Machine Learning for Determining Key Parameters in Welding Process of Underground Engineering Equipment

2021 ◽  
Author(s):  
Hao Guo ◽  
Lin Lin ◽  
Yancheng Lv ◽  
Jie Liu ◽  
Changsheng Tong
Keyword(s):  
Machine Learning ◽  
Welding Process ◽  
Underground Engineering ◽  
Engineering Equipment

scholarly journals Adaptive Intelligent Welding Manufacturing

2021 ◽  
Vol 100 (01) ◽  
pp. 63-83
Author(s):  
YUMING ZHANG ◽  
◽  
QIYUE WANG ◽  
YUKANG LIU
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Deep Learning ◽  
Welding Process ◽  
Welding Parameters ◽  
Weld Quality ◽  
Robotic Welding ◽  
Process Variables ◽  
Physical Constraints ◽  
Needed Information

Optimal design of the welding procedure gives the desired welding results under nominal welding conditions. During manufacturing, where the actual welding manufacturing conditions often deviate from the nominal ones used in the design, applying the designed procedure will produce welding results that are different from the desired ones. Adaption is needed to make corrections and adjust some of the welding parameters from those specified in the design. This is adaptive welding. While human welders can be adaptive to make corrections and adjustments, their performance is limited by their physical constraints and skill level. To be adaptive, automated and robotic welding systems require abilities in sensing the welding process, extracting the needed information from signals from the sensors, predicting the responses of the welding process to the adjustments on welding parameters, and optimizing the adjustments. This results in the application of classical sensing, modeling of process dynamics, and control system design. In many cases, the needed information for the weld quality and process variables of our concern is not easy to extract from the sensor’s data. Studies are needed to propose the phenomena to sense and establish the scientific foundation to correlate them to the weld quality or process variables of our concern. Such studies can be labor intensive, and a more automated approach is needed. Analysis suggests that artificial intelligence and machine learning, especially deep learning, can help automate the learning such that the needed intelligence for robotic welding adaptation can be directly and automatically learned from experimental data after the physical phenomena being represented by the experimental data has been appropriately selected to make sure they are fundamentally correlated to that with which we are concerned. Some adaptation abilities may also be learned from skilled human welders. In addition, human-robot collaborative welding may incorporate adaptations from humans with the welding robots. This paper analyzes and identifies the challenges in adaptive robotic welding, reviews efforts devoted to solve these challenges, analyzes the principles and nature of the methods behind these efforts, and introduces modern approaches, including machine learning/deep learning, learning from humans, and human-robot collaboration, to solve these challenges.

Classification of Weld Defects Based on Convolution Neural Network

2021 ◽  
pp. 23-36
Author(s):  
A.I. Gavrilov ◽  
M.Tr. Do
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Weld Seam ◽  
Control Process ◽  
Welding Process ◽  
Optimal Solution ◽  
Automatic Welding ◽  
Welding Seam ◽  
Noise Factors

Automatic welding technology has been widely applied in many industrial fields. It is a complex process with many nonlinear parameters and noise factors affecting weld quality. Therefore, it is necessary to inspect and evaluate the quality of the weld seam during welding process. However, in practice there are many types of welding seam defects, causes and the method of corrections are also different. Therefore, welding seam defects need to be classified to determine the optimal solution for the control process with the best quality. Previously, the welder used his experience to classify visually, or some studies proposed visual classification with image processing algorithms and machine learning. However, it requires a lot of time and accuracy is not high. The paper proposes a convolutional neural network structure to classify images of welding seam defects from automatic welding machines on pipes. Based on comparison with the classification results of some deep machine learning networks such as VGG16, Alexnet, Resnet-50, it shows that the classification accuracy is 99.46 %. Experimental results show that the structure of convolutional neural network is proposed to classify images of weld seam defects have availability and applicability

scholarly journals Stable 3D seam tracking for thick plate GMAW manufacturing with T-joints using Kalman filter and machine learning

