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Journal articles on the topic 'Paper-making machine'

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Published: 30 May 2022
Last updated: 31 May 2022

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1

Tagashira, Hiroaki. "Progress of OptiConcept Paper Making Machine." JAPAN TAPPI JOURNAL 59, no. 2 (2005): 201–8. http://dx.doi.org/10.2524/jtappij.59.201.

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2

Kalai, D. M. "Low Cost Automated Paper Bag Making Machine." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 3556–62. http://dx.doi.org/10.22214/ijraset.2019.4596.

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3

Whalley, R., and M. Ebrahimi. "Optimum control of a paper making machine headbox." Applied Mathematical Modelling 26, no. 6 (June 2002): 665–79. http://dx.doi.org/10.1016/s0307-904x(01)00076-2.

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4

Hidaka, Yasukuni. "Technical Trends of Plastic Wire for Paper Making Machine." JAPAN TAPPI JOURNAL 49, no. 10 (1995): 1421–38. http://dx.doi.org/10.2524/jtappij.49.1421.

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5

Kazantsev, V. P., D. A. Dadenkov, and L. V. Ponosova. "Electric drive control system of the paper-making machine." Journal of Physics: Conference Series 1415 (November 2019): 012016. http://dx.doi.org/10.1088/1742-6596/1415/1/012016.

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6

Oki, Arisa. "Recent Trend in Paper Machine Concept for Board Making." JAPAN TAPPI JOURNAL 74, no. 11 (2020): 1080–83. http://dx.doi.org/10.2524/jtappij.74.1080.

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7

Peng, Zhen Rui, and Hong Yin. "Control System Development of Paper and Yarn Compounding Bag-Making Machine." Applied Mechanics and Materials 58-60 (June 2011): 36–41. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.36.

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This paper analyzes the bag-making process and control requirements of paper and yarn compounding bag-making machine (PYCBMM). The overall control scheme is established. Configuration software WinCC Flexible 2008, Siemens touch panel MP377, Siemens PLC S7-200, Siemens frequency converter MM440, rotary encoder, color sensor, and other elements are synthetically applied to design the PYCBMM control system. During 12 months commissioning operation, the control system is stable, reliable, and easy to operate. All the technical indices outperform the design requirements.
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8

STERNER, MARION, and MIKAEL MAGNUSSON. "Innovative technology for making improved paper from the poorest fibers." November 2017 16, no. 11 (2017): 633–37. http://dx.doi.org/10.32964/tj16.11.633.

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The trademarked Papermorphosis technology, consisting of two novel units for separately increasing cross direction (CD) and machine direction (MD) elongation over purely mechanical processes, has been successfully tested on recycling fiber, reaching high elongation values in each direction of over 15% in CD or 20% in MD. Increase of elongation has also had a positive influence on the relative tensile energy absorption (TEA) values in CD and MD. It is well known that mechanical properties in paper basically depend on fiber choice, headbox properties, and refining. With the novel system, elongations in cross direction (CD) and machine direction (MD) can be individually customized, even in paper grades with lower strength deriving from recycled fiber. Customizing elongations also has the effect of improving or better balancing TEA values in both directions. The aim is to improve existing recycling papers and to broaden the use of recycling fiber with respect to the past.
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9

Kim, Wook-Hyeon, and Tae-Won Park. "Development of Program for Designing Barrel Cam of Machine Making Paper Cups." Transactions of the Korean Society of Mechanical Engineers A 35, no. 4 (April 1, 2011): 433–38. http://dx.doi.org/10.3795/ksme-a.2011.35.4.433.

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10

He, Ning, Dawei Shi, Michael Forbes, Johan Backström, and Tongwen Chen. "Robust tuning for machine-directional predictive control of MIMO paper-making processes." Control Engineering Practice 55 (October 2016): 1–12. http://dx.doi.org/10.1016/j.conengprac.2016.06.008.

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11

M. A., Burhanuddin, Sami M. Halawani, and A. R. Ahmad. "A Costing Analysis for Decision Making Grid Model in Failure-Based Maintenance." Advances in Decision Sciences 2011 (August 1, 2011): 1–15. http://dx.doi.org/10.1155/2011/205039.

