Study on full automatic arc welding machine for spherical tank

A full automatic welding machine for spherical tanks' all position multi layer welds has been developed. This machine is mainly composed of a two dimension seam tracking system based on microcomputer's memory and a welding tractor as well as rail. The main features of the machine are: while welding the first layer of a seam, its microcomputer system can analyze and store the tracing information from a two dimension sensor, and control the welding head device to realize two dimension real time tracing; while welding the second layer up to the top layer of the seam, it can realize two dimension tracing based on the memorial data, automatically determine the layer number and continually sway the welding head. The welding test shows that the machine has good tracing and welding behavior, and is suitable for spherical tank's all position multi layer welds.

Research on key control technologies of all-position automatic welding machine

The pipeline all-position automatic welding machine system is a special welding system for automatically welding circumferential joint of pipeline on site, which has been widely used to the long-distance pipeline construction projects due to the advantages of automatic control for welding parameters at all-position, moving speed of bugs and operating. In this paper, the key control technologies of PAWM all-position automatic welding machine （ developed by Pipeline Research Institute of CNPC） such us the automatic control system, control software, personal digital assistant （PDA） software and complex programmable logic device （ CPLD ） program as well us the control method of welding parameter have been described detailedly. With the higher welding quality, higher welding effwiency and lower labor intensity, PA WM all-position automatic welding machine has been successfully applied in many famous pipeline construction projects.

Digital controlling for GMA welding machine based on DSP

This paper introduced a welding machine for GMAW using digital controlling method based on DSP (Digital Signal Process). By means of flexible programming according to welding technologies and experiences the suitable characteristics of welding machine, such as line compensation, welding voltage and current feedback, wire-feed driving, SCR trigging and so on, can be controlled and self-adjusted using digital signals. Through the designing based on DSP it is put out that the traditional hardware of control circuit is decreased greatly which can enhance the stability and reliability of welding machine. Finally, the welding experiment using CO 2 shielding gas proves that the welding process is stable.

PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON SUPP0RT VECTOR MACHINE

A quality monitoring method by means of support vector machines (SVM) forrobotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the weldingprocess signal, a SVM classifier is constructed to establish the relationship between the feature ofprocess parameters and the quality of weld penetration. Under the samples obtained from auto partswelding production line, the learning machine with a radial basis function kernel shows goodperformance. And this method can be feasible to identity defect online in welding production.

Modeling of underwater wet welding seam forming based on support vector machines

Underwater welding is developing fast because of the exploration of marine resources, and underwater wet welding automation is urgently needed because of the rigorous environment. To control the welding process automatically, the model of the process should first be built to predict the current welding process status. In this paper, arc and visual sensors were used simultaneously to obtain the electrical and visual information of underwater wet welding, and support vector machines （SVM） were used to model the process, experiment results showed that the method could effectively use the information obtained and give precise prediction results.

Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

Case-based intelligent computer aided design system of welding machinery

The procedure during complex system plan design of welding position-locating machine is studied in this paper. The main difficulties in designing welding position-locating machine utilizing computer are discussed as well. A method of case-based intelligent CAD system for welding position-locating machine ( WMICAD) is put forward through wide research on design methodology. Object-oriented programming method is also applied to Ms system. The basic idea and realizing method are described.

An automatic arc welding machine for saddle joint seams on large diameter cylinders

This paper deals with the structure, components, characteristics and work principle of a newly developed automatic arc welding machine for saddle joint seams on large diameter cylinders. The equations for designing the geometry and dimensions of the cam controlling the moving locus of the welding torch have been derived. This welding machine has successfully been used in automatic welding saddle joint seams on boiler drums with good results and low cost.

Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

Research and Application of All-Position Automatic Welding Machine

The all position automatic welding machine system is the special welding system for pipeline girth automatic welding on site, which has been widely used in the long distance pipeline construction projects due to the advantages of automatic control for welding parameters at all position, moving speed of bugs and operating. This automatic pipeline welding system has been successfully used in several main pipeline projects in China, and has been approved by the constructors with the benefits of higher quality passing rate, higher welding efficiency and lower labor intensity.

Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

Support vector machines（SVM） received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.

Development of an automatic weld surface appearance inspection system using machine vision

In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy.

