefore, this paper studies the control of scanning line widths, the homogenity for stacking layers of a product, the warpping of a product, the aperture distortion of remained holes, the repeatability of products, and the acceleration areas of scanning to find the appropriate setting values for some key process parameters. In all experiments, cases without appropriate setting and cases with appropriate setting are both performed. The created products show cases with appropriate setting are always better. The setting of key parameters can be done with setups using modified rapid prototyping and appropriate setting of key parameters may be a good way to make laser-gelling stacking techniques belong to precise machining.
Vatta, Laura Lisa. "Manufacture of ferrofluid : basic aspects and the influence of key parameters on the process." Diss., 2003. http://hdl.handle.net/2263/24792.
Full textYang, Chih-Cheng, and 楊志誠. "Key Parameters Optimization Applying Six Sigma Methodology and Artificial Neural Network to a Multi-Range Curvature Optical Surface Grinding Process." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/56484523864412368666.
Full text國立中央大學
光電科學研究所碩士在職專班
93
Surfacing technology for optical components has been well established for almost 250 years. The industry has continued to grow vigorously mostly because of new applications. Throughout the long development history, many different designs and materials were applied; however, the process and challenges of today remain similar to those experienced from the very beginning. Processed surface quality is one of the key factors in achieving good optical performance. Although the modern machines are now equipped with better capabilities, optimization of operation conditions by skilled technicians remains a requirement. Especially, the best control parameters require practices that include trade-offs during prototyping and production startup. Moreover, time-consuming trial and error methods based on experience remain a general practice. All optical components follow the same three-phase surfacing process in general including: 1st - Generating, 2nd - Fine grinding, (or smoothing), 3rd - Polishing. The Fine grinding phase was identified as the most critical process for production efficiency, quality yield, and component performance. Thus, this experimental design focuses on the fine grinding process. The experiment design in this paper applied the general use ophthalmic spherical power range as study case. The lenses design including meniscus concave lens for myopia correction and periscopic convex lens for hyperopia or presbyopia correction. There are total 57 sets curvature designs with 0.25D step international ophthalmic standard spherical power range form S-7.00D to S+7.00D. This paper shows how process optimization can be achieved in two steps, the first step using Six Sigma methodology gauges the surfacing process control in order to confirm the five general specified factors that are critical to the surfacing operation. A second effective method coupling the Taguchi experimental design and the most important improvement tools of Six Sigma methodology was then applied. The design plan is based on the use of orthogonal arrays introduced by Taguchi. Through the application of Taguchi’s signal-to-noise (S/N) ratio, we demonstrate that the best parameters design plan from an experimental design can be determined. This has several implications: (1) It reduces the implementation time, (2) it can identify a fractional design that contains the best design plan and that design plan could be studied for full experimentation, (3) within a subset of a fractional design plan, the best design point can be found, and (4) the cost of experimentation is significantly reduced since a minimal number of runs is required to identify the best design point. Finally, this important result helps experimenters to select a fractional design plan that contains the “best design point.” The experiment condition for example, it takes minimum﹙53 x 53 =15,625﹚15,625 experiment trials if using the traditional trial and error methods in order to find the optimal parameters. The fact, the results prove the optimal parameters can be found and confirmed with only (18 x 3 =54) 54 trials according to the design in this paper. The result shows it takes only 0.34% time if the same effect use the traditional trial and error non-specific methods. The traditional control parameters require practices which include trade-offs by skilled senior engineers who are required at this moment to make experiential judgments. In this article, optimized parameters are obtained by applying the mathematical exercise of Non-linear “Artificial Neural Network” to eliminate the subjective judgments. It replaces the errors caused from the experiential judgments made by the expert senior engineers. In terms of the production equipment control and adjustment ability of the newly recruited technician, their capability for exact and reasonable recognition of the production parameters set up is substantially improved. Moreover, the optimal parameters can be applied as the default factory setting in order to be utilized as the reference parameters for general production purposes.
Books on the topic "Key process parameters"
Apostolidi, Eftychia, Stephanos Dritsos, Christos Giarlelis, José Jara, Fatih Sutcu, Toru Takeuchi, and Joe White. Seismic Isolation and Response Control. Edited by Andreas Lampropoulos. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2021. http://dx.doi.org/10.2749/sed019.
Full textDepartment of Defense. F-15A Versus F/a-22 Fighter Aircraft Initial Operational Capability: A Case for Transformation - Test and Evaluation Process, Critical Issues, Key Performance Parameters, Langley Air Force Base. Independently Published, 2017.
