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Статті в журналах з теми "Manufacturing not elsewhere classified"
Garanča, Biruta. "THE STRUCTURE OF MACHINERY BUILDING IN LATGALE AND PERSPECTIVES OF ITS DEVELOPMENT." Latgale National Economy Research 1, no. 1 (June 30, 2009): 53. http://dx.doi.org/10.17770/lner2009vol1.1.1761.
Повний текст джерелаFarid, A. M., and D. C. McFarlane. "Production degrees of freedom as manufacturing system reconfiguration potential measures." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222, no. 10 (October 1, 2008): 1301–14. http://dx.doi.org/10.1243/09544054jem1056.
Повний текст джерелаBrown, Tiffany A., Pamela K. Keel, and Ruth H. Striegel. "Feeding and Eating Conditions Not Elsewhere Classified (NEC) inDSM-5." Psychiatric Annals 42, no. 11 (November 1, 2012): 421–25. http://dx.doi.org/10.3928/00485713-20121105-08.
Повний текст джерелаLouis, David N., Pieter Wesseling, Werner Paulus, Caterina Giannini, Tracy T. Batchelor, J. Gregory Cairncross, David Capper, et al. "cIMPACT-NOW update 1: Not Otherwise Specified (NOS) and Not Elsewhere Classified (NEC)." Acta Neuropathologica 135, no. 3 (January 25, 2018): 481–84. http://dx.doi.org/10.1007/s00401-018-1808-0.
Повний текст джерелаReynolds, Elisabeth B., and Hiram Samel. "Manufacturing Startups." Mechanical Engineering 135, no. 11 (November 1, 2013): 36–41. http://dx.doi.org/10.1115/1.2013-nov-2.
Повний текст джерела2015 Program Committee, BRASS. "From Committees of RUSA: BRASS Program: Not Elsewhere Classified: Researching New and Niche Industries." Reference & User Services Quarterly 55, no. 2 (December 16, 2015): 156. http://dx.doi.org/10.5860/rusq.55n2.156.
Повний текст джерелаBurford, C., R. Laxton, Z. Sidhu, M. Aizpurua, A. King, I. Bodi, K. Ashkan, and S. Al-Sarraj. "ATRX immunohistochemistry can help refine ‘not elsewhere classified’ categorisation for grade II/III gliomas." British Journal of Neurosurgery 33, no. 5 (April 24, 2019): 536–40. http://dx.doi.org/10.1080/02688697.2019.1600657.
Повний текст джерелаDakskobler, Igor, Andrej Martinčič, and Daniel Rojšek. "Phytosociological Analysis Of Communities With Adiantum Capillusveneris In The Foothills Of The Julian Alps (Western Slovenia)." Hacquetia 13, no. 2 (December 1, 2014): 235–58. http://dx.doi.org/10.2478/hacq-2014-0016.
Повний текст джерелаGoldberg, David. "Should our major classifications of mental disorders be revised?" British Journal of Psychiatry 196, no. 4 (April 2010): 255–56. http://dx.doi.org/10.1192/bjp.bp.109.072405.
Повний текст джерелаGangadhar, K., and D. Santhosh. "Primary Skull Osteosarcoma: MDCT Evaluation and Histopathological Correlation in Two Cases." Neuroradiology Journal 25, no. 2 (April 2012): 188–92. http://dx.doi.org/10.1177/197140091202500206.
Повний текст джерелаДисертації з теми "Manufacturing not elsewhere classified"
Zhang, Yumin. "Virtual manufacturing - a study of some important issues relating to the transformation of traditional manufacturing organisations." Thesis, Aston University, 2006. http://publications.aston.ac.uk/12237/.
Повний текст джерела(6661946), Jeremy Wayne Byrd. "PREDICTIVE MAINTENANCE PRACTICES & STANDARDS." 2019.
Знайти повний текст джерелаManufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.
The three major types of maintenance are reactive, preventive, and
predictive. The research project focused on applying the principles of
predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains
the techniques and principles of applying the technology currently available to
reduce downtime and maintenance cost. The predictive maintenance procedures and
saving are compared with reactive and preventive methods to determine a value
of return. The report will examine the benefits of using the Internet of Things
technology to create autonomous self-diagnosing smart machines. The predictive
maintenance plan in this research illustration will introduce health check
equipment used to implement longer lasting machine components. In conclusion,
the project developed out an entire predictive maintenance plan to reduce
downtime and maintenance costs.
