Tkaczyk S, Jagla J (2001) Economic aspects of the implementation of a quality system process in polish enterprises. J Mater Process Technol 109:196–205. https://doi.org/10.1016/S0924-0136(00)00796-2
Article
Google Scholar
Kendirli S, Tuna M (2009) Quality cost’s constitution and effects on financial decision in enterprises: a research in Corum’s enterprises. Proceedings of the Academy of Accounting and Financial Studies 14:21–32
Google Scholar
Murumkar A, Teli SN, Loni RR (2018) Framework for reduction of quality cost. international journal of research in engineering application & management 156–162
Maslak O, Grishko N, Maslak M, Skliar M (2020) Quality costs of machine-building enterprises in Ukraine: a control mechanism. Technium Soc Sci J 9:326–336. https://doi.org/10.47577/tssj.v9i1.1163
Article
Google Scholar
Al-Ghanim A, Jordan J (1996) Automated process monitoring using statistical pattern recognition techniques on x-bar control charts. J Qual Maint Eng 2:25–49. https://doi.org/10.1108/13552519610113827
Article
Google Scholar
Wu Z, Shamsuzzaman M (2005) Design and application of integrated control charts for monitoring process mean and variance. J Manuf Syst 24:302–314. https://doi.org/10.1016/S0278-6125(05)80015-9
Article
Google Scholar
Kao LJ, Chiu CC (2020) Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process. J Manuf Syst 57:109–118. https://doi.org/10.1016/j.jmsy.2020.07.020
Article
Google Scholar
Montgomery DC (2019) Introduction to statistical quality control, 8th edn. Wiley, Hoboken
Google Scholar
Andalia W, Pratiwi I, Arita S (2019) Analysis of biodiesel conversion on raw material variation using statistical process control method. J Phys Conf Ser 1167:1–7
Article
Google Scholar
Ozdamar IH (2007) Statistical process control in forest products industry: case study on particleboard production process. Turkish J For 8:79–91
Google Scholar
Jalote P, Dinesh K, Raghavan S, Bhashyam MR, Ramakrishnan M (2000) Quantitative quality management through defect prediction and statistical process controlΤ. Electronics, Basel.
EN 309 (2005) Particleboards: definition and classification. European standard.
Istek A, Gozalan M, Ozlusoylu I (2017) The effects of surface coating and painting process on particleboard properties. Kastamonu Univ J For Fac 17:619–629. https://doi.org/10.17475/kastorman.180279
Article
Google Scholar
Sahin HI, Yalcin M, Yaglica N (2017) Determination of screw holding and thermal conductivity values of core layer compost waste additive particleboard. Artvin Coruh Univ J For Fac 18:121–129. https://doi.org/10.17474/artvinofd.320521
Article
Google Scholar
Smardzewski J, Kłos R (2011) Modeling of joint substitutive rigidity of board elements. annals of Warsaw Univesity of life science—SGGW. For Wood Technol 73:7–15
Google Scholar
Kurt R, Karayilmazlar S (2019) Estimating modulus of elasticity (MOE) of particleboards using artificial neural networks to reduce quality measurements and costs. Drvna Industrija 70:257–263. https://doi.org/10.5552/drvind.2019.1840
Article
Google Scholar
Arabi M, Faezipour M, Haftkhani AR, Maleki S (2012) The effect of particle size on the prediction accuracy of screw withdrawal resistance (SWR) models. J Indian Acad Wood Sci 9:53–56. https://doi.org/10.1007/s13196-012-0063-6
Article
Google Scholar
Bobadilla I, Arriaga F, Esteban M, Iñiguez G, Blázquez I (2008) Density estimation by vibration, screw withdrawal resistance and probing in particle and medium density fibre boards. 10th world conference on timber engineering. 3:1423–1430
Semple KE, Smith GD (2006) Prediction of internal bond strength in particleboard from screw withdrawal resistance models. Wood Fiber Sci 38:256–267
CAS
Google Scholar
Bardak S (2018) Predicting the impacts of various factors on failure load of screw joints for particleboard using artificial neural networks. BioResources 13:3868–3879. https://doi.org/10.15376/biores.13.2.3868-3879
