QUALITY CONTROL SYSTEM FOR THE TECHNOLOGICAL PROCESS OF THE BAKERY PRODUCTS MANUFACTURE BASED ON FUZZY LOGIC MODELS

Author(s):  
M.A. ALLYAMSHIN ◽  
◽  
P.M. MURASHEV ◽  

The paper considers a fuzzy model of a system for the control of bakery products technology, as one of the tools for use in an expert system for making decisions. On the grounds of the selected parameters the technological process of the manufacture of bakery products in a small bakery is evaluated and the results of experiments and the operation of the fuzzy control model are compared.

2020 ◽  
Vol 10 (18) ◽  
pp. 6565
Author(s):  
Risky Ayu Febriani ◽  
Hong-Seok Park ◽  
Chang-Myung Lee

Currently, challenges in quality improvement have driven various enterprises to create quality management systems in smart factories. The development of quality management systems enables quality control for reviewing product quality, identification, and eliminating product failures. However, process adjustment in quality control decisions may be hard to determine when failures are detected. To overcome this problem, an expert system (ES) that applies the failure mode and effects analysis (FMEA) method for developing quality control systems in brake disc production lines is considered. This quality control system concentrates on analyzing product defects that occur frequently in the production line and will lead to an improved performance of the braking system; the selected product defects are disc thickness variation (DTV), runout, and parallelism. This quality control system developed two modules, the designed FMEA (DFMEA) and component FMEA, which apply a rule-based algorithm for selecting actions. We propose the rules of configuration into the expert system code. The results indicate that the operator can carry out a quality control system with decision-making that can be supported by intelligent searching and reasoning in an expert system.


Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Baoping Zou ◽  
Jianxiu Wang ◽  
Zhanyou Luo ◽  
Lisheng Hu

The construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction quality control. This paper presents the construction principle of a tunnel smooth blasting quality control system, introduces a process quality control technology into quality control of tunnel smooth blasting construction, designs a framework for the tunnel smooth blasting quality control system, and collects control index data based on field investigation, expert consultation, and experimental research. By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. The following are the conclusions of this study: (1) This paper presented a method of constructing a tunnel smooth blasting quality control index system and established this system with seven criteria layers, namely, geological conditions, explosive properties, borehole parameters, charge parameters, method of initiation, tunnel parameters, and construction factors, as well as a total of nine indexes. (2) The comprehensive optimization control model of BP-ANN, ANFIS, and ENN for tunnel smooth blasting quality was established. (3) The uniform design method was used to optimize the blasting parameters of the tunnel section, which needs to be controlled, and verify that the construction of these comprehensive optimization control models can change the focus of tunnel smooth blasting quality control from the traditional single index control method into a dynamic, intelligent, pluralistic, and integrated control technology.


Author(s):  
Roger Olivella ◽  
Cristina Chiva ◽  
Marc Serret ◽  
Daniel Mancera ◽  
Luca Cozzuto ◽  
...  

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