Solid Freeform Fabrication by GMA Welding: Geometry Modeling, Adaptation and Control

1999 ◽  
Author(s):  
Yong-Min Kwak ◽  
Charalabos Doumanidis
Keyword(s):  
Shape Control ◽  
Laser Scanning ◽  
Gma Welding ◽  
Solid Freeform Fabrication ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Full Density ◽  
Surface Geometry ◽  
Bead Width ◽  
Freeform Fabrication

Abstract This paper introduces the Gas Metal Arc Welding process with deposition shape control to solid freeform fabrication for large sculpted metal objects and rapid tooling of molds and dies. Besides full density and toughness properties, GMAW deposition must ensure near-net-shape surface geometry. To this end, an analytical model of deposited morphology is derived, based on linearized superposition of ellipsoidal unit deposition globules. The time-varying parameters of these primitives are identified in-process using laser scanning measurements of the bead width. An experimental description of width on the GMAW inputs is also established. On this basis, bead width control through the wire feed is implemented in real time, using Smith prediction to cope with sensor delays, and feedforward to compensate for the predeposited terrain. This controller was validated in the laboratory, in stainless steel deposition in single and overlaid beads.

A Study on Experimental Design Methods for the Automatic GMA Welding Process

2019 ◽  
Vol 294 ◽  
pp. 119-123
Author(s):  
Zong Liang Liang ◽  
Tae Jong Yun ◽  
Won Bin Oh ◽  
Bo Ram Lee ◽  
Ill Soo Kim
Keyword(s):  
Experimental Design ◽  
Taguchi Method ◽  
Arc Welding ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Bead Width ◽  
Arc Welding Process

Generally, the welding parameters directly affect the weld forming and the joint performance. Because many parameters are involved in the automatic arc welding process, it is not realistic to use traditional experimental methods, such as full factorial design. Therefore, it is important to find out the good experimental design method to determine the welding parameters for optimal joint quality with a minimal number of experiments. Therefore, this study is aimed at investigating the effect of DOE (Design of Experiment) methods on bead width of mild steel parts welded by the automatic GMA (Gas Metal Arc) welding process. In this work, Taguchi method was used for studying the effect of the welding parameters on optimization of bead width, while Box-Behnken method was utilized to develop a mathematical model relating the bead width to welding parameters such as welding voltage, arc current, welding speed and CTWD (Contact Tip to Work Distance). The S/N (Signal-to-Noise) ratio and the ANOVA (Analysis of Variance) were employed to find the optimal bead width. Confirmation tests were carried out to validate the effectiveness of the Taguchi method. The experimental results show that welding current mainly affected the bead width. The predicted bead width of 3.12mm was in good agreement with the confirmation tests. With the regression coefficient analysis in the Box-Behnken design, a relationship between bead width and four significant welding parameters was obtained. A second-order model has also been established between the welding parameters and the bead width as welding quality. The developed model is adequate to navigate the design space.

scholarly journals Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1494
Author(s):  
Ran Li ◽  
Manshu Dong ◽  
Hongming Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Welding Speed ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Training Dataset ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Bead Width ◽  
Artificial Neural

Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

Distributed-Parameter Modeling for Geometry Control of Manufacturing Processes With Material Deposition

1998 ◽  
Vol 122 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Charalabos Doumanidis ◽  
Eleni Skordeli
Keyword(s):  
Solid Freeform Fabrication ◽  
Surface Topology ◽  
Distributed Parameter ◽  
Surface Geometry ◽  
Mass Source ◽  
Freeform Fabrication ◽  
Geometry Model ◽  
Material Deposition ◽  
Solid Objects ◽  
3D Solid

Recent solid freeform fabrication methods generate 3D solid objects by material deposition in successive layers made of adjacent beads. Besides numerical simulation, this article introduces an analytical model of such material addition, using superposition of unit deposition distributions, composed of elementary spherical primitives consistent with the mass transfer physics. This real-time surface geometry model, with its parameters identified by in-process profile measurements, is used for Smith-prediction of the material shape in the unobservable deposition region. The model offers the basis for a distributed-parameter geometry control scheme to obtain a desired surface topology, by modulating the feed and motion of a moving mass source. The model was experimentally tested on a fused wire deposition welding station, using optical sensing by a scanning laser stripe. Its applications to other rapid prototyping methods are discussed. [S0022-0434(00)02301-7]