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Background. In current economic downturn, industries have to set good control on production cost, to maintain their profit margin. Maintenance department as an imperative unit in industries should attain all maintenance data, process information instantaneously, and subsequently transform it into a useful decision. Then act on the alternative to reduce production cost. Decision Making Grid model is used to identify strategies for maintenance decision. However, the model has limitation as it consider two factors only, that is, downtime and frequency of failures. We consider third factor, cost, in this study for failure-based maintenance. The objective of this paper is to introduce the formulae to estimate maintenance cost. Methods. Fish bone analysis conducted with Ishikawa model and Decision Making Grid methods are used in this study to reveal some underlying risk factors that delay failure-based maintenance. The goal of the study is to estimate the risk factor that is, repair cost to fit in the Decision Making Grid model. Decision Making grid model consider two variables, frequency of failure and downtime in the analysis. This paper introduces third variable, repair cost for Decision Making Grid model. This approaches give better result to categorize the machines, reduce cost, and boost the earning for the manufacturing plant. Results. We collected data from one of the food processing factories in Malaysia. From our empirical result, Machine C, Machine D, Machine F, and Machine I must be in the Decision Making Grid model even though their frequency of failures and downtime are less than Machine B and Machine N, based on the costing analysis. The case study and experimental results show that the cost analysis in Decision Making Grid model gives more promising strategies in failure-based maintenance. Conclusions. The improvement of Decision Making Grid model for decision analysis with costing analysis is our contribution in this paper for computerized maintenance management system.
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12

Prates, Marcos O. "Spatial extreme learning machines: An application on prediction of disease counts." Statistical Methods in Medical Research 28, no. 9 (April 9, 2018): 2583–94. http://dx.doi.org/10.1177/0962280218767985.

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Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.
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13

Liu, Hong Quan, Hong Ru Liu, and Jin Xi Lu. "Optimal Design of the Vibration Device of Block Making Machine." Applied Mechanics and Materials 84-85 (August 2011): 711–14. http://dx.doi.org/10.4028/www.scientific.net/amm.84-85.711.

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This paper described the theory, source and effect of the vibration of the block making machine. The paper analyzed the situation of beneficial vibration and harmful vibration. The author improved the traditional vibration device of block making machine. The new type of block making machine realized harmless vibration and high efficiency.
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14

SONMEZ, Rifat, and Burak SÖZGEN. "A support vector machine method for bid/no bid decision making." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 23, no. 5 (May 24, 2017): 641–49. http://dx.doi.org/10.3846/13923730.2017.1281836.

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The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advan­tage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the sup­port vector machines.
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15

Tucker, Barbara M., and Judith A. McGaw. "Most Wonderful Machine: Mechanization and Social Change in Berkshire Paper Making, 1801-1885." American Historical Review 94, no. 4 (October 1989): 1171. http://dx.doi.org/10.2307/1906753.

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16

Prude, Jonathan, and Judith A. McGaw. "Most Wonderful Machine: Mechanization and Social Change in Berkshire Paper Making, 1801-1885." Journal of Interdisciplinary History 19, no. 2 (1988): 363. http://dx.doi.org/10.2307/204709.

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17

Berg, Maxine, and Judith A. McGaw. "Most Wonderful Machine: Mechanization and Social Change in Berkshire Paper Making 1801-1885." Technology and Culture 30, no. 3 (July 1989): 692. http://dx.doi.org/10.2307/3105984.

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18

Larson, John Lauritz, and Judith A. McGaw. "Most Wonderful Machine: Mechanization and Social Change in Berkshire Paper Making, 1801-1885." Journal of the Early Republic 7, no. 4 (1987): 417. http://dx.doi.org/10.2307/3123736.

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19

Gross, Laurence F., and Judith A. McGraw. "Most Wonderful Machine: Mechanization and Social Change in Berkshire Paper Making, 1801-1885." Journal of American History 75, no. 2 (September 1988): 604. http://dx.doi.org/10.2307/1887905.

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20

Matsumoto, Koji. "Sludge Dewatering Machine, Rotary Press Filter-Dewatering of Paper Making Waste Water Sludge-." JAPAN TAPPI JOURNAL 63, no. 1 (2009): 36–40. http://dx.doi.org/10.2524/jtappij.63.36.