Article
CAS
Google Scholar
Haftkhani AR, Arabi M (2013) Improve regression-based models for prediction of internal-bond strength of particleboard using Buckingham’s pi-theorem. J For Res 24:735–740. https://doi.org/10.1007/s11676-013-0412-3
Article
Google Scholar
Tas HH, Cetisli B (2016) Estimation of physical and mechanical properties of composite board via adaptive neural networks, polynomial curve fitting, and the adaptive neuro-fuzzy inference system. BioResources 11:2334–2348. https://doi.org/10.15376/biores.11.1.2334-2348
Article
CAS
Google Scholar
Korai H (2021) Difficulty of internal bond prediction of particleboard using the density profile. J Wood Sci 67:1–7. https://doi.org/10.1186/s10086-021-01994-4
Article
CAS
Google Scholar
Zhang B, Hua J, Cai L, Gao Y, Li Y (2022) Optimization of production parameters of particle gluing on internal bonding strength of particleboards using machine learning technology. J Wood Sci 68:1–11. https://doi.org/10.1186/s10086-022-02029-2
Article
Google Scholar
Arabgol S, Ko HS, Esmaeili S (2015) Artificial neural network and EWMA-based fault prediction in wind turbines. IIE annual conference and expo 2015:829–836
Fehlmann T, Kranich E (2014) Exponentially weighted moving average (EWMA) prediction in the software development process. 2014 Joint Conference of the international workshop on software measurement and the international conference on software process and product measurement 2014:263–270.https://doi.org/10.1109/IWSM.Mensura.2014.50
Alwan LC, Roberts HV (1989) Time-series modeling for statistical process control. J Bus Econ Stat 6:87–95. https://doi.org/10.1080/07350015.1988.10509640
Article
Google Scholar
Wang XA, Mahajan RL (1996) Artificial neural network model-based run-to-run process controller. IEEE Trans Compon Packag Manuf Technol Part C 19:19–26. https://doi.org/10.1109/3476.484201
Article
Google Scholar
Kucukoglu I, Atici-Ulusu H, Gunduz T, Tokcalar O (2018) Application of the artificial neural network method to detect defective assembling processes by using a wearable technology. J Manuf Syst 49:163–171. https://doi.org/10.1016/j.jmsy.2018.10.001
Article
Google Scholar
Asteris PG, Mokos VG (2020) Concrete compressive strength using artificial neural networks. Neural Comput Appl 32:11807–11826. https://doi.org/10.1007/s00521-019-04663-2
Article
Google Scholar
Iannace G, Ciaburro G, Trematerra A (2020) Modelling sound absorption properties of broom fibers using artificial neural networks. Appl Acoust 163:1–9. https://doi.org/10.1016/j.apacoust.2020.107239
Article
Google Scholar
Kurt R (2019) Determination of the most appropriate statistical method for estimating the production values of medium density fiberboard. BioResources 14:6186–6202. https://doi.org/10.15376/biores.14.3.6186-6202
Article
CAS
Google Scholar
Kurt R, Karayilmazlar S, Imren E, Cabuk Y (2017) Forecasting by using artificial neural networks: Turkey’s paper-paperboard industry case. J Bartin Fac For 19:99–106. https://doi.org/10.24011/barofd.334773
Article
Google Scholar
Imren E, Kaygin B, Karayilmazlar S (2021) Evaluation of foreign trade data of Turkish furniture industry with artificial neural networks. J Bartin Fac For 23:906–916. https://doi.org/10.24011/barofd.1011207
Article
Google Scholar
NCSS statistical software (2022) Individuals and moving range charts. https://www.ncss.com/software/ncss/quality-control-in-ncss/. Accessed 21 April 2022
Khoo MBC, Quah SH, Ch’ng CK, (2006) A combined individuals and moving range control chart. J Mod Appl Stat Method 5:248–257. https://doi.org/10.22237/jmasm/1146457140
Article
Google Scholar
Page ES (1954) Continuous inspection schemes. Biometrika 41:100–115. https://doi.org/10.2307/2333009
Article
Google Scholar
