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Journal articles on the topic 'Process Variable'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Goldfarb, Heidi B., Connie M. Borror, and Douglas C. Mongomery. "Mixture-Process Variable Experiments with Noise Variables." Journal of Quality Technology 35, no. 4 (2003): 393–405. http://dx.doi.org/10.1080/00224065.2003.11980237.

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2

Pipat, PHAISALPANUMAS, Keiichi KIMURA, Keisuke SUZUKI, and KHAJORNRUNGRUANG Panart. "608 Study on Variable Rotation Polishing in CMP Process." Proceedings of Conference of Kyushu Branch 2012.65 (2012): 207–8. http://dx.doi.org/10.1299/jsmekyushu.2012.65.207.

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3

Kim, Jihyun, Qiang Huang, and Jianjun Shi. "Latent variable based key process variable identification and process monitoring for forging." Journal of Manufacturing Systems 26, no. 1 (2007): 53–61. http://dx.doi.org/10.1016/j.jmsy.2007.12.001.

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4

Eryurek, Evren. "Process device diagnostics using process variable sensor signal." Journal of the Acoustical Society of America 115, no. 1 (2004): 18. http://dx.doi.org/10.1121/1.1646990.

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5

von Hippel, Eric. "Task partitioning: An innovation process variable." Research Policy 19, no. 5 (1990): 407–18. http://dx.doi.org/10.1016/0048-7333(90)90049-c.

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6

He, Xiao Bin, and Yu Pu Yang. "Variable MWPCA for Adaptive Process Monitoring." Industrial & Engineering Chemistry Research 47, no. 2 (2008): 419–27. http://dx.doi.org/10.1021/ie070712z.

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7

Kondo, Yasuo, Yuko Itoh, Mitsugu Yamaguchi, Satoshi Sakamoto, and Kenji Yamaguchi. "Prediction Model of Power Consumption for Variable Material Removal Rate Machining Process." International Journal of Materials, Mechanics and Manufacturing 7, no. 2 (2019): 68–71. http://dx.doi.org/10.18178/ijmmm.2019.7.2.432.

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8

LEE, M. K., S. H. HONG, H. M. KWON, and S. B. KIM. "OPTIMUM PROCESS MEAN AND SCREENING LIMITS FOR A PRODUCTION PROCESS WITH THREE-CLASS SCREENING." International Journal of Reliability, Quality and Safety Engineering 07, no. 03 (2000): 179–90. http://dx.doi.org/10.1142/s021853930000016x.

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The optimum process mean and screening limits are provided for a production process where the accepted item is sold in one of two alternative markets and the rejected item is reworked. All items are subject to screening where the screening variable may be the major quality characteristic of interest (performance variable) or the surrogate variable, which is highly correlated with the performance variable. Each item is classified into three quality grades A, B, and C: grade A items are sold to primary market, grade B items are sold to secondary market, and grade C items are reworked by the same manufacturing process. Assuming that performance and surrogate variables are jointly normally distributed, two profit models are constructed which include selling price, production, inspection, rework and penalty costs. Methods of finding the optimum process mean and the screening limits are presented and an illustrative example is given.

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9

Glynn, Adam N., and Kevin M. Quinn. "Why Process Matters for Causal Inference." Political Analysis 19, no. 3 (2011): 273–86. http://dx.doi.org/10.1093/pan/mpr021.

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Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions conditional on the value of a posttreatment variable than they would be able to do unconditionally. As data become available on a posttreatment variable, these conditional causal assumptions become active and information about the effect of interest is gained. This approach is most beneficial in situations where it is implausible to assume that treatment assignment is conditionally ignorable. We illustrate the approach with an example of estimating the effect of election day registration on turnout.

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10

Jin, Gang, Qichang Zhang, Shuying Hao, and Qizhi Xie. "Stability prediction of milling process with variable pitch and variable helix cutters." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 2 (2013): 281–93. http://dx.doi.org/10.1177/0954406213486381.

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The use of variable pitch or helix cutters is a known means to prevent chatter vibration during milling. In this article, an alternative method based on an improved semi-discretization method is proposed to predict the stability of variable pitch or variable helix milling. In order to consider the effect of distributed system delays attributed to helix variation, the average delays were calculated for each flute after the engaged cutting flutes were divided into a finite number of axial elements. Meanwhile, a straightforward integral force model, which can consider the piecewise continuous regions of the cutting that describe the helix angle is used to determine the cutting force. Through comparisons with prior works, time-domain simulations, and cutting tests, the proposed approach was verified. In addition, the method was applied to examine the effect of tool geometries on stability trends. Several phenomena for certain combinations of pitch and helix angles are shown and explained.