Find full textJacobson, David M., and Giles Humpston. Principles of Brazing. ASM International, 2005. http://dx.doi.org/10.31399/asm.tb.pb.9781627083515.
Full textHumpston, Giles, and David M. Jacobson. Principles of Soldering. ASM International, 2004. http://dx.doi.org/10.31399/asm.tb.ps.9781627083522.
Full textPeach, Ken. Managing Projects. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198796077.003.0012.
Full textLowe, Ben, ed. Political Thought and the Origins of the American Presidency. University Press of Florida, 2021. http://dx.doi.org/10.5744/florida/9780813066813.001.0001.
Full textBraidotti, Rosi, and Patricia Pisters, eds. Revisiting Normativity with Deleuze. Bloomsbury Publishing Plc, 2012. http://dx.doi.org/10.5040/9781350275911.
Full textSobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.
Full textBelekar, R. M., Renu Nayar, Pratibha Agrawal, and S. J. Dhoble, eds. Water Pollution Sources and Purification: Challenges and Scope. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150506841220101.
Full textKaratasakis, G., and G. D. Athanassopoulos. Cardiomyopathies. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199599639.003.0019.
Full textBook chapters on the topic "Key process parameters"
Zhang, Ming Yu, Qi Zhong Huang, Zhe An Su, Zhi Yong Xie, and Bo Yun Huang. "Study on Process Parameters of Multi-Factor Coupling Fields CVI." In Key Engineering Materials, 1451–54. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-456-1.1451.
Full textAiyiti, Wurikaixi, Wan Hua Zhao, Yi Ping Tang, and Bing Heng Lu. "Study on the Process Parameters of MPAW-Based Rapid Prototyping." In Key Engineering Materials, 1931–34. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-456-1.1931.
Full textLerma Valero, José R. "Key Parameters for Setting the Injection Molding Process." In Plastics Injection Molding, 178–92. München: Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9781569906903.014.
Full textTao, Z., and Y. Gao. "Effects of Key Parameters on the Performance of a New In-Process Optical Measurement Method." In Key Engineering Materials, 405–10. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-977-6.405.
Full textYamazaki, Y., T. Kinebuchi, H. Fukanuma, N. Ohno, and K. Kaise. "Deformation and Fracture Behaviors in the Freestanding APS-TBC - Effects of Process Parameters and Thermal Exposure." In Key Engineering Materials, 1935–38. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-456-1.1935.
Full textLee, Chong Mu, Choong Mo Kim, Sook Joo Kim, and Yun Kyu Park. "Enhancement of the Quality of the ZnO Thin Films by Optimizing the Process Parameters of High-Temperature RF Magnetron Sputtering." In Key Engineering Materials, 581–84. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-410-3.581.
Full textAllanore, Antoine, Luis A. Ortiz, and Donald R. Sadoway. "Molten Oxide Electrolysis for Iron Production: Identification of Key Process Parameters for Largescale Development." In Energy Technology 2011, 121–29. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118061886.ch12.
Full textSoni, Rahul, Ravi Pratap Singh, and Shailender Singh Bhadauria. "Disquisition of Impact of Key Electric Discharge Machining Input Parameters on Various Process Characteristics: A Review." In Modern Manufacturing Systems, 369–86. New York: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003284024-30.
Full textSika, Robert, Adam Jarczyński, and Arkadiusz Kroma. "Methodology of Determination of Key Casting Process Parameters on DISA MATCH Automatic Moulding Line Affecting the Formation of Alloy-Mould Contact Defects." In Lecture Notes in Mechanical Engineering, 416–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16943-5_36.
Full textLiu, Kailong, Yujie Wang, and Xin Lai. "Data Science-Based Battery Manufacturing Management." In Data Science-Based Full-Lifespan Management of Lithium-Ion Battery, 49–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01340-9_3.
Full textConference papers on the topic "Key process parameters"
Zhu, Xiaofang, Jingyuan Che, Yulu Hu, Quan Hu, Bin Li, Tao Huang, Xiaolin Jin, and Li Xu. "Simulation Exploration of Assembly Process and Key Parameters of TWT." In 2020 IEEE 21st International Conference on Vacuum Electronics (IVEC). IEEE, 2020. http://dx.doi.org/10.1109/ivec45766.2020.9520520.
Full textWang, Zaiying, and Zhipeng Shangguan. "Coordinated Control of Key Process Parameters in Dense Medium Coal Preparation." In 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2019. http://dx.doi.org/10.1109/icitbs.2019.00124.