2021 ◽  
Author(s):  
Yinshui He ◽  
Zhuohua Yu ◽  
Ziyi Xiao ◽  
Jian Le
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Clustering Algorithm ◽  
Weld Seam ◽  
Welding Process ◽  
Fault Detection And Diagnosis ◽  
Seam Tracking ◽  
Reference Image ◽  
Tracking Accuracy ◽  
The Impact

Abstract In this paper, a robust stable three-dimensional (3D) seam tracking method is investigate based on the Kalman filter (KF) and machine learning during the multipass gas metal arc welding process with a T-joint of 60 mm thickness. The laser vision sensor is used to profile the weld seam, and with the reference image captured before arcing a scheme is proposed to extract the variable weld seam profiles (WSPs) using scale-invariant feature transform and the clustering algorithm. An effective slope mutation detection method is presented to identify the feature points of the extracted WSP, namely the candidate welding positions. In order to lower the impact of fake welding positions on seam tracking, a Bayesian Network model is first built to implement fault detection and diagnosis for the visual feature measurement process using the involved process parameters and the trigger rule. A KF, as an estimator, is then established to further stabilize the tracking process combing with a self determination algorithm of the measurement result. With the visual calibration technology, 3D seam tracking is realized. Seam tracking results show that the proposed method overcomes the tremor of the tracking position and multiple fake candidate welding positions on tracking accuracy, and the tracking accuracy is 0.6 mm. This method provides potential industrial application value for industrial manufacturing with large-scale components.

scholarly journals Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

2021 ◽  
Vol 5 (4) ◽  
pp. 135
Author(s):  
Maximilian Rohe ◽  
Benedict Niklas Stoll ◽  
Jörg Hildebrand ◽  
Jan Reimann ◽  
Jean Pierre Bergmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Gas Flow ◽  
Descriptive Analysis ◽  
Welding Process ◽  
Shielding Gas ◽  
Test Procedures ◽  
Measuring Device ◽  
Flow Rates

Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas.

scholarly journals An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

Minerals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1294
Author(s):  
Honglei Wang ◽  
Zhenlei Li ◽  
Dazhao Song ◽  
Xueqiu He ◽  
Aleksei Sobolev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
False Alarm ◽  
False Alarm Rate ◽  
Learning Model ◽  
Underground Engineering ◽  
Risk Levels ◽  
Model Based ◽  
Machine Learning Model ◽  
Sample Category

Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.

scholarly journals Predicting the Ultimate Tensile Strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using Machine learning: a comparison with Response surface methodology

2021 ◽  
Author(s):  
Germán Omar Barrionuevo ◽  
José Luis Mullo ◽  
Jorge Andrés Ramos-Grez
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Response Surface Methodology ◽  
Friction Welding ◽  
Welding Process ◽  
Gradient Boosting ◽  
Rotary Friction Welding ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

Abstract Welding metal alloys with dissimilar melting points make conventional welding processes not feasible to be used. Friction welding, on the other hand, has proven to be a promising technology. However, obtaining the welded joint's mechanical properties with characteristics similar to the base materials remains a challenge. In the development of this work, several of the machine learning (ML) regressors (e.g., Gaussian process, decision tree, random forest, gradient boosting, and multi-layer perceptron) were evaluated for the prediction of the ultimate tensile strength (UTS) in joints of AISI 1045 steel and 2017-T4 aluminum alloy produced by rotary friction welding with laser assistance. A mixed design of experiments was employed to assess the effect of the rotation speed, friction pressure, and laser power over the UTS. Furthermore, the response surface methodology (RSM) was employed to determine an empirical equation for predicting the UTS, and contours maps determine the main interactions. A total of 48 specimens were employed to train the regressors; the 5-fold cross-validation methodology was used to find the algorithm with greater precision. The gradient boosting regressor (GBR) and Gaussian processes regressors present the highest precision with a less than 3% percentage error for the laser-assisted rotary friction welding process. The capability of the GBR exceeds the accuracy of the RSM with a coefficient of determination (R2) of 90.90 versus 83.24 %, respectively.

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