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11

Scali, C., A. Landi, G. Nardi, and A. Balestrino. "Variable Structure Regulators for Industrial Process Control." IFAC Proceedings Volumes 25, no. 25 (1992): 57–62. http://dx.doi.org/10.1016/s1474-6670(17)49579-6.

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12

Niemi, A. J. "Process Control Under Variable Flow and Volume." IFAC Proceedings Volumes 24, no. 1 (1991): 55–61. http://dx.doi.org/10.1016/s1474-6670(17)51296-3.

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13

Song, Guoli, Shuhui Wang, Qingming Huang, and Qi Tian. "Multimodal Similarity Gaussian Process Latent Variable Model." IEEE Transactions on Image Processing 26, no. 9 (2017): 4168–81. http://dx.doi.org/10.1109/tip.2017.2713045.

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14

Stodolsky, L. "Measurement process in a variable-barrier system." Physics Letters B 459, no. 1-3 (1999): 193–200. http://dx.doi.org/10.1016/s0370-2693(99)00659-0.

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15

Klyuchnikov, Nikita, and Evgeny Burnaev. "Gaussian process classification for variable fidelity data." Neurocomputing 397 (July 2020): 345–55. http://dx.doi.org/10.1016/j.neucom.2019.10.111.

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16

Yoon, H., M. R. Stouffer, W. A. Rosenhoover, J. A. Withum, and F. P. Burke. "Pilot process variable study of coolside desulfurization." Environmental Progress 7, no. 2 (1988): 104–11. http://dx.doi.org/10.1002/ep.3300070211.

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17

Nounou, Mohamed N., Bhavik R. Bakshi, Prem K. Goel, and Xiaotong Shen. "Process modeling by Bayesian latent variable regression." AIChE Journal 48, no. 8 (2002): 1775–93. http://dx.doi.org/10.1002/aic.690480818.

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18

Vaughan, Timothy S. "Variable sampling interval np process control chart." Communications in Statistics - Theory and Methods 22, no. 1 (1992): 147–67. http://dx.doi.org/10.1080/03610929308831011.

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19

González, Isabel, and Ismael Sánchez. "Variable Selection for Multivariate Statistical Process Control." Journal of Quality Technology 42, no. 3 (2010): 242–59. http://dx.doi.org/10.1080/00224065.2010.11917822.

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20

Pourmasoumi, Asef, Mohsen Kahani, and Ebrahim Bagheri. "Mining variable fragments from process event logs." Information Systems Frontiers 19, no. 6 (2016): 1423–43. http://dx.doi.org/10.1007/s10796-016-9662-x.

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21

Siekirk, John F. "Process variable effects on sheet metal quality." Journal of Applied Metalworking 4, no. 3 (1986): 262–69. http://dx.doi.org/10.1007/bf02833934.

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22

Boiko, I. "Variable-structure PID controller for level process." Control Engineering Practice 21, no. 5 (2013): 700–707. http://dx.doi.org/10.1016/j.conengprac.2012.04.004.

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23

Rodrigues, Rafaella F., Sergio Leite, Juliana G. Oliveira, Carlos Henrique Ataíde, and Marcos A. S. Barrozo. "Process Variable Influence on Fertilizer Drum Granulation Performance." Materials Science Forum 727-728 (August 2012): 1734–39. http://dx.doi.org/10.4028/www.scientific.net/msf.727-728.1734.

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The main purpose of this study was to determine the influence of process variables on granulation of ordinary super phosphate. The experiments were performed in a drum with an internal volume of 25 l and the evaluated variables were rotation speed, liquid phase, granulation time and fill level of the drum granulator. Central Composite Design (CCD) was used to choose the experimental conditions. The correlation between data and process variables was established using Multiple Regression. The granulation products were analyzed using standard separators and the Haver CPA photo-optical particle size measurement system. The tendency toward granulation and formation of coarse or fines could be predicted according to the specific surface of the granulation product and the granulation size distribution was found to be greatly dependent on the variable studies.

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24

Sonnleitner, B., G. Locher, and A. Fiechter. "On the Variable Facets of Process Data in a Process Computer." IFAC Proceedings Volumes 25, no. 2 (1992): 141–45. http://dx.doi.org/10.1016/s1474-6670(17)50340-7.

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25

Campanacho, Vanessa, Andrew T. Chamberlain, and Hugo F. V. Cardoso. "Postnatal maturation of the sternum in a Portuguese skeletal sample: a variable ossification process." Anthropologischer Anzeiger 76, no. 4 (2019): 319–31. http://dx.doi.org/10.1127/anthranz/2019/0966.