Full textBhagwat, Surbhi, and Vinod Kumar Mannaru. "Forging Process Modeling: Influence of Key Forging Process Parameters on Part Quality and Equipment Tonnage." In Symposium on International Automotive Technology 2017. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2017. http://dx.doi.org/10.4271/2017-26-0173.
Full textBin Wang, Mingzhe Yuan, Zhuo Wang, Haibin Yu, and Guang Zhu. "The development of soft measurement of key parameters in copper smelting process." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053138.
Full textBao, Zhenbo, Longxiang Yang, Jinxing Peng, and Fan Yang. "Analysis of Problems and Key Process Parameters in Household Biomass Gasification System." In 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/emim-17.2017.203.
Full textZhu, Xiaofang, Jingyuan Che, Yulu Hu, Quan Hu, Tao Huang, Li Xu, and Bin Li. "Computer Aided Simulation of Assembly Process and Key Assembly Parameters of TWT." In 2020 IEEE International Conference on Plasma Science (ICOPS). IEEE, 2020. http://dx.doi.org/10.1109/icops37625.2020.9717547.
Full textKinsey, Brad, Matt Bravar, and Jian Cao. "Methodology and Model to Determine Key Process Parameters for Tailor Welded Blank Forming." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-43610.
Full textLiu, Yuanqing, Ziyan Zhu, and Xianglin Zhu. "Soft sensor modeling for key parameters of marine alkaline protease MP fermentation process." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8408209.
Full textGuo, Hao, Lin Lin, Yancheng Lv, Jie Liu, and Changsheng Tong. "Machine Learning for Determining Key Parameters in Welding Process of Underground Engineering Equipment." In 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 2021. http://dx.doi.org/10.1109/sdpc52933.2021.9563365.
Full textByrne, Kelly, William Hawker, and James Vaughan. "Effect of key parameters on the selective acid leach of nickel from mixed nickel-cobalt hydroxide." In PROCEEDINGS OF THE 1ST INTERNATIONAL PROCESS METALLURGY CONFERENCE (IPMC 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4974412.
Full textReports on the topic "Key process parameters"
Kuropiatnyk, D. I. Actuality of the problem of parametric identification of a mathematical model. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2885.
Full textTidwell, Vincent Carroll, George A. Backus, Elena Arkadievna Kalinina, William J. Peplinski, and David Blaine Hart. Sensitivity of the Community Land Model (CLM4.0) to key modeling parameters and modeling of key physical processes with focus on the arctic environment. Office of Scientific and Technical Information (OSTI), August 2012. http://dx.doi.org/10.2172/1051726.
Full textZhang, Cheng, and Yue Yang. Impact of adaptive design on reducing the duration of clinical trials in rare cancers: a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0081.
Full textUngar, Eugene D., Montague W. Demment, Uri M. Peiper, Emilio A. Laca, and Mario Gutman. The Prediction of Daily Intake in Grazing Cattle Using Methodologies, Models and Experiments that Integrate Pasture Structure and Ingestive Behavior. United States Department of Agriculture, July 1994. http://dx.doi.org/10.32747/1994.7568789.bard.
Full textKeller, David P. Quantification of “constrained” potential of ocean NETs. OceanNets, 2022. http://dx.doi.org/10.3289/oceannets_d4.1.
Full textSmith, S. Jarrell, David W. Perkey, and Kelsey A. Fall. Cohesive Sediment Field Study : James River, Virginia. U.S. Army Engineer Research and Development Center, August 2021. http://dx.doi.org/10.21079/11681/41640.
Full textMorin, Shai, Gregory Walker, Linda Walling, and Asaph Aharoni. Identifying Arabidopsis thaliana Defense Genes to Phloem-feeding Insects. United States Department of Agriculture, February 2013. http://dx.doi.org/10.32747/2013.7699836.bard.
Full textYaron, Zvi, Abigail Elizur, Martin Schreibman, and Yonathan Zohar. Advancing Puberty in the Black Carp (Mylopharyngodon piceus) and the Striped Bass (Morone saxatilis). United States Department of Agriculture, January 2000. http://dx.doi.org/10.32747/2000.7695841.bard.
Full textLacerda Silva, P., G. R. Chalmers, A. M. M. Bustin, and R. M. Bustin. Gas geochemistry and the origins of H2S in the Montney Formation. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329794.
Full textPrusky, Dov, Nancy P. Keller, and Amir Sherman. global regulation of mycotoxin accumulation during pathogenicity of Penicillium expansum in postharvest fruits. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7600012.bard.
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