Adeoti OA (2013) Application of Cusum control chart for monitoring HIV/AIDS patients in Nigeria. Int J Stat Appl 3:77–80. https://doi.org/10.5923/j.statistics.20130303.07
Article
Google Scholar
Hawkins DM, Olwell DH (1998) Cumulative sum charts and charting for quality improvement. Springer, Berlin. https://doi.org/10.1007/978-1-4612-1686-5
Book
Google Scholar
Ikpotokin O, Braimah JO, Oboh HE (2021) Performance evaluation of conventional exponentially weighted moving average (EWMA) and p-value cumulative sum (CUSUM) control chart. Global J Pure Appl Sci 27:171–179. https://doi.org/10.4314/gjpas.v27i2.9
Article
Google Scholar
Kurt R, Karayilmazlar S (2021) Which control chart is the best for particleboard industry: Shewhart, CUSUM or EWMA? Drewno 64:95–117. https://doi.org/10.12841/wood.1644-3985.382.07
Article
Google Scholar
Aslam M, Shafqat A, Albassam M, Malela-Majika J, Shongwe SC (2021) A new CUSUM control chart under uncertainty with applications in petroleum and meteorology. PLoS ONE 16:1–16. https://doi.org/10.1371/journal.pone.0246185
Article
CAS
Google Scholar
Sunthornwat R, Areepong Y (2020) Average run length on CUSUM control chart for seasonal and non-seasonal moving average processes with exogenous variables. Symmetry 12:1–15. https://doi.org/10.3390/SYM12010173
Article
Google Scholar
En TS (2005) 311, Wood-based panels, surface soundness, test method. Turkish Standards Institution, Ankara
Google Scholar
En TS (1999) 319, Particleboards and fibreboards, determination of tensile strength perpendicular to the plane of the board. Turkish standards institution, Ankara
Google Scholar
En TS (1999) 310, Wood-Based panels, determination of modulus of elasticity in bending and of bending strength. Turkish standards institution, Ankara
Google Scholar
En TS (2011) 320 Particleboards and fibreboards, determination of resistance to axial withdrawal of screws. Turkish standards institution, Ankara
Google Scholar
Haykin S (1999) Neural networks: a comprehensive foundation, 3rd edn. Prentice Hall, Hoboken
Google Scholar
Beale MH, Hagan MT, Demuth HB (2010) Neural network ToolboxTM user’s guide MATLAB. MathWorks 2:77–81
Google Scholar
Kurt R (2018) Integrated use of artificial neural networks and Shewhart, CUSUM and EWMA control charts in statistical process control: a case study in forest industry enterprise. Bartin University, Bartin, 209
Lewis CD (1997) Demand forecasting and inventory control. Routledge, London
Google Scholar
Wang CC, Wang HY, Chen BT, Peng YC (2017) Study on the engineering properties and prediction models of an alkali-activated mortar material containing recycled waste glass. Constr Build Mater 132:130–141. https://doi.org/10.1016/j.conbuildmat.2016.11.103
Article
CAS
Google Scholar
Syafwan H, Syafwan M, Syafwan E, Hadi AF, Putri P (2021) Forecasting unemployment in North Sumatra using double exponential smoothing method. J Phys Conf Ser 1783:1–6. https://doi.org/10.1088/1742-6596/1783/1/012008
Article
Google Scholar
Evans JD (1996) Straightforward statistics for the behavioral sciences. Brooks/Cole, Pacific Grove
Google Scholar
Wuensch KL (1996) Straightforward statistics for the behavioral sciences by James D. Evans. J Am Stat Assoc 91:1750–1751. https://doi.org/10.2307/2291607
Article
Google Scholar
Toneva D, Nikolova S, Georgiev I, Harizanov S, Zlatareva D, Hadjidekov V, Lazarov N (2018) Facial soft tissue thicknesses in Bulgarian adults: relation to sex, body mass index and bilateral asymmetry. Folia Morphol 77:570–582. https://doi.org/10.5603/FM.a2017.0114
Article
CAS
Google Scholar
Rathnayaka IMSK, Dharmapriya TN, Liyandeniya AB, Deeyamulla MP, Priyantha N (2020) Trace metal composition of bulk precipitation in selected locations of Kandy district, Sri Lanka. Water Air Soil Pollut 231:1–12. https://doi.org/10.1007/s11270-020-04840-3
Article
CAS
Google Scholar
Campbell MJ (2021) Statistics at square one, 12th edn. Wiley, Hoboken
Book
Google Scholar