Automated identification of steel weld defects,a convolutional neural network improved machine learning approach

This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects,including lack of the fusion,porosity,slag inclusion,and the qualified(no defects)cases.This methodology solves the shortcomings of existing detection methods,such as expensive equipment,complicated operation and inability to detect internal defects.The study first collected percussed data from welded steel members with or without weld defects.Then,three methods,the Mel frequency cepstral coefficients,short-time Fourier transform(STFT),and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses.Classic and convolutional neural network-enhanced algorithms were used to classify,the extracted features.Furthermore,experiments were designed and performed to validate the proposed method.Results showed that STFT achieved higher accuracies(up to 96.63%on average)in the weld status classification.The convolutional neural network-enhanced support vector machine(SVM)outperformed six other algorithms with an average accuracy of 95.8%.In addition,random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.

DETECTION METHOD OF SPOT WELDING BASED ON MULTI-INFORMATION FUSION AND FRACTAL

A novel detection method of support vector machine （SVM） based on fractal dimension of signals is presented. And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding. It is shown from effect of different SVM models that these models with different inputs. In detection of defects, these models with inputs including sound signal have a high percentage of accuracy, the detection accuracy of these models with inputs including voltage signal will reduce. So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones. At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.

Girth welding system for vortex type refrigeration compressor of air conditioner

According to the requirements of welding process for vortex type compressor of air conditioner manufactured in product line, a special girth welding machine with PLC as control core was developed, which had both upright and 45 ° incline service positions. And some key technologies were researched, such as structural design of machine body, reliable conduction of rotary weldments and quality control of welding process and so on. The experimental results showed that this machine could satisfy the requirements of welding quality and girth welding technology, results also proved the machine was a high-effwiency and low-cost automatic welding device.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding

During deep penetration laser welding,there exist plume（weak plasma） and spatters,which are the results of weld material ejection due to strong laser heating.The characteristics of plume and spatters are related to welding stability and quality.Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW.An ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images.Plume area,laser beam path through the plume,swing angle,distance between laser beam focus and plume image centroid,abscissa of plume centroid and spatter numbers are defined as eigenvalues,and the weld bead width was used as a characteristic parameter that reflected welding stability.Welding status was distinguished by SVM（support vector machine） after data normalization and characteristic analysis.Also,PCA（principal components analysis） feature extraction was used to reduce the dimensions of feature space,and PSO（particle swarm optimization） was used to optimize the parameters of SVM.Finally a classification model based on SVM was established to estimate the weld bead width and welding stability.Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width,thus providing an experimental example of monitoring high-power disk laser welding quality.

Study on intelligent image welding defects in recognition for the tube pressure vessels

Currently, the welding defects recognition of X-ray nondestructive inspection is principally carried out by manual work, which highly depends on the experience of the inspectors and costs plenty of workload. In this paper, an intelligent image processing and recognition method for the tube welding radiographic testing in large-scale pressure vessels is proposed. Firstly, the raw image is preprocessed by median filtering, pseudo point removing and non-lincar image enhancement. Secondly, the welded joints parts are separated from the whole image by edge detection and threshold segmentation algorithms. Then, the separated images are handled by FFT transformation. Finally, whether defects exist and the specific type of defects are judged by Support Vector Machine. Software developed basing on this method works stably on site, and experiments demonstrate that the recognition results are compliance with the JB/T 4730. 2 or ASME standards.

Application of RVM for prediction of bead shape in underwater rotating arc welding

Bead sttape in underwater rotating arc welding was affected by several welding parameters. RVM （ relevance vector machine） was used to build a model to predict weld bead shape. The training data set of RVM eortsists of the welding parameters which are rotational frequency, rotational radius, height of torch and welding current and the features of the bead shape. The maximum error and mean error for prediction of width are 0. 10 mm and 0. 09 mm, respectively, and the maximum error and mean error for prediction of penetration are 0. 31 mm and 0. 12mm, respectively, which are showed that the prediction model can achieve higher prediction precision at reasonably small size of training data set.

Welding anomaly detection based on supervised learning and unsupervised learning

In order to solve the problem of automatic defect detection and process control in the welding and arc additive process,the paper monitors the current,voltage,audio,and other data during the welding process and extracts the minimum value,standard deviation,deviation from the voltage and current data.It extracts spectral features such as root mean square,spectral centroid,and zero-crossing rate from audio data,fuses the features extracted from multiple sensor signals,and establishes multiple machine learning supervised and unsupervised models.They are used to detect abnormalities in the welding process.The experimental results show that the established multiple machine learning models have high accuracy,among which the supervised learning model,the balanced accuracy of Ada boost is 0.957,and the unsupervised learning model Isolation Forest has a balanced accuracy of 0.909.