Year

1995

Degree Name

Doctor of Philosophy

Department

Department of Mechanical Engineering

Abstract

In recent years there has been a significant growth in the use of the automated and/or robotic welding system, carried out as a means of improving productivity and quality, reducing product costs and removing the operator from tedious and potentially hazardous environments. One of the major difficulties with the automated and/or robotic welding process is the inherent lack of mathematical models for determination of suitable welding process parameters. Developing these models is difficult because welding process parameters can affect not only weld bead geometry but also weld pool formation. Therefore, the aim of this research is to develop mathematical models to predict welding process parameters, to obtain the required weld bead geometry and to study the effects of weld process parameters on weld bead dimensions for the Gas Metal Arc Welding (GMAW) process.

In this study, the mathematical models for presenting the interrelationship between the welding process parameters and weld bead geometry were proposed. The effects of weld process parameters on weld bead profile were investigated through the empirical and theoretical mathematical models in weld pools. And finally, an infrared camera was employed to monitor the weld bead geometry. A relationship between the weld bead geometry and the surface temperature distribution was also established. An image analysis technique was employed to quantify the changes in the surface temperature distribution of the workpiece being welded. Using the infrared camera in conjunction with this image technique was shown to be one of the most sophisticated techniques to monitor perturbations occurred during welding.

Partial-penetration, single-pass, bead-on-plate welds were fabricated in 12 mm AS 1204 mild steel flats employing five different welding process parameters. The experimental results were used to develop three empirical equations: curvilinear;polynomial; and linear equations. The results were also employed to find the best mathematical equation under eleven weld bead dimensions to assist in the procedure optimisation and the process control algorithms for the GMAW process and to correlate welding process parameters with weld bead geometry of bead-on-plates deposited. With the help of a standard statistical package program, SAS, using an IBM-compatible PC, multiple regression analysis was undertaken for investigating and modelling the GMAW process, and significance test techniques were applied for the interpretation of the experimental data.

A transient two-dimensional (2D) axisymmetric model was developed for investigating the heat and fluid flow in weld pools and determining weld bead geometry, velocity profile and temperature distribution for the GMAW process. The mathematical formulation considers four driving forces for weld pool convection: electromagnetic; buoyancy; surface tension; and plasma drag forces. The formulation also deals with the molten metal droplets. The equation was solved using a general thermofluid-mechanics computer program, PHOENICS code, which is based on the SIMPLE algorithm.

The application of infrared thermography for the adaptive control of the GMAW process was studied. Welding process parameters were purposely varied and thermal response fluctuations were recorded. Infrared images were compared and contrasted with those extracted using machine vision. It was found that reasonable correlations exist between the two systems: infrared thermography and machine vision (as far as weld pool geometry interrogation is concerned). The technique developed can help to increase productivity and weld quality by minimising the amount of post process rework and inspection efforts otherwise needed.

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