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21

Grote, Thomas, and Philipp Berens. "On the ethics of algorithmic decision-making in healthcare." Journal of Medical Ethics 46, no. 3 (November 20, 2019): 205–11. http://dx.doi.org/10.1136/medethics-2019-105586.

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In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.
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22

Shu, Qi, and Bing Gang Wang. "Ergonomics Analysis Thinking of the Operation Domain Design of Workover Machines." Advanced Materials Research 228-229 (April 2011): 368–73. http://dx.doi.org/10.4028/www.scientific.net/amr.228-229.368.

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Nowadays there is little ergonomics consideration in the oil engineering machines and the improvement of comfort levels in the high intensity work of oil workers should be noted. Taking the case of the interface design of operation domain of the workover machines, this paper studied the design method of man-machine interface of oil workover machines basing on its development trend, from the view point of improving operation efficiency and operating reliability. In the paper two kinds of software, SolidWorks and CATIA, were used to build 3D virtual human body models and conduct the man-machine interface design of the workspace. Ultimately, a new thinking of man-machine interface optimization design of oil machinery was obtained by making a comparison of the two design processes.
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23

Gerasimov, V., V. Ignatov, K. Sovin, and S. Buryakov. "Processes of waste management of disposed agricultural machinery in AIC." Sel'skohozjajstvennaja tehnika: obsluzhivanie i remont (Agricultural Machinery: Service and Repair), no. 2 (February 1, 2020): 46–53. http://dx.doi.org/10.33920/sel-10-2002-07.

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The paper reflects the nature of the main technological measures that are carried out at specialized enterprises (sites) for processing waste disposal of self-propelled machines in the agro-industrial complex. Waste generation occurs at all stages of the life cycle. The product life cycle includes a number of stages, each of which involves the use of specific types of work, in the course of which there are production and consumption waste (hereinafter — waste). Considering the full life cycle of the machine, it should be understood that the production stage of the machine can last for a long time. But the time characteristic of making a particular machine at this stage will be equal to the time interval associated with making a particular instance of the machine.
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24

Edmonds, Ernest. "Communication Machines as Art." Arts 8, no. 1 (February 9, 2019): 22. http://dx.doi.org/10.3390/arts8010022.

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The paper presents a personal history of making machines as artworks. The particular kind of art machines that have been made since around 1970 are communication machines: ones that enable humans to interact with each other. However, they do not provide communication in the normal sense, but use a small bandwidth for relatively complex connections, making the experience of the interactions the art experience. The paper concludes by explaining how it later became possible to use computer networking and the Internet to make artworks that were more complex and, in part, autonomous generative machines whilst retaining the earlier communication machine functions.
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25

Rosli, Muhammad Firdaus, Lim Meng Hee, and M. Salman Leong. "Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring." Applied Mechanics and Materials 773-774 (July 2015): 154–57. http://dx.doi.org/10.4028/www.scientific.net/amm.773-774.154.

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Machines are the heart of most industries. By ensuring the health of machines, one could easily increase the company revenue and eliminates any safety threat related to machinery catastrophic failures. In condition monitoring (CM), questions often arise during decision making time whether the machine is still safe to run or not? Traditional CM approach depends heavily on human interpretation of results whereby decision is made solely based on the individual experience and knowledge about the machines. The advent of artificial intelligence (AI) and automated ways for decision making in CM provides a more objective and unbiased approach for CM industry and has become a topic of interest in the recent years. This paper reviews the techniques used for automated decision making in CM with emphasis given on Dempster-Shafer (D-S) evident theory and other basic probability assignment (BPA) techniques such as support vector machine (SVM) and etc.
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26

Jian, Shao Yong, Si Jun Tao, and Xin Ren Huang. "Preference Selection Index Method for Machine Selection in a Flexible Manufacturing Cell." Advanced Materials Research 1078 (December 2014): 290–93. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.290.

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The selection of a desirable machine is an important concern for the manufacturing firm. The selection process contains some critical selection attributes and the task of this process is to choose the desirable machine from a number of candidate machines. Then the machine selection problem is actually a multi-attribute decision making problem. This paper will develop a preference selection index method to solve the problem of machine selection in a flexible manufacturing cell. A case study is used to demonstrate that the proposed method is effective and feasible.
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27

Xiong, Wei, Hongmiao Fan, Liang Ma, and Chen Wang. "Challenges of human—machine collaboration in risky decision-making." Frontiers of Engineering Management 9, no. 1 (January 27, 2022): 89–103. http://dx.doi.org/10.1007/s42524-021-0182-0.

