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Journal articles on the topic 'ADDITIVE METHOD'

Author: Grafiati

Published: 11 August 2021

Last updated: 1 February 2022

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1

Xiang, Lingyun, Jiaohua Qin, Xiao Yang, and Qichao Tang. "n Adaptive Steganographic Method Using Additive Noise." Journal of Computers 11, no. 3 (May 2016): 207–15. http://dx.doi.org/10.17706/jcp.11.3.207-215.

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2

NAKAGAWA, Yasutada. "1802 FEM Analysis of Additive Manufacturing by Powder Bed Fusion Method." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1802–1_—_1802–4_. http://dx.doi.org/10.1299/jsmelem.2015.8._1802-1_.

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3

Gorbenko, N. I., and V. P. Il’in. "The Additive Peaceman–Rachford Method." Journal of Mathematical Sciences 216, no. 6 (June 25, 2016): 753–60. http://dx.doi.org/10.1007/s10958-016-2939-2.

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4

Pavarino, Luca F. "Additive Schwarz methods for thep-version finite element method." Numerische Mathematik 66, no. 1 (December 1993): 493–515. http://dx.doi.org/10.1007/bf01385709.

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5

Shome, Biswadip. "An Enhanced Additive Correction Multigrid Method." Numerical Heat Transfer, Part B: Fundamentals 49, no. 4 (October 2006): 395–407. http://dx.doi.org/10.1080/10407790500434117.

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6

Hamidoune, Y. O. "An Isoperimetric Method in Additive Theory." Journal of Algebra 179, no. 2 (January 1996): 622–30. http://dx.doi.org/10.1006/jabr.1996.0028.

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7

Tran, Thanh, and Ernst P. Stephan. "Additive schwarz methods for the H-version boundary element method." Applicable Analysis 60, no. 1-2 (February 1996): 63–84. http://dx.doi.org/10.1080/00036819608840418.

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8

Haferssas, R., P. Jolivet, and F. Nataf. "An Additive Schwarz Method Type Theory for Lions's Algorithm and a Symmetrized Optimized Restricted Additive Schwarz Method." SIAM Journal on Scientific Computing 39, no. 4 (January 2017): A1345—A1365. http://dx.doi.org/10.1137/16m1060066.

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9

Prayogi, Satria Yudha. "SIMPLE ADDITIVE WEIGHTING APPLICATION METHOD OF TABLET PC SELECTION FOR BEGINNERS." Computer Engineering, Science and System Journal 1, no. 1 (January 31, 2016): 35–40. http://dx.doi.org/10.24114/cess.v1i1.4034.

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10

Fujita, Masatoshi, and Ryojiro Tominaga. "Electronics Device Printer Applying Additive Manufacturing Method." Journal of The Japan Institute of Electronics Packaging 23, no. 6 (September 1, 2020): 476–81. http://dx.doi.org/10.5104/jiep.23.476.

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11

Koçak, S., A. Kazaz, and S. Ulubeyli. "Subcontractor selection with additive ratio assessment method." Journal of Construction Engineering, Management & Innovation 1, no. 1 (March 31, 2018): 18–32. http://dx.doi.org/10.31462/jcemi.2018.01018032.

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12

Goodridge, Wayne S. "Sensitivity Analysis Using Simple Additive Weighting Method." International Journal of Intelligent Systems and Applications 8, no. 5 (August 5, 2016): 27–33. http://dx.doi.org/10.5815/ijisa.2016.05.04.

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13

Chai, Yuan, Qing-Hua Qin, and Yi Xiao. "Recent Patents on Construction Additive Manufacturing Method." Recent Patents on Mechanical Engineering 9, no. 2 (May 24, 2016): 94–101. http://dx.doi.org/10.2174/2212797609666160505113815.

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14

Khare, H. S., N. N. Gosvami, I. Lahouij, Z. B. Milne, J. B. McClimon, and R. W. Carpick. "Nanotribological Printing: A Nanoscale Additive Manufacturing Method." Nano Letters 18, no. 11 (October 16, 2018): 6756–63. http://dx.doi.org/10.1021/acs.nanolett.8b02505.

