A New Knowledge Discovery Model for Extracting Diagnosis Rules of Manufacturing Process

2006 ◽  
Vol 505-507 ◽  
pp. 889-894
Author(s):  
Ying Chieh Tsai ◽  
Ching Hsue Cheng ◽  
Jing Rong Chang
Keyword(s):  
Knowledge Discovery ◽  
Manufacturing Process ◽  
Conveyor Belt ◽  
Membership Functions ◽  
Minimum Entropy ◽  
Manufacturing Line ◽  
Proposed Model ◽  
New Knowledge ◽  
Entropy Principle ◽  
Correlation Based Feature Selection

The knowledge obtained from the experience of monitoring manufacturing process is critical to guarantee good products produced at the end of manufacturing line. Recently, many methods have been developed for the described purpose above. In this paper, a new knowledge discovery model based on soft computing is proposed. The proposed model contains a new algorithm Modified Correlation-based Feature Selection (MCFS), a new algorithm Modified Minimum Entropy Principle Algorithm (MMEPA), and Variable Precision Rough Set Model (VP-model). After conducting a real case of monitoring the process of manufacturing industrial conveyor belt, some advantages of the proposed model are that (1) MCFS can quickly identifying and screening irrelevant, redundant, and noisy features for data reduction; (2) MMEPA can objectively construct membership functions of fuzzy sets for fuzzifing the reduced dataset; (3) VP-model can extract causal relationship rules for controlling product quality; (4) Extracted rules by the proposed knowledge discovery model are easily understood and interpretable.

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

Modification of Jig Design to Improve the Ergonomic Rating in Manufacturing Process

2011 ◽  
Vol 121-126 ◽  
pp. 1377-1381
Author(s):  
Teng Gang Xu
Keyword(s):  
Manufacturing Process ◽  
Manufacturing Line ◽  
Manufacturing Company ◽  
Current Scenario

Ergonomics is the study of the job, equipment and workplace in order to make them suitable and convenient to the workers. At an appliance manufacturing company the ergonomic rating of a worker’s hands and arms movement was higher above the desired value. Moreover, there were accidents that occurred on these lines sometimes. This paper analyzed the current scenario and developed a new design carried out on manufacturing Line 1 and Line 2 at that company to make the platform and jig more ergonomic to the operators.

Dynamic Quality Prediction of Manufacturing Process Based on Extreme Learning Machine

2014 ◽  
Vol 889-890 ◽  
pp. 1231-1235
Author(s):  
Jun Guo ◽  
Yi Bing Li ◽  
Bai Gang Du
Keyword(s):  
Extreme Learning Machine ◽  
Manufacturing Process ◽  
Manufacturing Processes ◽  
Manufacturing Cost ◽  
Quality Prediction ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Learning Machine ◽  
Dynamic Quality ◽  
Quality Categories

In many manufacturing processes, the abnormal changes of some key process parameters could result in various categories of faulty products. In this paper, a machine learning approach is developed for dynamic quality prediction of the manufacturing processes. In the proposed model, an extreme learning machine is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. The proposed model is successfully applied to a japanning-line, which improves the product quality and saves manufacturing cost.

scholarly journals A Metaheuristic Approach to Manufacturing Process Planning in Reconfigurable Manufacturing Systems

2012 ◽  
Author(s):  
Farayi Musharavati ◽  
Napsiah Ismail ◽  
Abdel Majid S. Hamouda ◽  
Abdul Rahman Ramli
Keyword(s):  
Simulated Annealing ◽  
Process Planning ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Solution Technique ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Analytical Functions ◽  
Manufacturing Line ◽  
Manufacturing Process Planning

Proses perancangan pembuatan adalah berkaitan dengan keputusan berdasarkan pemilihan tatarajah yang optimum daripada modul proses untuk pemprosesan bahagian kerja. Untuk pembentukan semula barisan pembuatan bagi pelbagai bahagian kerja, keputusannya dipengaruhi jenis proses yang sedia ada, hubungkait jujukan pemprosesan dan juga aturan pemprosesan bahagian kerja tersebut. Keputusan proses perancangan pembuatan mungkin bercanggah, oleh itu tugasan membuat keputusan perlu mengambil kira cara setemu. Kertas kerja ini membentangkan teknik optima untuk masalah berkaitan proses perancangan pembuatan dalam rangka kerja pembuatan pembentukan semula. Proses MPP dimodelkan sebagai masalah pengoptimuman dan keadah penyelesaian yang diperolehi daripada teknik metahuristik dikenali sebagai simulasi penyepuhlindapan. Fungsi analisis bagi memodel proses perancangan pembuatan adalah berdasarkan pengetahuan mengenai proses dan sistem pembuatan serta kekangan proses. Applikasi bagi pendekatan ini ditunjukkan melalui barisan pembuatan pembentukan semula berbilang tahap siri selari. Keputusan menunjukkan penambahbaik yang signifikasi diperolehi dalam penyelesaian untuk masalah jenis ini dengan menggunakan simulasi penyepuhlindapan. Tambahan pula, teknik metaheuristik berkebolehan untuk mengenal pasti kaedah proses pembuatan yang optima berdasarkan senario pengeluaran yang diberi. Kata kunci: Metaheuristik, simulasi penyepuhlindapan, proses perancangan pembuatan, sistem pembuatan pembentukan semula, senario pembuatan Manufacturing process planning (MPP) is concerned with decisions regarding selection of an optimal configuration for processing parts. For multiparts reconfigurable manufacturing lines, such decisions are strongly influenced by the types of processes available, the relationships for sequencing the processes and the order of processing parts. Decisions may conflict, hence the decision making tasks must be carried out in a concurrent manner. This paper outlines an optimization solution technique for the MPP problem in reconfigurable manufacturing systems (RMSs). MPP is modelled in an optimization perspective and the solution methodology is provided through a metaheuristic technique known as simulated annealing. Analytical functions for modelling MPP are based on knowledge of processes available to the manufacturing system as well as processing constraints. Application of this approach is illustrated through a multistage parallel–serial reconfigurable manufacturing line. The results show that significant improvements to the solution of this type of problem can be gained through the use of simulated annealing. Moreover, the metaheuristic technique is able to identify an optimal manufacturing process plan for a given production scenario. Key words: Metaheuristics, simulated annealing, manufacturing process planning, reconfigurable manufacturing systems, production scenarios

scholarly journals An Entropic Scheme for an Angular Moment Model for the Classical Fokker-Planck-Landau Equation of Electrons

2014 ◽  
Vol 15 (2) ◽  
pp. 422-450 ◽  
Author(s):  
Jessy Mallet ◽  
Stéphane Brull ◽  
Bruno Dubroca
Keyword(s):  
Discrete Model ◽  
Landau Equation ◽  
Numerical Test ◽  
Test Cases ◽  
Minimum Entropy ◽  
Fundamental Properties ◽  
Direct Numerical Solution ◽  
Entropy Principle ◽  
The Moment ◽  
Fokker Planck

AbstractIn plasma physics domain, the electron transport is described with the Fokker-Planck-Landau equation. The direct numerical solution of the kinetic equation is usually intractable due to the large number of independent variables. That is why we propose in this paper a new model whose derivation is based on an angular closure in the phase space and retains only the energy of particles as kinetic dimension. To find a solution compatible with physics conditions, the closure of the moment system is obtained under a minimum entropy principle. This model is proved to satisfy the fundamental properties like a H theorem. Moreover an entropic discretization in the velocity variable is proposed on the semi-discrete model. Finally, we validate on numerical test cases the fundamental properties of the full discrete model.

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