A Framework for Automated Quality Control of Wood-Framed Panels in Robotic-Based Manufacturing Using Computer Vision and Deep Learning

Abstract

The advent of robotic systems has brought significant transformations across various industries, increasing the quality of products and services. However, due to construction projects’ intricacy and robotic manufacturing’s technological challenges, robotics in the construction sector is still in the nascent stages of development. The variability in construction materials presents a major challenge to the integration of robotics-based manufacturing. Lumber misalignments can cause costly reworks to the wood framing process due to lumber damage, structural deviations, and nail gun misfires. This paper seeks to address the critical quality control challenge for robotic-based manufacturing in industrialized construction. The proposed automated quality control system detects alignment issues using computer vision and deep learning technology. Detected misalignments are transmitted through a graphical user interface (GUI) to construction workers to allow them to determine whether corrective actions are required or not. The field experiments illustrated the significance of the proposed system in ensuring a proper framing process and enhancing the quality, safety, and productivity of robotic manufacturing in industrialized construction.