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Robotic Arc Welding and AM Process Parameter Optimisation

Automated Process Parameter Optimisation for Robotic Arc Welding and Additive Manufacturing TWI Core Research Project 34246/2020


Multi-axis industrial robots are widely implemented in arc welding production, providing the benefits of increased productivity and improved quality in sectors such as aerospace, rail, power generation and oil & gas structures. When developing the method of manufacturing for a new product, the majority of time is often spent on the determination and development of welding parameters. Identification of the correct parameters is mainly based on trial-and-error experiments incorporating the welding engineers’ knowledge and experience, which does not differ from the development of a manual welding process. This could undermine the technical and economic advantages of robotic processes.

This case study reports on minimising the efforts towards parameter development using a numerical modelling approach that is integrated with the robot controller and vision system.


  • Create and implement numerical models for process parameter development and its integration with robotic metal inert gas (MIG)/metal active gas (MAG) welding.
  • Develop and integrate an automated process monitoring system (laser scanning) with the robotic welding process.
  • Demonstrate the integrated concept combining the numerical modelling and process monitoring units with the arc welding robot, to contribute to the further development of intelligent automation for arc welding.


Industry often requires frequent design changes as part of product development with a corresponding need for requalification of welding procedures for each new design. In this case, the weld qualification process can become time consuming and require significant materials when robotic operation is involved. Frequently, the robotic welding trial process takes longer and costs more than manual welding, owing to the effort spent on robot data analysis, equipment re-programming and revised equipment setup.

To address some of these industrial challenges, a project concept was developed to control weld parameters in real time using a feedback loop system, combining a numerical model and weld monitoring system. Validated numerical finite element (FE) and computational fluid dynamics (CFD) models were applied to map welding parameters and weld beads using the produced weld database. An automated laser based monitoring system was developed to monitor and provide feedback to the control loop.

In this project, the developed modules were tested and validated. The concept can be adapted by other robotic welding and material processing processes. The proposed concept represented an important part of intelligent automation for welding and additive manufacturing.


Initially, trials were carried out using MAG welding to generate data in the form of temperature variation during welding and weld shape geometry as a function of different welding parameters. This data formed the input for numerical modelling. After obtaining temperature dependent material properties from the literature, two-dimensional, multi-block, structured, quadrilateral mesh was produced for CFD analysis. Wire feed rate, torch travel speed, welding current and voltage were considered variables.

After calibration of the model, weld pool dynamics were assessed for liquid and solid fractions with respect to time (Figure 1). Also, the model was compared with weld macros (Figure 2).

A laser based camera was used as a monitoring system to gather bead shape information. A representative weld bead shape recorded using the monitoring system is shown in Figure 3 while the recorded data compared with the macro is shown in Figure 4.


  • The concept of utilising numerical modelling to enable automatic parameter development and refinement for robotic arc welding process was found feasible. The finite element (FE) and computation fluid dynamics (CFD) based numerical modelling that was calibrated using the data collected from representative welding and monitoring trials showed effective prediction of the impact of welding parameters on the formation of the weld.
  • The numerical models developed by the project, which included bespoke heat transfer conditions, was found to be more representative to the real-case scenario, compared with a conventional modelling approach. The conventional approach to apply local temperature-dependent thermal boundaries, were initially applied but found ineffective. To reflect this, the model was modified by removing the bottom layer of gas underneath the workpiece and a CFD based melting model was included. The approach demonstrated effective prediction of weld bead profiles, despite the emerging challenges related to coherently match volume fraction, temperature and liquid fraction results. 
  • The WiKi scanner and i-Cube camera used as monitoring systems were found to provide reliable outcomes for weld bead shape and size. The data could easily be compared with the outputs of the numerical model. This provided confidence in the integration of the numerical modelling with monitoring systems and weld parameter optimisation.


    This project was funded by TWI’s Core Research Programme.

Figure 1. Contour plot of liquid fraction at different times during the welding process
Figure 1. Contour plot of liquid fraction at different times during the welding process
Figure 2. Comparison of modelling results with experimental macrographs
Figure 2. Comparison of modelling results with experimental macrographs
Figure 3. Recorded weld bead dimensions as seen on screen using iCube Servo robot software
Figure 3. Recorded weld bead dimensions as seen on screen using iCube Servo robot software
Figure 4. Comparison of monitoring results with experimental macrographs
Figure 4. Comparison of monitoring results with experimental macrographs
Avatar Karan Derekar Project Leader, Arc Welding Engineering

Karan joined TWI’s Arc Welding Engineering section in 2020. He manages a variety of projects in relation to robotic arc welding, and wire and arc-based, additive manufacturing (AM) (direct energy deposition/DED-arc AM), and works on a range of metals such as carbon, low alloy and stainless steels, and aerospace and marine industry-grade aluminium and high strength alloys. Before joining TWI, Karan completed a PhD from Coventry University and worked as a Welding Engineer for a pressure vessel manufacturing company. He has achieved a Masters degree in Welding Technology and a Bachelors degree in Metallurgical Engineering, and is Member of The Welding Institute.