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15

Karayel, Elif, and Yahya Bozkurt. "Additive manufacturing method and different welding applications." Journal of Materials Research and Technology 9, no. 5 (September 2020): 11424–38. http://dx.doi.org/10.1016/j.jmrt.2020.08.039.

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16

Chen, C., R. L. Chen, and M. H. Sheu. "Fast additive normalisation method for exponential computation." IEE Proceedings - Computers and Digital Techniques 151, no. 3 (2004): 191. http://dx.doi.org/10.1049/ip-cdt:20040258.

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17

Badea, Lori, and Junping Wang. "An additive Schwarz method for variational inequalities." Mathematics of Computation 69, no. 232 (May 20, 1999): 1341–55. http://dx.doi.org/10.1090/s0025-5718-99-01164-3.

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18

Karande, Prasad, and Shankar Chakraborty. "Supplier Selection Using Weighted Utility Additive Method." Journal of The Institution of Engineers (India): Series C 96, no. 4 (February 27, 2015): 397–406. http://dx.doi.org/10.1007/s40032-015-0177-x.

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19

Sartori, Giuseppe, and Carlo Umiltà. "The Additive Factor Method in Brain Imaging." Brain and Cognition 42, no. 1 (February 2000): 68–71. http://dx.doi.org/10.1006/brcg.1999.1164.

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20

Felner, A., R. E. Korf, and S. Hanan. "Additive Pattern Database Heuristics." Journal of Artificial Intelligence Research 22 (November 1, 2004): 279–318. http://dx.doi.org/10.1613/jair.1480.

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We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.

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21

Wrześniewska-Tosik, Krystyna, Tomasz Mik, Ewa Wesołowska, Sarah Montes, Tomasz Kowalewski, and Michał Kudra. "Composite Nonwovens with Natural Additive." Fibres and Textiles in Eastern Europe 28, no. 1(139) (February 29, 2020): 123–29. http://dx.doi.org/10.5604/01.3001.0013.5867.

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Various methods of nonwoven composite materials manufacturing are known. One such method is the well-known technique called spun-bonding. The production technology for composite nonwoven by the spun-bond method is known, but the technique of introducing an additive in the form of shredded wastes of natural origin so as to obtain a composite nonwoven fabric with interesting functional properties is new. The article describes a method of producing an innovative composite nonwoven using the spun-bond technique. As a result of incorporating various additives into the nonwoven structure, composite nonwovens with modified properties are obtained. Composite nonwovens, depending on the additive used, can be utilised as filtration material in the construction, agriculture or automotive industry.

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22

Kostin, V. A., and G. M. Grigorenko. "Modelling of temperature fields, stresses and deformations in cylinder shells produced by additive manufacturing method." Paton Welding Journal 2019, no. 7 (July 28, 2019): 12–17. http://dx.doi.org/10.15407/tpwj2019.07.03.

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23

Ye, Xueyi, Yan Zhang, Yunlu Wang, and Guopeng Lu. "Universal steganalysis method based on additive noise model." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 26, no. 8 (February 18, 2013): 681–86. http://dx.doi.org/10.3724/sp.j.1187.2012.00681.

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24

Adachi, Mitsuru, Kenya Kurita, Yoshihiko Nagata, and Shuji Koiwai. "Formation of light metal using additive manufacturing method." Journal of Japan Institute of Light Metals 66, no. 7 (2016): 360–67. http://dx.doi.org/10.2464/jilm.66.360.

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25

Courtière, A., J. Hardouin, T. Hasbroucq, C. A. Possamaı̈, and F. Vidal. "The additive factor method in rat information processing." Behavioural Processes 50, no. 2-3 (August 2000): 113–21. http://dx.doi.org/10.1016/s0376-6357(00)00098-x.