(5930069), Mariana Moreno. "Robust Process Monitoring for Continuous Pharmaceutical Manufacturing." Thesis, 2019.
Знайти повний текст джерелаRobust process monitoring in real-time is a challenge for Continuous Pharmaceutical Manufacturing. Sensors and models have been developed to help to make process monitoring more robust, but they still need to be integrated in real-time to produce reliable estimates of the true state of the process. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this work, we present such a systematic framework, which for a given sensor network and measurement uncertainties will predict the most likely state of the process. As a result, real-time process decisions, whether for process control, exceptional events management or process optimization can be based on the most reliable estimate of the process state.
Data reconciliation (DR) and gross error detection (GED) have been developed to accomplish robust process monitoring. DR and GED mitigate the effects of random measurement errors and non-random sensor malfunctions. This methodology has been used for decades in other industries (i.e., Oil and Gas), but it has yet to be applied to the Pharmaceutical Industry. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. However, it requires the sensor network to be redundant (i.e., the number of measurements has to be greater than the degrees of freedom).
In this dissertation, the SSDR framework is defined and implemented it in two different continuous tableting lines: direct compression and dry granulation. The results for two pilot plant scales via continuous direct compression tableting line are reported in this work. The two pilot plants had different equipment and sensor configurations. The results for the dry granulation continuous tableting line studies were also reported on a pilot-plant scale in an end-to-end operation. New measurements for the dry granulation continuous tableting line are also proposed in this work.
A comparison is made for the model-based DR approach (SSDR-M) and the purely data-driven approach (SSDR-D) based on the use of principal component constructions. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. The reconciled measurement values generate using SSDR-M satisfy the model equations and can be used together with the model to estimate unmeasured variables. However, in the presence of nonlinearities, the SSDR-M and SSDR-D will differ. SSDR successfully estimates the real state of the process in the presence of gross errors, as long as steady-state is maintained and the redundancy requirement is met. Gross errors are also detected whether using SSDR-M or SSDR-D.
Hsiao, Yu-Chan Helen. "A framework of university incubator to maintain financial sustainability." 2008. http://arrow.unisa.edu.au:8081/1959.8/42985.
Повний текст джерела(8740677), Jeremy Sickmiller. "REAL TIME CONTROL OF MANUFACTURING UTILIZING A MANUFACTURING EXECUTION SYSTEM (MES)." Thesis, 2020.
Знайти повний текст джерела(9726050), Onkar V. Sonur. "The Sustainable Manufacturing System Design Decomposition." Thesis, 2020.
Знайти повний текст джерела(8065976), Kanjakha Pal. "Process Intensification Enabling Direct Compression for Pharmaceutical Manufacturing: From Spherical Agglomeration to Precise Control of Co-Agglomeration." Thesis, 2019.
Знайти повний текст джерела(8625390), Na Li. "HOW TO IMPLEMENT LEAN SIX SIGMA IN CHINA: A CASE STUDY OF THREE MANUFACTURING COMPANIES." Thesis, 2020.
Знайти повний текст джерелаLean Six Sigma (LSS) has been implemented worldwide for many years and has been successful in many organizations. Eloot, Huang, and Lehnich (2013) noted that achieving manufacturing excellence by using LSS was an opportunity for many companies. Liker and Rother (2011) pointed out that only 2% of companies successfully achieved the desired results with Lean plans.
The presented dissertation identified the critical success factors of LSS implementation for Chinese manufacturing companies and explored the challenges occurring during the LSS transformations. The objectives of this dissertation were:
i. to understand how the employee training process for LSS can be designed using total quality management (TQM) adoption in private manufacturing organizations in China;
ii. to understand how LSS practices can be adopted successfully in SMEs in China;
iii. to examine and explore the critical success factors (CSF) of LSS implementation;
iv. to discuss the challenges occurring during LSS transformation.
Objective (i) was achieved through a descriptive single case study. This case study showed how to apply a design for Six Sigma methodology (DMADV) for staff training in quality management tools in a private organization in China. The author also discussed the problems occurring during the Six Sigma project and explored how organizational culture impacted Six Sigma implementation. Objective (ii) was achieved through a detailed descriptive single case study which recorded how LSS practices were adopted successfully in a SME-VTCL in China using DMAIC methodology. Survey data was collected to identify and explore the critical success factors of LSS implementation in SMEs, by querying the voice of top, middle, and frontline management, as well as frontline workers of these companies. Objectives (iii) and (iv) were realized utilizing descriptive, exploratory, and multi-case studies designed to gather and analyze observational and interview data. The resulting interview data, and the key factors for successful LSS transformation of these three companies were discussed from the perspective of senior management and LSS promoters within the companies. Based on interview data and the Lean iceberg model, a new LSS transformation model was proposed. The author also developed 6 propositions based on the findings from the interviews.