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26

KWON, HYUCK-MOO, SUNG-HOON HONG, MIN-KOO LEE, and SANG-BOO KIM. "A process monitoring procedure based on a surrogate variable for dichotomous performance variable." IIE Transactions 33, no. 12 (2001): 1129–33. http://dx.doi.org/10.1080/07408170108936902.

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27

Cho, Tae Yeon, Connie M. Borror, and Douglas C. Montgomery. "Mixture-process variable experiments including control and noise variables within a split-plot structure." International Journal of Quality Engineering and Technology 2, no. 1 (2011): 1. http://dx.doi.org/10.1504/ijqet.2011.038719.

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28

Rahman, Md Musfiqur, Syed Ahmad Imtiaz, and Kelly Hawboldt. "A hybrid input variable selection method for building soft sensor from correlated process variables." Chemometrics and Intelligent Laboratory Systems 157 (October 2016): 67–77. http://dx.doi.org/10.1016/j.chemolab.2016.06.015.

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29

Aldrich, Chris. "Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework." Minerals 10, no. 5 (2020): 420. http://dx.doi.org/10.3390/min10050420.

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Linear regression is often used as a diagnostic tool to understand the relative contributions of operational variables to some key performance indicator or response variable. However, owing to the nature of plant operations, predictor variables tend to be correlated, often highly so, and this can lead to significant complications in assessing the importance of these variables. Shapley regression is seen as the only axiomatic approach to deal with this problem but has almost exclusively been used with linear models to date. In this paper, the approach is extended to random forests, and the results are compared with some of the empirical variable importance measures widely used with these models, i.e., permutation and Gini variable importance measures. Four case studies are considered, of which two are based on simulated data and two on real world data from the mineral process industries. These case studies suggest that the random forest Shapley variable importance measure may be a more reliable indicator of the influence of predictor variables than the other measures that were considered. Moreover, the results obtained with the Gini variable importance measure was as reliable or better than that obtained with the permutation measure of the random forest.

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30

Oberle, Thomas, Christoph Ziegler, Robert Thieme, and Martin Porschen. "Substrate Transport for Production at Variable Process Speeds." NIP & Digital Fabrication Conference 2018, no. 1 (2018): 103–6. http://dx.doi.org/10.2352/issn.2169-4451.2018.34.103.

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31

M., HUSSEIN, MAC-GREGOR F., MANSOUR M., and ELBESTAWI A. "MACHINING PROCESS PLANNING THROUGH LATENT VARIABLE MODEL INVERSION." International Conference on Applied Mechanics and Mechanical Engineering 13, no. 13 (2008): 135–55. http://dx.doi.org/10.21608/amme.2008.39734.

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32

Li, Gang, Baosheng Liu, S. Joe Qin, and Donghua Zhou. "Dynamic latent variable modeling for statistical process monitoring." IFAC Proceedings Volumes 44, no. 1 (2011): 12886–91. http://dx.doi.org/10.3182/20110828-6-it-1002.00934.

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33

Tian, L., A. J. Niemi, and R. Ylinen. "Recursive Process Identification Under Variable Flow and Volume." IFAC Proceedings Volumes 25, no. 5 (1992): 339–44. http://dx.doi.org/10.1016/s1474-6670(17)51015-0.

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34

Kindlundh, Maria, Peter Norlin, and Ulrich G. Hofmann. "A neural probe process enabling variable electrode configurations." Sensors and Actuators B: Chemical 102, no. 1 (2004): 51–58. http://dx.doi.org/10.1016/j.snb.2003.10.009.

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35

Yamane, Masayuki, and Minoru Inami. "Variable refractive index systems by sol-gel process." Journal of Non-Crystalline Solids 147-148 (January 1992): 606–13. http://dx.doi.org/10.1016/s0022-3093(05)80685-4.

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36

Cherkauer, Keith A., Laura C. Bowling, and Dennis P. Lettenmaier. "Variable infiltration capacity cold land process model updates." Global and Planetary Change 38, no. 1-2 (2003): 151–59. http://dx.doi.org/10.1016/s0921-8181(03)00025-0.

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37

Faouzi Harkat, M., Gilles Mourot, and José Ragot. "Variable Reconstruction Using RBF-NLPCA for Process Monitoring." IFAC Proceedings Volumes 36, no. 5 (2003): 1131–36. http://dx.doi.org/10.1016/s1474-6670(17)36645-4.