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26

Modarres, M., and S. Sadi-Nezhad. "Fuzzy Simple Additive Weighting Method by Preference Ratio." Intelligent Automation & Soft Computing 11, no. 4 (January 2005): 235–44. http://dx.doi.org/10.1080/10642907.2005.10642907.

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27

Mammen, Enno, and Byeong U. Park. "A simple smooth backfitting method for additive models." Annals of Statistics 34, no. 5 (October 2006): 2252–71. http://dx.doi.org/10.1214/009053606000000696.

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28

Chen, Zhuojun. "Understanding of the Modeling Method in Additive Manufacturing." IOP Conference Series: Materials Science and Engineering 711 (January 7, 2020): 012017. http://dx.doi.org/10.1088/1757-899x/711/1/012017.

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29

Gao, Qing, and Likang Li. "An additive Schwarz method for plate bending problems." Computer Methods in Applied Mechanics and Engineering 155, no. 3-4 (March 1998): 261–71. http://dx.doi.org/10.1016/s0045-7825(97)00155-2.

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30

Warnars, Harco Leslie Hendric Spits, Arif Fahrudin, and Wiranto Herry Utomo. "Student performance prediction using simple additive weighting method." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 630. http://dx.doi.org/10.11591/ijai.v9.i4.pp630-637.

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In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.

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31

Wadia, Reena. "Fabrication of dental implants by the additive method." British Dental Journal 226, no. 8 (April 2019): 575. http://dx.doi.org/10.1038/s41415-019-0267-x.

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32

Zavadskas, Edmundas Kazimieras, Zenonas Turskis, Titas Dejus, and Milda Viteikiene. "Sensitivity analysis of a simple additive weight method." International Journal of Management and Decision Making 8, no. 5/6 (2007): 555. http://dx.doi.org/10.1504/ijmdm.2007.013418.

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33

Liu, Fwu Hsing, Wen Hsueng Lin, Yung Kang Shen, and Jeou Long Lee. "Fabrication Inner Channel Ceramics Using Layer Additive Method." Key Engineering Materials 443 (June 2010): 528–33. http://dx.doi.org/10.4028/www.scientific.net/kem.443.528.

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This paper presents a layer additive method, ceramic laser curing, to form a ceramic part with inner channel features, by which silica powder is bonded by curing effect under disposal of a 20W CO2 laser. This process includes four steps: making slurry by mixing a binder with ceramic powder, paving the slurry on the surface of a platform, scanning the paved slurry layer via laser beam, removing the un-cured slurries from the solidified ceramic component. This process needed only low laser power to build ceramic parts by using “curing effect”. The deflection and shrinkage of ceramics could be decreased, also the distortion due to post sintering process was avoidable. The inner channel structures were support by ceramic slurries to avoid the sagged deflection and to maintain the dimensional accuracy. The maximum flexural strength of the cured specimen was 4.7 MPa. This process has potential to fabricate inner complex ceramic components for industrial applications.

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34

ELIAS, S. R., G. D. STUBLEY, and G. D. RAITHBY. "AN ADAPTIVE AGGLOMERATION METHOD FOR ADDITIVE CORRECTION MULTIGRID." International Journal for Numerical Methods in Engineering 40, no. 5 (March 15, 1997): 887–903. http://dx.doi.org/10.1002/(sici)1097-0207(19970315)40:5<887::aid-nme93>3.0.co;2-i.

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35

NIINO, Toshiki. "Additive Manufacturing : Challenges to the Third Manufacturing Method." Journal of the Society of Mechanical Engineers 115, no. 1120 (2012): 160–61. http://dx.doi.org/10.1299/jsmemag.115.1120_160.

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36

Adams, D., and C. J. Turner. "An implicit slicing method for additive manufacturing processes." Virtual and Physical Prototyping 13, no. 1 (November 2017): 2–7. http://dx.doi.org/10.1080/17452759.2017.1392684.

