scholarly journals On-Line Spot Welding Quality Prediction with Analysis of Welding Parameters

2020 ◽  
Vol 3 (1) ◽  
pp. 127-140
Author(s):  
Emin Cantez ◽  
İsmail Atalay ◽  
Oğuz Alper İsen ◽  
Serkan Aydın
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Spot Welding ◽  
Welding Parameters ◽  
Quality Prediction ◽  
Welding Quality ◽  
Important Place ◽  
Machine Learning Methods ◽  
On Line

Spot welding is one of the metal joining technologies and has an important place especially in the automotive industry. A passenger car has average 5000 spots. Destructive inspection is carried out at certain periods to check these spots. However, not all parts can be checked. In this work, welding parameters were collected and analyzed. By applying different machine learning methods, the quality of the spot welding was tried to be estimated and the results were compared.

scholarly journals Numerical Simulation Mode of Spot Contact Welding Processes

2021 ◽  
pp. 85-91
Author(s):  
А.С. Угловский ◽  
И.М. Соцкая ◽  
Е.В. Шешунова
Keyword(s):  
Spot Welding ◽  
Low Carbon Steel ◽  
Welding Process ◽  
Welding Parameters ◽  
Low Carbon ◽  
Detailed Data ◽  
Welding Quality ◽  
Welding Processes ◽  
Contact Welding

Цель рассмотрения численного метода заключалась в получении подробных данных, позволяющих оценить проведение сварочного процесса: изменение объёма сварного шва, радиуса сварного шва, радиуса зоны термического влияния. При проведении моделирования авторами выведены зависимости параметров точечной сварки низкоуглеродистой стали толщиной до 3,2 мм. Данные зависимости будут определять качество сварных швов. Соответствующее сочетание параметров точечной сварки обеспечит прочное соединение и хорошее качество сварки. The purpose of the numerical method consideration was to obtain detailed data allowing evaluating the performance of the welding process: changing the volume of the weld, the radius of the weld, the radius of the weld-affected zone. During the simulation the authors have derived dependencies of the parameters of spot welding of low-carbon steel up to 3.2 mm thick. These dependencies will determine the quality of the welds. The correct combination of spot welding parameters will ensure a firm joint and good welding quality.

Multisensor fusion for on line monitoring of the quality of spot welding in automotive industry

Measurement ◽  
2008 ◽  
Vol 41 (4) ◽  
pp. 412-423 ◽  
Author(s):  
J.D. Cullen ◽  
N. Athi ◽  
M. Al-Jader ◽  
P. Johnson ◽  
A.I. Al-Shamma’a ◽  
...  
Keyword(s):  
Automotive Industry ◽  
Spot Welding ◽  
Multisensor Fusion ◽  
On Line

A Comprehensive Approach for Resistance Spot Welding Quality Estimation Using Dynamic Resistance Based Model

2012 ◽  
Vol 249-250 ◽  
pp. 732-738
Author(s):  
A. El Ouafi ◽  
R. Belanger ◽  
M. Guillot
Keyword(s):  
Quality Indicators ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Parameters ◽  
Dynamic Resistance ◽  
Weld Quality ◽  
Welding Quality ◽  
Quality Estimation ◽  
Resistance Spot ◽  
On Line

On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance gives a relative clear picture of the welding nugget formation and presents a significant correlation withseveral RSW quality indicators. This paper presents a structuredand comprehensiveapproach developed to design an effective dynamic resistancebased model for on-line quality estimation in RSW. The proposed approach examines welding parameters and conditions known to have an influence on weld quality, and builds a quality estimation model step by step. The modeling procedure begins by examining, through a structured experimental design, the relationships between welding parameters, typical characteristics of the dynamic resistance curves and multiple welding quality indicators. Using these results and various statistical tools, different integrated quality estimation models combining an assortment of dynamic resistance attributes are developed and evaluated. The results demonstrate that the proposed approach can lead to a consistentmodel able to accurately and reliably provide an appropriate estimationof the weld quality under variable welding conditions.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

TRAFFIC FLOWS FORECASTING BASED ON MACHINE LEARNING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Traffic Flows ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Packet Arrival ◽  
Entire Flow ◽  
The Mean ◽  
Active Flow

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

Hybrid Non-Dominated Sorting Genetic Algorithm

2018 ◽  
pp. 264-286 ◽  
Author(s):  
Sankhadeep Chatterjee ◽  
Sarbartha Sarkar ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Soumya Sen
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Quality Prediction ◽  
Learning Methods ◽  
Nsga Ii ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Precautionary Measures

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

Optimizing spot welding parameters in a sheet metal assembly by neural networks and genetic algorithm

2007 ◽  
Vol 221 (7) ◽  
pp. 1175-1184 ◽  
Author(s):  
M Hamedi ◽  
M Shariatpanahi ◽  
A Mansourzadeh
Keyword(s):  
Neural Networks ◽  
Spot Welding ◽  
Welding Process ◽  
Welding Current ◽  
The Body ◽  
Welding Parameters ◽  
Automotive Body ◽  
Body In White ◽  
Assembly Operations

Deformation of the spot-welded sub-assemblies in assembly operations and the gap between the matching sub-assemblies have been quality concerns specifically in the automotive industry. Overall quality of the car body and its sub-assemblies, apart from quality of each stamped part, depends markedly on the welding process. This paper considers optimization of three important process parameters in the spot welding of the body components, namely welding current, welding time, and gun force. In this research, first the effects of these parameters on deformation of the sub-assemblies are experimentally investigated. Then neural networks and multi-objective genetic algorithms are utilized to select the optimum values of welding parameters that yield the least values of dimensional deviations in the sub-assemblies. Welding sub-assemblies with the optimized set of parameters brought all of them into the tolerance range. The proposed approach can be utilized in manufacturing sub-assemblies that can fit and match better with adjacent parts in the automotive body. It enhances quality of the joint and will result in improving overall quality of the body in white.

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