Process Optimization Method for Day Ward Based on Bayesian Decision-Tree

This paper aims at cover body dent resistance optimization problems, developed a whole process method using the finite element simulation method and the corresponding engineering experience to solve the dent resistance problem. Use of Tcl/Tk language to develop the script for fast simulation model consider material nonlinearity and contact nonlinearity, Use Abaqus software to calculate the results, and then customized to optimize use of simplified script parameters on changes in the working conditions of the structure will be optimized. The results show that this set of process optimization method to solve the variable conditions dent resistance is quickly, efficiently and accurately.

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An Explainable Bayesian Decision Tree Algorithm

Frontiers in Applied Mathematics and Statistics ◽

10.3389/fams.2021.598833 ◽

2021 ◽

Vol 7 ◽

Author(s):

Giuseppe Nuti ◽

Lluís Antoni Jiménez Rugama ◽

Andreea-Ingrid Cross

Keyword(s):

Monte Carlo ◽

Decision Tree ◽

Decision Trees ◽

Medical Industry ◽

Data Sets ◽

Bayesian Decision ◽

Decision Tree Algorithm ◽

Probabilistic Framework ◽

Classification Problems ◽

Tree Algorithm

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does not require a pruning step. This algorithm generates the greedy-modal tree (GMT) which is applicable to both regression and classification problems. We tested the algorithm on various benchmark classification data sets and obtained similar accuracies to other known techniques. Furthermore, we show that we can statistically analyze how was the GMT derived from the data and demonstrate this analysis with a financial example. Notably, the GMT allows for a technique that provides explainable simpler models which is often a prerequisite for applications in finance or the medical industry.

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Design Process Optimization Method of Indemnificatory Housing Project Based on DMAIC Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.357-360.2182 ◽

2013 ◽

Vol 357-360 ◽

pp. 2182-2187

Author(s):

Jian Zhu ◽

Peng Mao ◽

Mei Li

Keyword(s):

Process Optimization ◽

Design Process ◽

Large Scale ◽

Optimization Method ◽

Housing Project ◽

Design Requirement

The indemnificatory housing project has many characteristics such as large scale, short-periodic cycle and diverse design requirement. With the aid of DMAIC theory we focus on analyzing and optimizing the design process of housing project, changing the logical relations between the procedures, eliminating unnecessary links and making some enhancement in defective ones, aiming to build up a design process, which the duration and cost are more less and the quality is higher, to meet its particularity.

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Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

Journal of King Saud University - Computer and Information Sciences ◽

10.1016/j.jksuci.2015.11.003 ◽

2016 ◽

Vol 28 (3) ◽

pp. 330-344 ◽

Cited By ~ 18

Author(s):

Muhammad Bilal ◽

Huma Israr ◽

Muhammad Shahid ◽

Amin Khan

Keyword(s):

Decision Tree ◽

Sentiment Classification ◽

Bayesian Decision ◽

Classification Techniques ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Knn Classification

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A Process Optimization Method for High Maturity Process Improvements

2010 International Conference on Management and Service Science ◽

10.1109/icmss.2010.5577138 ◽

2010 ◽

Author(s):

Tianying Chen ◽

Bosheng Zhou ◽

Wenjie Luo

Keyword(s):

Process Optimization ◽

Optimization Method ◽

Process Improvements ◽

High Maturity

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Use of decision-tree induction for process optimization and knowledge refinement of an industrial process

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400000469 ◽

1994 ◽

Vol 8 (1) ◽

pp. 63-75 ◽

Cited By ~ 4

Author(s):

A. Famili

Keyword(s):

Decision Tree ◽

Knowledge Base ◽

Process Optimization ◽

Electrochemical Machining ◽

Industrial Process ◽

Intelligent Manufacturing ◽

Domain Model ◽

Machine Learning Techniques ◽

Decision Tree Induction ◽

Learning Techniques

AbstractDevelopment of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

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EXPERIMENTAL EVALUATION OF THE EFFECTIVENESS OF NON-AUTOCLAVED AERATED CONCRETE MANUFACTURING PROCESS OPTIMIZATION METHOD

Bulletin of the PNRPU Applied ecology Urban development ◽

10.15593/2409-5125/2018.01.08 ◽

2018 ◽

Keyword(s):

Process Optimization ◽

Manufacturing Process ◽

Experimental Evaluation ◽

Optimization Method ◽

Autoclaved Aerated Concrete ◽

Aerated Concrete

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Research Methods and Applications of Gear Manufacturing Process Optimization

Mathematical Problems in Engineering ◽

10.1155/2019/7043604 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Qiang Li ◽

Liyang Xie ◽

Jiaxin Song ◽

Haiyang Li ◽

Guoliang Xu

Keyword(s):

Process Optimization ◽

Evaluation Method ◽

Comprehensive Evaluation ◽

Fuzzy Comprehensive Evaluation ◽

Optimization Method ◽

Analysis Method ◽

Analytic Hierarchy ◽

Comprehensive Evaluation Method ◽

Gear Manufacturing ◽

Hierarchy Process

This paper presents an optimization method for gear processes; through this method, the most worth optimized processes can be obtained and optimized, thus improving the reliability and supportability of gear products. Firstly, the POPN (Process Optimization Priority Number) analysis method considering the current process level and the process improvement cost is proposed with reference to the RPN (Risk Priority Number) analysis method to obtain the most worth optimized processes. Due to the fact that the unreasonable weight distribution of importance (I), changing difficulty (C), rationality (R), and detective difficulty (D) still exists in the POPN analysis, then combining the POPN analysis method and the AHP (Analytic Hierarchy Process)-fuzzy comprehensive evaluation method to evaluate and rank the FPOPN (fuzzy POPN) level of each process, the higher the FPOPN level is, the more the process should be optimized. Finally, according to the evaluation results, some processes with high FPOPN level are optimized and the optimal parameters’ combination of these processes is obtained through the tests based on the Taguchi method. A detailed optimization example is also given from the beginning to end in accordance with the above methods, and compared with the original process gears, the optimized gears have a big increase in gear performance through the above optimization method.

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