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AbstractThe purpose of this paper is to delineate the research challenges of human—machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human—machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human—machine collaboration in risky decision-making. Afterward, we review the literature on human—machine collaboration in a general decision context, from the perspectives of human—machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human—machine communication and teamwork in risky decision-making, followed by future research avenues.
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28

Huang, Zishan. "UAV Intelligent Control Based on Machine Vision and Multiagent Decision-Making." Advances in Multimedia 2022 (May 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/8908122.

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In order to improve the effect of UAV intelligent control, this paper will improve machine vision technology. Moreover, this paper adds scale information on the basis of the LSD algorithm, uses the multiline segment standard to merge these candidate line segments for intelligent recognition, and uses the LSD detection algorithm to improve the operating efficiency of the UAV control system and reduce the computational complexity. In addition, this paper combines machine vision technology and multiagent decision-making technology for UAV intelligent control and builds an intelligent control system, which uses intelligent machine vision technology for recognition and multiagent decision-making technology for motion control. The research results show that the UAV intelligent control system based on machine vision and multiagent decision-making proposed in this paper can achieve reliable control of UAVs and improve the work efficiency of UAVs.
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29

Mori, Yoshitatsu. "Drainage and drainage resistance coefficients at hydrofoils of wire part for paper making machine." Proceedings of The Computational Mechanics Conference 2002.15 (2002): 577–78. http://dx.doi.org/10.1299/jsmecmd.2002.15.577.

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30

Sueoka, Atsuo, Takahiro Ryu, Akihiro Yasuda, Takahiro Kondou, and Keiichi Katayama. "Pattern Formation of Roll-Covering Rubber in a Paper-Making Machine and Its Countermeasures." Transactions of the Japan Society of Mechanical Engineers Series C 61, no. 591 (1995): 4160–67. http://dx.doi.org/10.1299/kikaic.61.4160.

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31

Chen, Hong Yan, Jian Qu, Fu Yi Cai, and You Wu. "Theoretical and Experimental Research on Matching of Cooling System Property of Househole Ice-Making Machine." Applied Mechanics and Materials 716-717 (December 2014): 747–50. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.747.

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View of the current ice-making machine for cooling system of the state of loosely designed in this paper under operating principle and structure and the cooling characteristics of a household ice-making machine,precise exposition of various refrigeration equipment`s design and calculation method, analysis of its selection ideas for the ice-making machine for cooling system designed to provide a important value of reference.
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32

Zhang, De Hui, and Xiao Qiang Wu. "Design of Foil-Making Machine Based on Motion Controller." Applied Mechanics and Materials 721 (December 2014): 334–37. http://dx.doi.org/10.4028/www.scientific.net/amm.721.334.

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The work of traditional foil making machine is a manual or semiautomatic process, the work efficiency is low, and the security risks exist. In this paper, a kind of foil making machine is designed based on motion controller, the automation of foil process is realized, the work efficiency is improved, the production cost is reduced, and safety of the operator is guaranteed. It makes a certain contribution for the development of foil making machine.
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33

Li, Yang, Jun Xu, and Yun Long Lei. "The Research of Stemming Making Jam Machine." Applied Mechanics and Materials 709 (December 2014): 36–40. http://dx.doi.org/10.4028/www.scientific.net/amm.709.36.

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This paper introduces the basic function research idea and design scheme of the unity machine, combining with the actual production needs for stemming making blockage machine. This machine adopts pneumatic motor as power source to drive agitator and pump. It can achieve stemming production and the blockage of blasthole. It designs chassis which can walk and steer and workbench lifting mechanism used to implement each blasthole of tunnel face blocked automatically
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34

Matsuda, Michiko, and Fumihiko Kimura. "Configuration of the Digital Eco-Factory for Green Production." International Journal of Automation Technology 6, no. 3 (May 5, 2012): 289–95. http://dx.doi.org/10.20965/ijat.2012.p0289.

