The Role of Welding Robots in the Construction Industry

By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.Aug 1 2024

A recent review article published in Buildings outlines the key technical challenges faced by welding robots in the construction industry and explores potential solutions to these issues. The article also provides a comprehensive overview of the current state of welding robot technology, highlighting both the difficulties encountered and advancements in addressing these challenges.

Background

The rapidly developing construction industry requires enhanced efficiency and safety, which can be accomplished using construction robots. Such robots can effectively resolve labor supply-demand contradictions and safety issues due to their excellent automation efficiency and reliable protection mechanism.

With the increasing application of steel structures, welding robots have emerged as promising tools in the construction industry due to their efficient and high-quality operation in complex and variable situations. However, harsh, variable, and complex construction environments present critical technical challenges for weld position, path, and quality for welding robots.

Thus, this article comprehensively reviewed the intelligent techniques and existing challenges related to welding robots in the building industry. The bibliometric data were derived from the Elsevier database from the past 15 years (2010 to 2024) using the relevant keywords.

Weld Seam Tracking Technology

Weld seam tracking is essential for welding robots to maintain high weld quality. Moreover, stringent construction schedules necessitate the ability to perform welding tasks in all weather conditions. As a result, welding robots must be capable of accurately recognizing and consistently monitoring the welding seam, even in low-light or dark environments.

Huge smoke and high-temperature splashes from the welding process threaten the operator’s health and hinder the robot’s visual system. These factors may result in poor judgment of the weld properties, negatively impacting the timeliness and precision of weld seam tracking. Thus, sensing technology is used to locate the welding torch and ensure precise and stable welding.

Commonly employed sensing methods for seam tracking include arc sensing, vision sensing, and laser sensing. Arc sensing detects voltage and current variations with welding torch height to locate the weld seam center. It is convenient, sensitive, and can be operated continuously.

Vision sensing is commonly applied in all stages of robotic welding. However, traditional vision systems based on optical components face limitations of complexity, precision, and extended durations in determining weld features. Alternatively, laser sensing with structured light emitters exhibits enhanced precision and efficiency in weld seam tracking.

Trajectory Planning Technology

Complex variable structures and highly uncertain welding seam positions require the welding robot to precisely weld standard prefabricated elements and rapidly adapt to the weld seams of different sizes, shapes, and locations in varying construction spaces. Thus, trajectory planning technology is essential to improve welding robots’ motion performance and environmental adaptability.

Motion performance optimization generally focuses on global path optimization and local joint stability. This ensures efficient operation and smooth transition of the welding torch regarding displacement, velocity, and acceleration.

The environmental adaptability requirements of welding robots are increasing with the growing intricacy and variety in steel structure construction. The robots should evade obstacles and function cooperatively while working on large and complex structures. However, current levels of automation and intelligence of welding robots are inadequate.

Quality Control Technology

The weld quality directly influences the stability and safety of steel structures. Thus, real-time control of welding robots is crucial in fluctuating construction situations such as environmental factors, workpiece shapes, and weld positions. Current quality control methods focus on optimizing the process parameters before welding, tracking the weld pool during welding, and inspecting weld quality after welding.

Parameters like voltage, current, welding speed, and shielding gas flow govern the weld pool features, heat-affected zone width, and mechanical characteristics of the weld joint. Additionally, while the droplet transition state determines the shape and size of the weld pool, weld quality is defined by the robustness of the weld pool.

Thus, monitoring the weld pool’s shape, size, and temperature distribution through sensor and image processing technologies can help judge the quality and defects of weld seams.

Quality inspection post-welding, performed by appearance inspection, nondestructive testing, and mechanical testing, can further ensure weld quality. While optical tools improve visual inspection, nondestructive methods for weld defect identification include X-rays, ultrasonic, and magnetic detection. These can detect the weld seam without affecting joint performance.

Conclusion and Future Prospects

Overall, the primary technical challenges hindering welding robots are weld seam tracking under harsh construction environments, weld trajectory planning in variable constructions, and weld quality control under complex construction conditions. Moreover, the welding robots’ insufficient autonomy and intelligence limit their widespread use in construction.

Thus, the researchers suggest integrating multi-sensor technology to ensure the accurate operation of welding robots in harsh construction environments. Additionally, improving the welding robots’ autonomous control and decision-making will enable their efficient and precise application in diverse conditions.

Finally, intelligent steel structure weld quality prediction can be achieved using machine learning. Consequently, accurate prediction of the geometric characteristics of weld joints will ensure high quality and safety in welding operations.

Journal Reference

Bu, H., Cui, X., Huang, B., Peng, S., & Wan, J. (2024). Research Review and Future Directions of Key Technologies for Welding Robots in the Construction Industry. Buildings, 14(8), 2261. DOI: 10.3390/buildings14082261, https://www.mdpi.com/2075-5309/14/8/2261

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