In summary, the results of this study provided value and references for LSS practitioners to expand the body of knowledge on the strategies used to implement LSS successfully inside organizations. The findings of this research may potentially lead more Chinese organizations to successfully adopt LSS to provide customers with high-quality products. The three LSS implementation cases described critical success factors (CSFs) and challenges that occurred during the transformation, may improve the success rate of implementation, help enterprises achieve the desired results through LSS, and enhance the sustainability of LSS implementations.
(9136835), Sungbum Jun. "SCHEDULING AND CONTROL WITH MACHINE LEARNING IN MANUFACTURING SYSTEMS." Thesis, 2020.
Знайти повний текст джерела(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Знайти повний текст джерелаOne way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.
Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.
Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.
However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.
A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.
To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.
Книги з теми "Manufacturing not elsewhere classified"
Britain, Great. Miscellaneous Manufacturing Not Elsewhere Classified. Stationery Office Books, 1996.
Знайти повний текст джерелаBritain, Great. Manufacture of Electrical Equipment Not Elsewhere Classified. Stationery Office Books, 1996.
Знайти повний текст джерелаOffice, Central Statistical. Manufacture of Domestic Appliances Not Elsewhere Classified. Stationery Office Books, 1996.
Знайти повний текст джерелаBritain, Great. Manufacture of Other Transport Equipment Not Elsewhere Classified. Stationery Office Books, 1996.
Знайти повний текст джерелаGrant, Jon E., and Marc N. Potenza. Overview of the Impulse Control Disorders Not Elsewhere Classified and Limitations of Knowledge. Edited by Jon E. Grant and Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0012.
Повний текст джерелаGroup, Research, and The Agricultural Chemicals Not Elsewhere Classified Research Group. The 2000-2005 World Outlook for Agricultural Chemicals Not Elsewhere Classified (Strategic Planning Series). 2nd ed. Icon Group International, 2000.
Знайти повний текст джерелаGroup, Research, and The Space Vehicle Equipment Not Elsewhere Classified Research Group. The 2000-2005 World Outlook for Space Vehicle Equipment Not Elsewhere Classified (Strategic Planning Series). 2nd ed. Icon Group International, 2000.
Знайти повний текст джерелаCanada. Occupational Analysis and Classification Systems Division., ed. Canadian classification and dictionary of occupations, occupations in major groups: 91, transport equipment operating, 93, material handling, 95, other crafts and equipment operating, 99, occupations not elsewhere classified. [Ottawa]: Employment and Immigration Canada, 1986.
Знайти повний текст джерелаMataix-Cols, David, and Odile A. van den Heuvel. Neuroanatomy of Obsessive Compulsive and Related Disorders. Edited by Gail Steketee. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195376210.013.0027.
Повний текст джерелаWatson, Francis. A Gospel of the Eleven. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198814801.003.0010.
Повний текст джерелаЧастини книг з теми "Manufacturing not elsewhere classified"
Zhao, Qiu-yun, Le Wei, and Hong-ping Shu. "Research on Credibility Support Mechanism of Manufacturing Cloud Service Based on Classified QoS." In Proceedings of the 5th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2014), 67–70. Paris: Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-100-0_12.
Повний текст джерелаDelgado, Julio, Claire Roddie, and Michael Schmitt. "Point-of-Care Production of CAR-T Cells." In The EBMT/EHA CAR-T Cell Handbook, 45–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94353-0_8.
Повний текст джерелаTerán, Héctor C., Oscar Arteaga, Guido R. Torres, A. Eduardo Cárdenas, R. Marcelo Ortiz, Miguel A. Carvajal, and O. Kevin Pérez. "Mobile Robotic Table with Artificial Intelligence Applied to the Separate and Classified Positioning of Objects for Computer-Integrated Manufacturing." In Communications in Computer and Information Science, 218–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00617-4_20.
Повний текст джерелаRashidifar, Rasoul, F. Frank Chen, Hamed Bouzary, and Mohammad Shahin. "A Mathematical Model for Cloud-Based Scheduling Using Heavy Traffic Limit Theorem in Queuing Process." In Lecture Notes in Mechanical Engineering, 197–206. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_20.