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This paper proposes a construction method for the digital eco-factory to support promotion and implementation of green production. The digital eco-factory is configured on the digital factory. The digital factory is constructed by applying multi agent technology. In the digital factory, all factory elements such as machine tools and assembly machines are configured as software agents. The digital factory mirrors the structure of the actual factory. The shop floor is modeled as a virtual shop floor by machine agents. In addition to machine agents, machined workpieces and assembled parts are also configured as software agents. The workpiece agent selects machines to complete itself as a part. The part agent also leads the assembly processes for making the complete product. The operator can set up the initial shop floor configuration through the user interface. The operator also controls the production scenario by means of inputting a production order with candidate production plans. When constructing the digital eco-factory using this digital factory, the calculation function for indices of environmental burdens such as CO2 and energy consumption are installed in each machine agent. The workpiece/part agent monitors them. The proposed configuration is applied to the printed board production line for verification of the proposal.
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35

Li, Zhaohui, Yue Feng, Jun Xu, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. "Teaching Machines to Extract Main Content for Machine Reading Comprehension." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9973–74. http://dx.doi.org/10.1609/aaai.v33i01.33019973.

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Machine reading comprehension, whose goal is to find answers from the candidate passages for a given question, has attracted a lot of research efforts in recent years. One of the key challenge in machine reading comprehension is how to identify the main content from a large, redundant, and overlapping set of candidate sentences. In this paper we propose to tackle the challenge with Markov Decision Process in which the main content identification is formalized as sequential decision making and each action corresponds to selecting a sentence. Policy gradient is used to learn the model parameters. Experimental results based on MSMARCO showed that the proposed model, called MC-MDP, can select high quality main contents and significantly improved the performances of answer span prediction.
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36

Hanif, Athar, Ali Salman Rana, Farhan Arif, Umar Farooq, and Muhammad Usman Asad. "To Automate AMF Pinspotter Machine." Advanced Materials Research 403-408 (November 2011): 4910–17. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4910.

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This paper gives instructions for making complete and efficient control system of AMF bowling machine, it helps decreasing machine cycle time, make the machine energy efficient, reduce error in scoring, perform automatic resetting of machine in the event of power failure, calculating and displaying accurate speed and giving more options for user interface.
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37

Sato, Kiyoshi. "Strength Agent for Spraying Between Layers (Polymjet-Series). Result of Estimate by Paper Making Machine." JAPAN TAPPI JOURNAL 51, no. 2 (1997): 292–97. http://dx.doi.org/10.2524/jtappij.51.292.

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38

RYU, Takahiro, Atsuo SUEOKA, Kenichiro MATSUZAKI, Terumasa UENO, and Hidetoshi MORITA. "504 Pattern Formation of Roll-Covering Rubber in a Paper-Making Machine and Its Countermeasures." Proceedings of the Dynamics & Design Conference 2003 (2003): _504–1_—_504–6_. http://dx.doi.org/10.1299/jsmedmc.2003._504-1_.

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39

Zerilli, John, Alistair Knott, James Maclaurin, and Colin Gavaghan. "Algorithmic Decision-Making and the Control Problem." Minds and Machines 29, no. 4 (December 2019): 555–78. http://dx.doi.org/10.1007/s11023-019-09513-7.

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AbstractThe danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up to this point its manifestation in machine learning contexts has not received serious attention. This paper aims to fill that gap. We argue that, except in certain special circumstances, algorithmic decision tools should not be used in high-stakes or safety-critical decisions unless the systems concerned are significantly “better than human” in the relevant domain or subdomain of decision-making. More concretely, we recommend three strategies to address the control problem, the most promising of which involves a complementary (and potentially dynamic) coupling between highly proficient algorithmic tools and human agents working alongside one another. We also identify six key principles which all such human–machine systems should reflect in their design. These can serve as a framework both for assessing the viability of any such human–machine system as well as guiding the design and implementation of such systems generally.
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40

Żurowski, Wojciech, and Piotr Sztąberek. "Case study while operating Schlosser vibropress." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 6 (June 30, 2018): 331–33. http://dx.doi.org/10.24136/atest.2018.087.