Solid Freeform Fabrication of Al-Li 2199 via Controlled-Short-Circuit-MIG Welding

2011 ◽  
Vol 409 ◽  
pp. 843-848
Author(s):  
David W. Heard ◽  
Julien Boselli ◽  
Raynald Gauvin ◽  
Mathieu Brochu
Keyword(s):  
Photoelectron Spectroscopy ◽  
Solid Freeform Fabrication ◽  
Short Circuit ◽  
Lithium Concentration ◽  
Gas Metal Arc Welding ◽  
Fusion Welding ◽  
Welding Parameters ◽  
Freeform Fabrication ◽  
Metal Arc Welding ◽  
Welding Processes

Aluminum-lithium (Al-Li) alloys are of interest to the aerospace and aeronautical industries as rising fuel costs and increasing environmental restrictions are promoting reductions in vehicle weight. However, Al-Li alloys suffer from several issues during fusion welding processes including solute segregation and depletion. Solid freeform fabrication (SFF) of materials is a repair or rapid prototyping process, in which the deposited feedstock is built-up via a layering process to the required geometry. Recent developments have led to the investigation of SFF processes via Gas Metal Arc Welding (GMAW) capable of producing functional metallic components. A SFF process via GMAW would be instrumental in reducing costs associated with the production and repair of Al-Li components. Furthermore the newly developed Controlled-Short-Circuit-MIG (CSC-MIG) process provides the ability to control the weld parameters with a high degree of accuracy, thus enabling the optimization of the solidification parameters required to avoid solute depletion and segregation within an Al-Li alloy. The objective of this study is to develop the welding parameters required to avoid lithium depletion and segregation. In the present study weldments were produced via CSC-MIG process, using Al-Li 2199 sheet samples as the filler material. The residual lithium concentration within the weldments was then determined via Atomic Absorption (AA) and X-ray Photoelectron Spectroscopy (XPS). The microstructure was analyzed using High Resolution Scanning Electron Microscopy (HR-SEM). Finally the mechanical properties of welded samples were determined through the application of hardness and tensile testing.

The Effect of Shielding Gas and Arc Voltage on the Bead Shape and Pore Generation of Galvanized Steel Pipe Welds with Gas Metal Arc Welding

2013 ◽  
Vol 746 ◽  
pp. 240-244
Author(s):  
Young Min Lim ◽  
Bok Su Jang ◽  
Jin Hyun Koh
Keyword(s):  
Surface Tension ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
High Surface ◽  
Welding Process ◽  
Galvanized Steel ◽  
Steel Pipe ◽  
Bead Width ◽  
Arc Voltage ◽  
Metal Arc Welding

The present study was carried out to investigate the effect of shielding gases (Ar, CO2, Ar+5%CO2, Ar+10%CO2, Ar+20%CO2, Ar+2%O2, Ar+5%O2 and Ar+10%O2) and arc voltage (16-24V) on the bead shape and porosity formation of galvanized steel pipe welds made by a gas metal arc welding process. It was confirmed that the bead height was lowered and bead width was wider with increasing voltages. Bead shapes made by Ar was narrow and convex due to a high surface tension while those made by mixture gas compositions such as Ar+CO2 and Ar+O2t became wider and smoother due to a lower surface tension. The pores were generated the least at low arc voltages of 16-20V and they were more formed over 22V. It was confirmed that Ar produced the most porosity while active and mixture gases such as CO2 and Ar+10%CO2 , Ar+5%O2 and Ar+10%O2 produced little pores by forming ZnO in the weld pool.