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38

Hallinan, Nathaniel. "The generalized time variable reconstructed birth–death process." Journal of Theoretical Biology 300 (May 2012): 265–76. http://dx.doi.org/10.1016/j.jtbi.2012.01.041.

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39

Li, Ping, and Songcan Chen. "A review on Gaussian Process Latent Variable Models." CAAI Transactions on Intelligence Technology 1, no. 4 (2016): 366–76. http://dx.doi.org/10.1016/j.trit.2016.11.004.

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40

Dainson, B. E., B. Tartakovsky, D. R. Lewin, and M. Sheintuch. "Variable Structure Models in Process Observation and Control." Industrial & Engineering Chemistry Research 34, no. 9 (1995): 3008–13. http://dx.doi.org/10.1021/ie00048a012.

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41

Gharehpapagh, Bahar, Melik Dolen, and Ulas Yaman. "Investigation of Variable Bead Widths in FFF Process." Procedia Manufacturing 38 (2019): 52–59. http://dx.doi.org/10.1016/j.promfg.2020.01.007.

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42

Newberry, Alice M., James F. Alexander, and Charles W. Turner. "Gender as a process variable in family therapy." Journal of Family Psychology 5, no. 2 (1991): 158–75. http://dx.doi.org/10.1037/0893-3200.5.2.158.

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43

Foxall, Eric, and Nicolas Lanchier. "Generalized stacked contact process with variable host fitness." Journal of Applied Probability 57, no. 1 (2020): 97–121. http://dx.doi.org/10.1017/jpr.2019.79.

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AbstractThe stacked contact process is a three-state spin system that describes the co-evolution of a population of hosts together with their symbionts. In a nutshell, the hosts evolve according to a contact process while the symbionts evolve according to a contact process on the dynamic subset of the lattice occupied by the host population, indicating that the symbiont can only live within a host. This paper is concerned with a generalization of this system in which the symbionts may affect the fitness of the hosts by either decreasing (pathogen) or increasing (mutualist) their birth rate. Standard coupling arguments are first used to compare the process with other interacting particle systems and deduce the long-term behavior of the host–symbiont system in several parameter regions. The spatial model is also compared with its mean-field approximation as studied in detail by Foxall (2019). Our main result focuses on the case where unassociated hosts have a supercritical birth rate whereas hosts associated to a pathogen have a subcritical birth rate. In this case, the mean-field model predicts coexistence of the hosts and their pathogens provided the infection rate is large enough. For the spatial model, however, only the hosts survive on the one-dimensional integer lattice.

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44

Horowitz, Mardi, Robert Rosenbaum, and Nancy Wilner. "Role relationship dilemmas: A potential new process variable." Psychotherapy: Theory, Research, Practice, Training 25, no. 2 (1988): 241–48. http://dx.doi.org/10.1037/h0085338.

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45

Yue, Yu Ryan, and Ji Meng Loh. "Variable selection for inhomogeneous spatial point process models." Canadian Journal of Statistics 43, no. 2 (2015): 288–305. http://dx.doi.org/10.1002/cjs.11244.

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46

Dmitrenko, V. A., N. N. Karasev, É. S. Putilin, L. Glebov, and V. Smirnov. "A process for creating mirrors with variable reflectance." Journal of Optical Technology 73, no. 3 (2006): 202. http://dx.doi.org/10.1364/jot.73.000202.

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47

Titchener, M. R., and J. J. Hunter. "Synchronisation process for the variable-length T-codes." IEE Proceedings E Computers and Digital Techniques 133, no. 1 (1986): 54. http://dx.doi.org/10.1049/ip-e.1986.0005.

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48

El Gmati, I., P. Calmon, R. Fulcrand, et al. "Variable RF MEMS fluidic inductor incorporating lamination process." Micro & Nano Letters 5, no. 6 (2010): 370. http://dx.doi.org/10.1049/mnl.2010.0131.

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49

Zhou, Le, Gang Li, Zhihuan Song, and S. Joe Qin. "Autoregressive Dynamic Latent Variable Models for Process Monitoring." IEEE Transactions on Control Systems Technology 25, no. 1 (2017): 366–73. http://dx.doi.org/10.1109/tcst.2016.2550426.

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50

Cansizoglu, Mehmet F., Emad Badradeen, Gwo-Ching Wang, and Tansel Karabacak. "Enhanced hydrogen storage by a variable temperature process." International Journal of Hydrogen Energy 44, no. 7 (2019): 3771–78. http://dx.doi.org/10.1016/j.ijhydene.2018.12.087.

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