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37

KARAMZIN, YU N., and I. G. ZAKHAROVA. "ON NEW ADDITIVE DIFFERENCE METHOD FOR PARABOLIC EQUATIONS." Mathematical Models and Methods in Applied Sciences 06, no. 03 (May 1996): 353–63. http://dx.doi.org/10.1142/s0218202596000122.

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A new approach is suggested to the construction of conservative difference schemes for multidimensional second-order parabolic equations containing divergent first-order differential forms. These schemes hold the property of having fixed sign for non-negative solutions. The convergence of schemes is proved in the grid norm L2 at the rate O(τ + h2).

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38

Gjesdal, Thor. "A note on the additive correction multigrid method." International Communications in Heat and Mass Transfer 23, no. 2 (March 1996): 293–98. http://dx.doi.org/10.1016/0735-1933(96)00014-0.

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39

Köksalan, Murat, and Selin Bilgin Özpeynirci. "An interactive sorting method for additive utility functions." Computers & Operations Research 36, no. 9 (September 2009): 2565–72. http://dx.doi.org/10.1016/j.cor.2008.11.006.

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40

Brenner, Susanne C. "An additive Schwarz preconditioner for the FETI method." Numerische Mathematik 94, no. 1 (March 1, 2003): 1–31. http://dx.doi.org/10.1007/s002110200397.

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41

Wang, Weijun, Chen Zheng, Feng Tang, and Yicha Zhang. "A practical redesign method for functional additive manufacturing." Procedia CIRP 100 (2021): 566–70. http://dx.doi.org/10.1016/j.procir.2021.05.124.

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42

Hansun, Seng, Vincent Charles, Christiana Rini Indrati, and Subanar. "Revisiting the Holt-Winters' Additive Method for Better Forecasting." International Journal of Enterprise Information Systems 15, no. 2 (April 2019): 43–57. http://dx.doi.org/10.4018/ijeis.2019040103.

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Time series are one of the most common data types encountered by data scientists and, in the context of today's exponentially increasing data, learning how to best model them to derive meaningful insights is an important skill in the Big Data and Data Science toolbox. As a result, many researchers have dedicated their efforts to developing time series analysis methods to predict future values based on previously observed values. One of the well-known methods is the Holt-Winters' seasonal method, which is commonly used to capture the seasonality effect in time series data. In this study, the authors aim to build upon the Holt-Winters' additive method by introducing new formulas for finding the initial values. Obtaining more accurate estimations of the initial values could result in a better forecasting result. The authors use the basic principle found in the weighted moving average method to assign more weight to the most recent data and combine it with the original initial conditions found in the Holt-Winters' additive method. Based on the experiment performed, the authors conclude that the new formulas for finding the initial values in the Holt-Winters' additive method could give a better forecasting when compared to the traditional Holt-Winters' additive method and the weighted moving average method in terms of the accuracy level.

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43

Нагулин, К., K. Nagulin, С. Курынцев, S. Kuryntsev, А. Горунов, and A. Gorunov. "Additive Techniques – the third industrial revolution." Science intensive technologies in mechanical engineering 1, no. 7 (July 4, 2016): 39–44. http://dx.doi.org/10.12737/20599.

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This paper reports two basic methods of additive techniques, a method of layer-by-layer agglomeration or metal powder melting and a method based on metal surfacing technique. A thorough description of the routine methods of additive techniques their advantages and disadvantages, a product range and field of product application are presented. Basic problems arising at the use of additive techniques and produce manufactured such as the certification of initial material, technology certification, production process control in its development, test procedure of parts manufactured are touched upon. The peculiarities of behavior of blanks obtained through additive techniques at further technological operations, for example, at laser welding are described. Basic trends in researches carried out by leading scientists in the field mentioned are emphasized.

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44

Balic Zunic, T. "Addition Method in the Quantitative Analysis: Dependence of Error on the Quantity of Additive." Materials Science Forum 228-231 (July 1996): 43–48. http://dx.doi.org/10.4028/www.scientific.net/msf.228-231.43.