Повний текст джерелаKalscheuer, Florian, Henrik Eschen, and Thorsten Schüppstuhl. "Towards Semi Automated Pre-assembly for Aircraft Interior Production." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021, 203–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-74032-0_17.
Повний текст джерелаLuckman, Susan, and Jane Andrew. "What Does ‘Handmade’ Mean Today?" In Creative Working Lives, 125–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44979-7_5.
Повний текст джерелаHout, Sam A. "Classified Areas." In Sterile Manufacturing, 27–28. CRC Press, 2021. http://dx.doi.org/10.1201/9781003162506-5.
Повний текст джерелаReid, William H. "Psychotic Disorders Not Elsewhere Classified." In The Treatment of Psychiatric Disorders, 200–203. Routledge, 2018. http://dx.doi.org/10.4324/9781315825908-20.
Повний текст джерелаWicoff, James S. "Speech Disorders Not Elsewhere Classified." In The Treatment of Psychiatric Disorders, 41. Routledge, 2018. http://dx.doi.org/10.4324/9781315825908-9.
Повний текст джерелаReid, William H. "Impulse Control Disorders Not Elsewhere Classified." In The Treatment of Psychiatric Disorders, 314–20. Routledge, 2018. http://dx.doi.org/10.4324/9781315825908-28.
Повний текст джерелаТези доповідей конференцій з теми "Manufacturing not elsewhere classified"
Zhao, Qiuyun, Le Wei, and Hongping Shu. "Research on Credibility Support Mechanism of Manufacturing Cloud Service Based on Classified QoS." In 5th International Asia Conference on Industrial Engineering and Management Innovation (IEMI 2014). Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/iemi-14.2014.10.
Повний текст джерелаShibutani, Tadahiro, Tetsu Tsuruga, Qiang Yu, and Masaki Shiratori. "Interface Strength Between Sub-Micron Thin Films in Opening and Sliding Delamination Modes." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-39631.
Повний текст джерелаLee, Pil-Ho, Haseung Chung, Sang Won Lee, Jeongkon Yoo, and Jeonghan Ko. "Review: Dimensional Accuracy in Additive Manufacturing Processes." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4037.
Повний текст джерелаLall, Pradeep, Vikalp Narayan, Jim Blanche, and Mark Strickland. "Effect of Manufacturing Process Parameters on Property Evolution of Printed Circuit Board Laminates." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-93057.
Повний текст джерелаRuan, Jianzhong, Jun Zhang, and F. W. Liou. "Support Structures Extraction for Hybrid Layered Manufacturing." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dac-21098.
Повний текст джерелаAtiqullah, Mir M., Aaron R. Cowin, Ed M. Ising, Terrance K. Kelly, and K. Ravindra. "Development of a Sophomore Manufacturing Laboratory Course to Streamline the Manufacturing Education Within Mechanical Engineering Curriculum." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61935.
Повний текст джерелаYing, Chong Ho, Mohd Sobri Idris, Siti Nur Adlina Norazman, Nazerah Yaacob, Rozana Aina Maulat Osman, Mogalahalli Venkatesh Reddy, and Nor Zachy Fernandez. "Structural Analysis and Electrical Properties of Li<sub>7</sub>La<sub>3</sub>Ce<sub>2</sub>O<sub>12</sub> as a Solid Electrolyte for all Solid-State Lithium-Ion Batteries." In International Conference on Advancement of Materials, Manufacturing and Devices 2021. Switzerland: Trans Tech Publications Ltd, 2022. http://dx.doi.org/10.4028/p-7p6ol2.
Повний текст джерелаMamros, Elizabeth M., Matthew C. Eaton, Jinjin Ha, and Brad L. Kinsey. "Numerical Analysis of SS316L Biaxial Cruciform Specimens Under Proportional Loading Paths." In ASME 2021 16th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/msec2021-59877.
Повний текст джерелаSun, Chun-Hua. "Algorithm of Non-Interference Tool Path Generation for Manufacturing Integral Impeller." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57650.
Повний текст джерелаZhao, Jie, Yongxiang Hu, and Zhenqiang Yao. "Laser Induced Forward Transfer: Topography Dependence of Laser Fluence and Thickness for Titanium Film." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6587.
Повний текст джерелаЗвіти організацій з теми "Manufacturing not elsewhere classified"
Science, Fera. Analysis of CBD Products. Food Standards Agency, November 2022. http://dx.doi.org/10.46756/sci.fsa.cis490.
Повний текст джерела