Data Analysis Tool and Image Acquisition System for Linear Arc Weld Deposition Evaluation The ElectricArc Welding process is affected by several physical phenomena which have a direct impact on the final product quality. To understand and improve weld deposition is a key challenge to upgrade the overall metallic welding process. However, controlling the welding process of continuously fed melting wire electrode is still an open problem, which has not yet been solved. In order to study the phenomenon involved in the metallic transfer in linear welding, we employ a highspeed camera and laser lighting equipment to record the deposition images using two distinct imaging setups. Combining the image acquisition setup with voltage, current and wire speed data, we are able to obtain a dataset of distinct welding conditions, which allow us to observe critical aspects of the deposition. We present an acquisition methodology for generating a weld image dataset, as well as a software tool that makes possible to analyze, process and visualize each frame from recorded linear welding, providing per sample statistics and a framework to draw annotations of the due video. Through visual information and inferential statistics, we are able to identify how each parameter influences the weld of thick steel plates. Furthermore, we believe the tools provided will enable researchers to evaluate the deposition process and, thus, foster the development of hardware, software, and control techniques related to the field.

A Robotic Passive Vision System for Texture Analysis in Weld Beads The use of robots is increasing in different industries, as in the case of large metal structures. The use of mobile robots meets the needs of these industries: it can be easily moved in the production line, with gains in repeatability and process efficiency, reducing rework costs. The poor configuration of the robotic and welding system generates problems in the internal structure and the surface of the weld, thus compromising the final quality of the piece. Visual inspections are common to identify problems that may have occurred during these processes. Inspections that use xrays, ultrasound, or thermal cameras require additional equipment and highly trained inspectors to analyze the results and detect problems. This work presents a computer vision system based on a passive monocular camera for analysis of weld bead textures. Images of weld beads with and without discontinuities are captured and a dimensional reduction algorithm known as Principal Component Analysis (PCA) is applied to select the main characteristics that describe each group. Afterward, the Support Vector Machine (SVM) supervised learning method is applied to recognize the patterns of image groups, making it possible to classify new images of weld beads as welds with or without discontinuities. The system makes use of the same camera coupled to the robot responsible for conducting the welding, without the need for additional sensors, and assists the welding inspector in the evaluation of the performed process.