A Fuzzy Rule-Based Method for Seeking Stable Arc Condition under Short-Circuiting Mode of GMA Welding Process

1992 ◽  
Vol 206 (2) ◽  
pp. 117-125
Author(s):  
Y J Won ◽  
H S Cho
Keyword(s):  
Gma Welding ◽  
Welding Process ◽  
Fuzzy Rule ◽  
Welding Parameters ◽  
Experimental Conditions ◽  
Rule Based ◽  
Arc Stability ◽  
Linguistic Rules ◽  
On Line ◽  
Searching Method

An unstable arc during welding induces unsound bead, thus resulting in poor mechanical properties at the welded joints. One of the important factors affecting arc stability is the welding voltage/current combination. In practice, a proper combination is usually determined by performing off-line experiments, and no extensive work has been reported on developing on-line searching methods. The paper proposes a systematic on-line method for searching the proper voltage/current combination to provide a stable arc condition in the short-circuiting metal transfer mode. Because the relationship between such welding parameters and the resulting arc stability is excessively complex and non-linear, it is difficult to determine a proper voltage/current combination based on mathematical modelling. To overcome this difficulty, a fuzzy rule-based method is proposed so that the complexity and non-linearity of arc behaviour is effectively represented by adopting fuzzy linguistic rules. The fuzzy rules here are constituted with two fuzzy variables, namely Mita's arc stability index and its derivative with respect to voltage. Then, under experimental conditions, fuzzy inferencing is performed to adjust recursively the voltage/current combination in such a way as to give a stable arc condition. The performance of the proposed searching method is also evaluated through a series of experiments. The experimental results show that the proposed searching method can effectively estimate the proper voltage/current combination to give a stable arc condition.

A Study on Development of the Optimal Neural Network in GMA Welding Process

2014 ◽  
Vol 936 ◽  
pp. 1759-1763
Author(s):  
Qian Qian Wu ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Min Ho Park ◽  
Young Su Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Process Parameters ◽  
Gma Welding ◽  
High Reliability ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Welding Quality ◽  
Optimal Parameters ◽  
On Line

Gas Metal Arc (GMA) welding is considered as a multi-parameter process that it’s hard to find optimal parameters for good welding. To overcome the problem, an artificial neural network based on the backpropagation algorithm was built to realize the relationships between process parameters and welding quality as output parameter. In this study, Mahalanobis Distance (MD) was employed to evaluate the availability of a given welding parameters which was proved to performance well in multivariate statistics. Input parameters such as welding current and arc voltage were chosen due to their significant influence on the welding quality. To improve the precision of given parameters’ evaluation, neural networks with different configurations were verified. The analyses on the measured and predicted MD by the proposed neural network were conducted. The proposed neural network based on the error backpropogation algorithm was proved to have high reliability to evaluate process parameters, which further makes it available in on-line monitoring system.

The Optimal Levenberg-Marquardt Algorithm to Predict Welding Quality Using Mahalanobis Distance Theory

2014 ◽  
Vol 936 ◽  
pp. 1873-1877
Author(s):  
Ill Soo Kim ◽  
Qian Qian Wu ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Min Ho Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mahalanobis Distance ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Welding Quality ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
On Line

With the development of computational technology, neural network has attracted the more and more attentions to reveal the relationships between the process parameters and welding geometry. However, the Gas Metal Arc (GMA) welding is complex and of multiple interactions so that mathematical model for welding parameters has not been achieved. Neural networks have been noted as being particularly advantageous for modeling systems which contain noisy, fuzzy and uncertain elements, while a sufficient algorithm is employed. In this study, Levenberg-Marquardt algorithm was employed into GMA welding process. Mahalanobis Distance (MD) was measured to determine the on-line welding quality to avoid joint failure as welding quality. To get an optimal neural network, cases with different configurations were carried out. The Root of the Mean sum of Squared (RMS) error was adopted to evaluate the accuracy of the prediction by neural networks with LM algorithm. The results presented that the proposed algorithm had the superiority of high accuracy that can be used in the on-line welding process.

