Impact of cyber-physical systems on enhancing gas metal arc welding operations: A critical review

Integrating cyber-physical systems (CPS) into gas metal arc welding (GMAW) has transformed the welding process and improved the quality of welded parts. GMAW is a widely used welding method due to its simplicity, high deposition rate, and ability to be automated. However, current GMAW faces several challenges, including porosity, lack of penetration, spatter, wire feed issues, heat distortion, and lack of fusion. To address this gap, CPS can be effectively introduced to overcome GMAW challenges by combining sensing, computation, and control to create a smart, adaptive welding process. By integrating CPS, GMAW technology can be further improved by enabling sensors to detect shielding gas flow rate, humidity, and contamination; artificial intelligence algorithms to predict porosity risk based on sensor data; CPS to monitor torque, motor speed, and wire tension; and real-time feedback control to adjust wire feed rate or notify maintenance if issues occur. Thermal imaging and arc sensors can monitor heat input and penetration depth, while CPS uses predictive models (such as digital twins) to simulate heat input and stress distribution. CPS can also develop self-learning welding systems that improve over time. Cloud-based CPS platforms enable supervisors to track weld quality in real-time, remotely adjust process settings, and receive predictive maintenance alerts. With this understanding, this review is structured to discuss how CPS can be effectively utilized to address GMAW challenges and enhance welded product quality. With the adoption of CPS in the GMAW process, achieving defect-free welds with better microstructural and mechanical properties is now realistically attainable. Finally, this work presents a conceptual design framework for enhancing GMAW systems, along with potential solutions and conclusions.