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Paper discussed an accident that occurred during the use of concrete block making machine for concrete blocks. The circumstances of the accident and its causes were indicated. Attention is paid to the necessity of reliable application of the provisions regarding minimum requirements for the safety of exploitation of used machines.
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41

Deja, Mariusz, and Mieczyslaw Siemiatkowski. "Generative Process Planning with Reasoning Based on Geometrical Product Specification." Key Engineering Materials 597 (December 2013): 159–64. http://dx.doi.org/10.4028/www.scientific.net/kem.597.159.

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The focus of this paper is on CAPP for parts manufacture in systems of definite process capabilities, involving the use of multi-axis machining centers for parts shaping and grinding machines for finishing. It presents in particular a decision making scheme for setup determination as a part of generative process planning. The planning procedure consists of two stages. The first stage is associated with generic setup determination applying tool accessibility analysis to machined features and reasoning based on geometrical product specification (GPS). The second stage involves machine specific setup planning considering the availability and capability of machines on a shop floor through setup merging. The impact of dimensional and geometric tolerance as well as the surface roughness of relatively complex mechanical element on the generated process plan with several setups was presented. The relevant reasoning mechanism within a decision making scheme on generated process alternatives is shown based on a numeric case.
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42

Seecharan, Turuna, Ashraf Labib, and Andrew Jardine. "Maintenance strategies: Decision Making Grid vs Jack-Knife Diagram." Journal of Quality in Maintenance Engineering 24, no. 1 (March 12, 2018): 61–78. http://dx.doi.org/10.1108/jqme-06-2016-0023.

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Purpose Maintenance management is a vital strategic task given the increasing demand on sustained availability of machines. Machine performance depends primarily on frequency and downtime; therefore, ranking critical machines based on these two criteria is important to determine the appropriate maintenance strategy. The purpose of this paper is to compare two methods, using case studies, to allocate maintenance strategies while prioritising performance based on frequency and downtime or Mean Time to Repair: the Decision Making Grid (DMG) and Jack-Knife Diagram (JKD). Design/methodology/approach The literature indicates the need for an approach able to integrate maintenance performance and strategy in order to adapt existing data on equipment failures and to routinely adjust preventive measures. Maintenance strategies are incomparable; one strategy should not be applied to all machines, nor all strategies to the same machine. Findings Compared to the Pareto histogram, the DMG and JKD provide visual representations of the performance of the worst machines with respect to frequency and downtime, thus allowing maintenance technicians to apply the appropriate maintenance strategy. Each method has its own merits. Research limitations/implications This work compares only two methods based on their original conceptualisation. This is due to their similarities in using same input data and their main features. However, there is a scope to compare to other methods or variations of these methods. Practical implications This paper highlights how the DMG and JKD can be incorporated in industrial applications to allocate appropriate maintenance strategy and track machine performance over time. Originality/value Neither DMG nor JKD have been compared in the literature. Currently, the JKD has been used to rank machines, and the DMG has been used to determine maintenance strategies.
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43

Shirbhate, Sanjay. "Multipurpose Low Cost Table Machine." IRA-International Journal of Technology & Engineering (ISSN 2455-4480) 7, no. 2 (S) (July 10, 2017): 101. http://dx.doi.org/10.21013/jte.icsesd201710.

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The concept of this paper is to develop idea of multipurpose machine as by conducting various operation on same machine, using single operating system. Here, in this machine there is a electric motor which is connected to the pulleys driven by belt (more than one pulley for various speed). The pulley is connected with the shaft which rotate in circular motion and to this shaft circular tools are connected as per required operation as circular saw cuts wooden pieces, pipe cutter will cuts iron rod or iron hollow pipe, grinding circular wheel grinds the job, polishing wheel polishes the job etc. more types of tools and the operation can be done on the same machine. The idea of this machine is to motivate small scale house hold units for ex. Furniture making, office table, Chair, show case for wedding ceremony etc. with low cost machines with multipurpose operation done on same machine.
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44

Li, Tie Gang, Chu Lin Fu, Peng Guan, Tian Biao Yu, and Wan Shan Wang. "Machine Tool Selection Based on AHP and ACO." Applied Mechanics and Materials 44-47 (December 2010): 874–78. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.874.