Sliding Mode Algorithm for On-line Learning in Fuzzy Rule-based Neural Networks

2008 ◽  
Vol 41 (2) ◽  
pp. 12793-12798 ◽  
Author(s):  
Andon V. Topalov ◽  
Okyay Kaynak ◽  
Nikola G. Shakev ◽  
Suk K. Hong
Keyword(s):  
Neural Networks ◽  
Sliding Mode ◽  
Fuzzy Rule ◽  
Rule Based ◽  
On Line ◽  
On Line Learning

scholarly journals Credit Card Fraud Detection Using Fuzzy Rule Based Classifier

2020 ◽  
pp. 41-44
Author(s):  
Prof. Sangeetha J. ◽  
Jegatheesh B. S. ◽  
Balaji B ◽  
Hemnath N
Keyword(s):  
Credit Card ◽  
Fuzzy Rule ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Rule Based ◽  
The Everyday ◽  
On Line ◽  
Web App ◽  
Rule Based Classifier ◽  
Credit Card Usage

Fraud detection is an emerging topic of notable importance. Data mining strategies have been applied most considerably to the detection of insurance fraud, monetary fraud and financial fraud. This project will mainly focus on detecting fraudulent credit card transactions. Fraud detection in telecommunication systems, particularly the case of extraordinary imposed fraud, providing an anomaly detection technique supported by way of a signature schema, fraud deals with cases regarding criminal purposes that typically are different to identify, have additionally attracted a a tremendous deal of attention in latest years. The use of credit cards has dramatically increased because of a fast advancement inside the electronic commerce technology. Credit card will become the most popular mode of payment for each on line as properly as ordinary purchase, in instances of fraud related to it are also growing day through day. In this research sequence of operations in credit card transaction processing using a Fuzzy rule based classifier and accuracy is improved in the detection of frauds compared to other algorithms. A Naïve Bayes is initially trained with the everyday behaviour of a card holder. If an incoming credit card transaction is not accepted by the trained version with sufficiently excessive probability, it’s considered to be fraudulent. At the same time, it ensures that true transactions aren’t rejected. Supervised learning requires prior type to anomalies. In this research fuzzy rule primarily based category set of rules used for modelling real world credit card information statistics and detecting the anomaly usage of credit card information’s. Whenever anomaly credit card usage detected the system will capture the anomaly user face and freeze the anomaly user system. Django framework is used for web app creation.

Control of the weld quality using welding parameters in a robotic welding process

2018 ◽  
Vol 1 (87) ◽  
pp. 32-40 ◽  
Author(s):  
M.-H. Park ◽  
B.-J. Jin ◽  
T.-J. Yun ◽  
J.-S. Son ◽  
C.-G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Welding Quality ◽  
Arc Welding Process ◽  
Intelligent Models ◽  
Welding System

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

scholarly journals Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

2021 ◽  
pp. 3-13
Author(s):  
Masuma Mammadova ◽  
Nuru Bayramov ◽  
Zarifa Jabrayilova
Keyword(s):  
Input Data ◽  
Clinical Signs ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Second Phase ◽  
Rule Based ◽  
Rule Based System ◽  
Linguistic Rules ◽  
Clinical Conditions ◽  
Critical Situations

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decisions

Application of Microcomputers to Automatic Weld Quality Control

1980 ◽  
Vol 102 (2) ◽  
pp. 62-68 ◽  
Author(s):  
M. Tomizuka ◽  
D. Dornfeld ◽  
M. Purcell
Keyword(s):  
Control Method ◽  
Computer Control ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Seam Tracking ◽  
Process Time ◽  
Welding Parameters ◽  
Weld Quality ◽  
On Line ◽  
Welding Processes

The demand for increased productivity of welding operations has led to the expanded use of computer control to allow higher production rates while maintaining weld quality. The charateristics of the gas metal arc welding process and the relationship between welding parameters, the desired output of the welding process, and the automation of the process are discussed. A strategy for two-axis welding torch positioning and velocity control is developed based on preview control techniques. To evaluate the applicability of the proposed control method the motion of heat source along different welding paths is simulated on an analog computer with on-line control of process time constants by an LSI-11 microcomputer. The simulation results show that high quality seam tracking can be accomplished by controlling the torch motion using the proposed method. The method appears to be suitable for on-line control of welding processes.

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