Additive manufacturing (AM) is an Industry 4.0 technology that assists or replaces the conventional manufacturing (CM) of complex geometries in various sectors, including transport, steel, aerospace, military, and architecture. The aim is to improve processes, reduce energy consumption, atmospheric emissions, and solid waste, and streamline stages while complying with the new environmental regulations. The main objective of this work was to carry out a cradle-to-gate Life Cycle Assessment (LCA), considering the raw material extraction, pre-processing, manufacturing, and post-processing stages, comparing two manufacturing methods for the same ER-90 metal flange part, conventional forging and wire and arc additive manufacturing (WAAM), all following the requirements and operations proposed by the ISO 14040/44 standard. WAAM is a Directed Energy Deposition (DED) technology that uses welding techniques to produce 3D objects with more complex geometries. Compared to the forging industry, which requires a lot of heat and kinetic energy in its metal part production stages, WAAM is a more sustainable and modern alternative because it does not require high temperatures and energy to produce the same parts. The environmental indicators compared in the process stages were energy consumption, greenhouse gas (GHG) emissions, and solid waste. The total energy consumption in AM was 18,846.61 MJ, the GHG emissions were 864.49 kgCO₂-eq, and the solid waste generated was 142.34 kg, which were 63.8 %, 90.5%, and 31.6% lower than the environmental indicators calculated for CM, respectively.

This research presents a novel, low-cost optical acquisition system based on infrared imaging for real-time weld bead geometry monitoring in Gas Metal Arc Welding (GMAW). The system uniquely employs a commercial CCD camera (1000–1150 nm) with tailored filters and lenses to isolate molten pool thermal radiation while mitigating arc interference. A single camera and a mirror-based setup simultaneously capture weld bead width and reinforcement. Acquired images are processed in real time (10 ms intervals) using MATLAB R2016b algorithms for edge segmentation and geometric parameter extraction. Dimensional accuracy under different welding parameters was ensured through camera calibration modeling. Validation across 35 experimental trials (over 6000 datapoints) using laser profilometry and manual measurements showed errors below 1%. The resulting dataset successfully trained a Support Vector Machine, highlighting the system’s potential for smart manufacturing and predictive modeling. This study demonstrates the viability of high-precision, low-cost weld monitoring for enhanced real-time control and automation in welding applications.