Ferrández-García CE, Andréu-Rodríguez FJ, Ferrández-García MT, Ferrández-Villena M, García-Ortuño T (2010) Effect of press temperature on physical and mechanical properties of particleboard made from giant reed (Arundo donax L.). In International Conference on Agricultural Engineering-AgEng 2010: Towards Environmental Technologies, France. 6–8
Warmbier K, Wilczyński M, Danecki L (2014) Effects of some manufacturing parameters on mechanical properties of particleboards with the core layer made from willow salix viminalis. Annals Warsaw Univ Life Sci SGGW For Wood Technol 88:277–281
Google Scholar
Korkmaz M, Kilinc I, Yapici F, Baydag M (2017) The investigation of the effects of production factors on the screw holding resistance value of oriented strand board (OSB). J Adv Technol Sci 6:940–948
Google Scholar
Widyorini R (2020) Evaluation of physical and mechanical properties of particleboard made from petung bamboo using sucrose-based adhesive. BioResources 15:5072–5086. https://doi.org/10.15376/biores.15.3.5072-5086
Article
CAS
Google Scholar
Kumas I (2013) Production of different conditions on the technological properties of particleboard manufactured from alder (Alnus glutinosa subsp. Barbata). Karadeniz Technical University, Trabzon, 85
Camlibel O (2021) The effect of multi-layers hot press on mechanical properties of particleboard. Turkish J Agric Nat Sci 8:800–807. https://doi.org/10.30910/turkjans.870258
Article
Google Scholar
Kalaycioglu H, Deniz I, Hiziroglu S (2005) Some of the properties of particleboard made from paulownia. J Wood Sci 51:410–414. https://doi.org/10.1007/s10086-004-0665-8
Article
CAS
Google Scholar
Nemli G, Ors Y, Kalaycioglu H (2005) The choosing of suitable decorative surface coating material types for interior end use applications of particleboard. Constr Build Mater 19:307–312. https://doi.org/10.1016/j.conbuildmat.2004.07.015
Article
Google Scholar
Sackey EK, Semple KE, Oh SW, Smith GD (2008) Improving core bond strength of particleboard through particle size redistribution. Wood Fiber Sci 40:214–224
CAS
Google Scholar
Lin CJ, Hiziroglu S, Kan SM, Lai HW (2008) Manufacturing particleboard panels from betel palm (Areca catechu Linn.). J Mater Process Technol 197:445–448. https://doi.org/10.1016/j.jmatprotec.2007.06.048
Article
CAS
Google Scholar
Guruler H, Balli S, Yeniocak M, Goktas O (2015) Estimation the properties of particleboards manufactured from vine prunings stalks using artificial neural networks. Mugla J Sci Technol 1:24–33. https://doi.org/10.22531/muglajsci.209996
Article
Google Scholar
Waelaeh S, Tanrattanakul V, Phunyarat K, Panupakorn P, Junnam K (2017) Effect of polyethylene on the physical and mechanical properties of particleboard. Macromol Symp 371:8–15. https://doi.org/10.1002/masy.201600030
Article
CAS
Google Scholar
Ab Hafidz MY, Mohd AF, Zulkifli M (2018) Mechanical properties and formaldehyde emission of rubberwood particleboard using emulsified methylene diphenyl diisocyanate (EMDI) Binder at different press factor continuous press. Int J Eng Technol 7:335–338. https://doi.org/10.14419/ijet.v7i4.14.27669
Article
Google Scholar
Chung MJ, Wang SY (2019) Physical and mechanical properties of composites made from bamboo and woody wastes in Taiwan. J Wood Sci 65:1–10. https://doi.org/10.1186/s10086-019-1833-1
Article
CAS
Google Scholar
Choupani Chaydarreh K, Lin X, Guan L, Hu C (2022) Interaction between particle size and mixing ratio on porosity and properties of tea oil camellia (Camellia oleifera Abel.) shells-based particleboard. J Wood Sci 68:1–12. https://doi.org/10.1186/s10086-022-02052-3
Article
CAS
Google Scholar
Sampathrajan A, Vijayaraghavan NC, Swaminathan KR (1992) Mechanical and thermal properties of particle boards made from farm residues. Biores Technol 40:249–251. https://doi.org/10.1016/0960-8524(92)90151-M
Article
CAS
Google Scholar