Quality Control of the Continuous Hot Pressing Process of Medium Density Fiberboard Using Fuzzy Failure Mode and Effects Analysis
Source abstract
Abstract
In this paper, a fuzzy failure mode and effects analysis (FMEA) method is proposed by combining fault theory and a failure analysis method. The method addresses the problem of board thickness control failure and the problem of thickness deviation defect blanking, which can occur during continuous hot pressing (CHP) process, which is one of the most important processes in the production of medium-density fiberboard (MDF). The method combines the fault analysis with the Hamming code method and using the Hamming code to calculate and represent the cylinder array of the continuous hot-pressed thickness control execution unit to analyze and process the potential fixed thickness failure modes in MDF hot press production, and then summarizes the decision rules for controlling the board thickness and the level of sheet deviation. By combining the fuzzy FMEA method of the Hamming code and the logical OR operation of the experimental analysis, the method of thickness deviation and recognition control fault information for the CHP of MDF, which is proposed in this paper, permits the increase of the number of error levels, which makes optimization for controller more convenient and improves the efficiency to recognize errors.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Material Properties and Processing · Textile materials and evaluations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
Department of Intelligent Computer Systems, Częstochowa University of Technology, 42-200 Częstochowa, Poland
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an 710054, China
Funding
Key Research and Development Program of Shaanxi Province · Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data
References (37)
Show source-provided references
1. Some advantages of three-layer medium-density fibreboard as compared to the traditional single-layer one10.1186/s10086-019-1822-4 ↗
2. Effects of heat treatment on some properties of MDF (medium-density fiberboard)10.1080/17480272.2015.1073176 ↗
3. Pre-pressing and pre-heating via hot-air/hot-steam process for the production of binderless medium-density fiberboards10.15376/biores.11.3.6613-6624 ↗
4. Wu, Q. (2013). Synergetic Control for Slab Thickness Deviation Rectification of MDF Continuous Press. [Ph.D. Thesis, Northeast Forestry University]. (In Chinese).
5. Impact of hot pressing temperature on medium density fiberboard (MDF) performance10.1155/2017/4056360 ↗
6. Global sliding-mode dynamic surface control for MDF continuous hot pressing slab thickness via LESO10.1007/s13042-018-0804-y ↗
7. Adaptive neural network saturated control for MDF continuous hot pressing hydraulic system with uncertainties10.1109/ACCESS.2017.2782727 ↗
8. Robust adaptive neural prescribed performance control for MDF continuous hot pressing system with input saturation10.1109/ACCESS.2018.2800778 ↗
9. A novel control for MDF continuous hot pressing accurate tracking: Adaptive fuzzy approach10.1504/IJCAT.2019.098038 ↗
10. Error-driven decision and error condition word for continuous hot press flocking control system10.1504/IJMIC.2016.081133 ↗
11. Urbonas, A., Raudonis, V., Maskeliunas, R., and Damaševičius, R. (2019). Automated identification of wood veneer surface defects using faster region-based convolutional neural network with data augmentation and transfer learning. Appl. Sci., 9.10.3390/app9224898 ↗
12. Multi-focusing algorithm for microscopy imagery in assembly line using low-cost camera10.1007/s00170-019-03407-9 ↗
13. Failure mode and effect analysis improvement: A systematic literature review and future research agenda10.1016/j.ress.2020.106885 ↗
14. Extended Flocking Algorithm for Self-parameter Tuning10.1002/ecj.11650 ↗
15. Application of Fuzzy Matrix Correlation and Risk Preference in FMEA Decision Making
16. Hamming Code Specification Analysis Based on Binary Tree Structure
17. Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method10.1016/j.asoc.2020.106134 ↗
18. A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: The case of oil spill10.1080/03088839.2018.1520401 ↗
19. Dong, G., Wei, W., Xia, X., Woźniak, M., and Damaševičius, R. (2020). Safety risk assessment of a Pb-Zn mine based on fuzzy-grey correlation analysis. Electronics, 9.10.3390/electronics9010130 ↗
20. Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels10.1016/j.comnet.2019.04.017 ↗
21. A risk-based fuzzy evidential framework for FMEA analysis under uncertainty: An interval-valued DS approach
22. Liu, S.G., Zhang, X.J., and Lan, Y. (2019). An Improved Assessment Method for FMEA for a Shipboard Integrated Electric Propulsion System Using Fuzzy Logic and DEMATEL Theory. J. Energy, 12.10.3390/en12163162 ↗
23. Failure mode and effects analysis: A comparison of two common risk prioritisation methods10.1136/bmjqs-2015-004130 ↗
24. Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment10.1016/j.ssci.2017.10.018 ↗
25. A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method10.1016/j.engappai.2018.10.005 ↗
26. Shafiee, M., Enjema, E., and Kolios, A. (2019). An integrated FTA-FMEA model for risk analysis of engineering systems: A case study of subsea blowout preventers. Appl. Sci., 9.10.3390/app9061192 ↗
27. Failure mode and effects analysis: An integrated approach based on rough set theory and prospect theory10.1007/s00500-019-04305-8 ↗
28. A new failure mode and effects analysis model using Dempster–Shafer evidence theory and grey relational projection method10.1016/j.engappai.2018.08.010 ↗
29. Evaluating knowledge management failure factors using intuitionistic fuzzy FMEA approach10.1007/s10115-018-1172-3 ↗
30. An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk10.1016/j.jclepro.2018.04.167 ↗
31. FMEA coupling evaluation method based on cloud model and collaborative decision
32. Evaluation of water resources allocation plan in the Yellow River Basin coupled with game theory and DS evidence theory
33. Dempster–Shafer Theory and Statistical Inference with Weak Beliefs
34. Liu, Y.Q., and Wu, Q. (2014, January 3–5). Slab Thickness Deviation Rectification Control of MDF Continuous Press Based on Hierarchical Structure of Synergetic Governance. Proceedings of the International Conference on Mechatronics and Control, Jinzhou, China.
35. Process failure risk assessment based on group decision and generalized Hausdorff distance
36. Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis-technique for order preference by similarity to ideal solution method10.1002/qre.2468 ↗
37. Risk assessment for a floating attitude tension leg platform by application of a hybrid fuzzy-statistical process control model10.1631/jzus.A1900052 ↗