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Aiming at uncertainty of process plans in the process decision making, the paper introduced a method based on ant colony optimization (ACO) to find out the set of optimal solutions for multi-objective machine tool selection. The Analytic Hierarchy Process (AHP) was applied to simplify process decision making plans, and the structural diagram of AHP was made up according to the different demands of manufacturing engineer. By means of the examples the different elements to different manufacturing processes are comprehensive evaluated, and the most splendid process scheme is evaluated, meanwhile the feasibility of the proposed method applying process decision making is verified.
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45

Mushtaq, Shiza, M. M. Manjurul Islam, and Muhammad Sohaib. "Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review." Energies 14, no. 16 (August 20, 2021): 5150. http://dx.doi.org/10.3390/en14165150.

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This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.
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46

Wang, Lidong, and Cheryl Ann Alexander. "Machine Learning in Big Data." International Journal of Mathematical, Engineering and Management Sciences 1, no. 2 (September 1, 2016): 52–61. http://dx.doi.org/10.33889/ijmems.2016.1.2-006.

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Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.
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47

Ma, Yin Ping, and Yao Hua Wu. "Research on Automatic Correction Method for Cigarette Making Machine Transmission Device Based on Fuzzy-PID Dual Mode Control." Advanced Materials Research 898 (February 2014): 525–29. http://dx.doi.org/10.4028/www.scientific.net/amr.898.525.

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In the long-term process of working, cigarette making machine usually has transmission device running deviation problem. In response to this problem, this paper presents an automatic correction method of cigarette making machine transmission device based on cigarette making machine structure and analysis of running deviation. It divides the whole control process into different stages. when the error signal e is beyond the threshold value, use Fuzzy control; and when it is within the scope of threshold value, use PID control. Apply Simulink and Fuzzy toolbox of Matlab software for simulation experiment, and the experiment results demonstrated that the using of Fuzzy-PID dual mode control system can improve dynamic performance and stable performance of the control system, and can effectively and automatically correct cigarette making machine transmission device.
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48

Wang, Lidong. "Machine Learning in Big Data." International Journal of Advances in Applied Sciences 4, no. 4 (December 1, 2015): 117. http://dx.doi.org/10.11591/ijaas.v4.i4.pp117-123.

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<p>Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.</p>
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49

Zhu, Jian Min, Hai Wei Li, and Zhi Wen Huang. "Grey Correlation Analysis-Based Comprehensive Dynamic Characteristics Evaluation of NC Machine Tool." Advanced Materials Research 479-481 (February 2012): 1230–36. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1230.

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The dynamic characteristics of the NC machine are the important technical indicators of NC machine performance, and the precision of existing machine's dynamic characteristics evaluation analysis that based on structural dynamics modeling and simulation is poor. For this problem, this paper presents NC machine dynamic characteristics evaluation methods based on Grey Correlation Analysis. This method, by measuring the vibration signal of feature points in NC machine and making 1/3 octave spectrum analysis for vibration signal, can obtain the characteristics data sequence of vibration signal. Through the grey correlation analysis method, calculate the relevancy between the characteristics data sequence of each vibration signal and that of ideal dynamic characteristics, and quantitatively evaluate the dynamic characteristics of machine tools according to the size of the grey correlation. Took three same type NC milling machines as study objects, designed the cutting vibration resistance experiments, used this paper's method for the evaluation analysis of the comprehensive dynamic characteristics of NC milling machine, and obtained the evaluation results which is in line with the actual dynamic characteristics of machine tools, and verified the evaluation method of comprehensive dynamic characteristics that proposed in this paper.
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Nie, Xiao Gen, and Yan Bing Liu. "Research and Development of the 5-Axis Shoe Last NC Machine." Advanced Materials Research 139-141 (October 2010): 1869–73. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1869.

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Based on the analysis of the disadvantages of shoe last making with 3-axis shoe last NC machine, this paper presents a novel solution for last making with 5-axis shoe last NC machine, introduces the working principles, motions, components and implementation of 5-axis shoe last NC machine in detail. A key feature of the machine is that it can produce complete lasts in one working process, avoiding milling the toe and heel with final manual finishing operations. This paper proposes an algorithm of the tool path generation of the machine. By the grid data of the last surfaces and spiral interpolation, we can obtain the cutter contact points and cutter location points and then deduce the formula of solving each component motion in the machining. A model machine developed by the presented solution demonstrates the improved machining efficiency and high quality of shoe last.
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