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45

Wieczorek, Marcin, Marek Dębosz, Paweł Świt, Aleksandra Piech, Joanna Kasperek, and Paweł Kościelniak. "Reliable calibration by nonlinear standard addition method in the presence of additive interference effects." Monatshefte für Chemie - Chemical Monthly 149, no. 9 (August 7, 2018): 1567–72. http://dx.doi.org/10.1007/s00706-018-2203-1.

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46

Kapil, Sajan, Prathamesh Joshi, Hari Vithasth Yagani, Dhirendra Rana, Pravin Milind Kulkarni, Ranjeet Kumar, and K. P. Karunakaran. "Optimal space filling for additive manufacturing." Rapid Prototyping Journal 22, no. 4 (June 20, 2016): 660–75. http://dx.doi.org/10.1108/rpj-03-2015-0034.

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Purpose In additive manufacturing (AM) process, the physical properties of the products made by fractal toolpaths are better as compared to those made by conventional toolpaths. Also, it is desirable to minimize the number of tool retractions. The purpose of this study is to describe three different methods to generate fractal-based computer numerical control (CNC) toolpath for area filling of a closed curve with minimum or zero tool retractions. Design/methodology/approach This work describes three different methods to generate fractal-based CNC toolpath for area filling of a closed curve with minimum or zero tool retractions. In the first method, a large fractal square is placed over the outer boundary and then rest of the unwanted curve is trimmed out. To reduce the number of retractions, ends of the trimmed toolpath are connected in such a way that overlapping within the existing toolpath is avoided. In the second method, the trimming of the fractal is similar to the first method but the ends of trimmed toolpath are connected such that the overlapping is found at the boundaries only. The toolpath in the third method is a combination of fractal and zigzag curves. This toolpath is capable of filling a given connected area in a single pass without any tool retraction and toolpath overlap within a tolerance value equal to stepover of the toolpath. Findings The generated toolpath has several applications in AM and constant Z-height surface finishing. Experiments have been performed to verify the toolpath by depositing material by hybrid layered manufacturing process. Research limitations/implications Third toolpath method is suitable for the hybrid layered manufacturing process only because the toolpath overlapping tolerance may not be enough for other AM processes. Originality/value Development of a CNC toolpath for AM specifically hybrid layered manufacturing which can completely fill any arbitrary connected area in single pass while maintaining a constant stepover.

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47

Zhang, Yicha, Ramy Harik, Georges Fadel, and Alain Bernard. "A statistical method for build orientation determination in additive manufacturing." Rapid Prototyping Journal 25, no. 1 (January 7, 2019): 187–207. http://dx.doi.org/10.1108/rpj-04-2018-0102.

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Purpose For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination. Design/methodology/approach The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined. Findings A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency. Originality/value The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

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48

Leal, João Paulo. "Additive Methods for Prediction of Thermochemical Properties. The Laidler Method Revisited. 1. Hydrocarbons." Journal of Physical and Chemical Reference Data 35, no. 1 (March 2006): 55–76. http://dx.doi.org/10.1063/1.1996609.

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49

Deng, Xian Guo, Gui Hong Pei, and Zi Tian Zheng. "The Determination of Totall Station's Additive Constant with Triangle Method." Applied Mechanics and Materials 99-100 (September 2011): 1319–24. http://dx.doi.org/10.4028/www.scientific.net/amm.99-100.1319.

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Aiming at the situation that surveyors are difficult to determinate the additive constant and unable to set corresponding constant of the totall station when the lens's constant is unknown, propose the triangle law, which could determinate the lens's additive constant neglecting the effection of multiplication constant. As the triangle law's model is quite complex, it is too difficult to known the formula of precision, monte-carlo principle is used to calculate the additive constant and assess additive constant's accuracy in this model. Finally, illustrated the application of triangle method by an example.

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50

Narasimman, P., and R. Amuda. "A New Method to Modelling the Additive Functional Equations." Applied Mathematics & Information Sciences 10, no. 3 (May 1, 2016): 1047–51. http://dx.doi.org/10.18576/amis/100322.

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