Validation of evaporation-determined model of arc-cathode coupling in the peak current phase in pulsed GMA welding

2021 ◽  
Author(s):  
Marek Sebastian Simon ◽  
Oleg Mokrov ◽  
Rahul Sharma ◽  
Uwe Reisgen ◽  
Guokai Zhang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Weld Pool ◽  
Cathode Surface ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Qualitative Reasoning ◽  
Current Phase ◽  
Temperature Plasma ◽  
Peak Current

Abstract A first experimental validation of the EDACC (evaporation-determined arc-cathode coupling) model is performend by comparing the experimental and simulated current in the peak current phase of a pulsed GMAW (gas metal arc welding) process. For this, the EDACC model was extended to limit the cathode surface temperature to a realistic value of <2400K. The information on the plasma for the EDACC model was gathered from literature and extrapolated and extended according to qualitative reasoning. The information on the cathode surface of the EDACC model was derived from a steady-state simulation of the weld pool, using an averaging approach over time for the energy and current. The weld pool surface temperature was compared to pyrometric measurements, that were performed for this work, and the agreement was found to be fair. The observed agreement between the modelled and experimentally determined current was within 10%. As strong assumptions were made for the comparison, the validation cannot be considered as final, but the assumptions are thoroughly analyzed and discussed. However the critical link between surface temperature, plasma temperature and total current transmitted could be reconstructed.

scholarly journals Prediction of GMA Welding Characteristic Parameter by Artificial Neural Network System

2014 ◽  
Vol 1061-1062 ◽  
pp. 481-491 ◽  
Author(s):  
Il Soo Kim ◽  
Ji Hye Lee ◽  
Javad Malekani ◽  
Prasad K.D.V. Yarlagadda
Keyword(s):  
Intelligent Systems ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Optimal Number ◽  
Ann Model ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Marquardt Algorithm ◽  
Artificial Neural

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

A Study on Fuzzy Logic Theory to Predict the Process Parameters in GMA Welding Process

2006 ◽  
Vol 505-507 ◽  
pp. 541-546
Author(s):  
Il Soo Kim ◽  
Joon Sik Son ◽  
H.H. Kim ◽  
I.J. Kim ◽  
B.Y. Kang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Gma Welding ◽  
Rapid Development ◽  
Welding Process ◽  
Bead Geometry ◽  
Bead Width ◽  
Fuzzy Logic Theory ◽  
Welding System

Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been achieved duo to difficulties of the control and sensor technologies. In this paper, the classification of the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with fuzzy logic is presented. The Fuzzy C-Means (FCM) algorithm, which is best known an unsupervised fuzzy clustering algorithm is employed here to analysis the specimen of the bead geometry. Then the quality of the GMA welding can be classified by this fuzzy clustering technique, and the optimal bead geometry can also be achieved.

Dynamic Modeling and Adaptive Control of the Gas Metal Arc Welding Process

1994 ◽  
Vol 116 (3) ◽  
pp. 405-413 ◽  
Author(s):  
Jae-Bok Song ◽  
David E. Hardt
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Adaptive Control System ◽  
Bead Width ◽  
Metal Arc Welding ◽  
Arc Welding Process

Control of the welding process is a very important step in welding automation. Since the welding process is complex and highly nonlinear, it is very difficult to accurately model the process for real-time control. In this research, a discrete-time transfer function matrix model for gas metal arc welding process is proposed. This empirical model takes the common dynamics for each output and inherent process and measurement delays into account. Although this linearized model is valid only around the operating point of interest, the adaptation mechanism employed in the control system render this model useful over a wide operating range. Since welding is inherently a nonlinear and multi-input, multi-output process, a multivariable adaptive control system is used for high performance. The process outputs considered are weld bead width and depth, and the process inputs are chosen as the travel speed of the torch and the heat input. A one-step-ahead (or deadbeat) adaptive control algorithm combined with a recursive least-squares methods for on-line parameter estimation is implemented in order to achieve the desired weld bead geometries. Control weighting factors are used to maintain the stability and reduce excessive control effort. Some guidelines for the control design are also suggested. Command following and disturbance rejection properties of the adaptive control system for both SISO and MIMO cases are investigated by simulation and experiment. Although a truly independent control of the outputs is difficult to implement because of a strong output coupling inherent in the process, a control system for simultaneous control of bead width and depth was successfully implemented.

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