The design and development of an intelligent adaptive extrusion system for additive manufacturing robotics in construction using advanced cementitious materials

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe global construction industry has been facing a significant challenge of low productivity and vast waste production for decades and is in desperate need of a technological transformation to overcome the inefficiencies inherent in its current processes. Robotics and automation systems have proven to be a leading solution in increasing productivity, efficiency, and product quality in manufacturing. In addition to helping manufacturers become more sustainable by reducing material and energy wastage, which are a much-needed improvement in the construction sector. Additive Manufacturing is one such ground-breaking technological development that has transformed numerous industries over the last three decades and has lately gained traction in the construction industry. As demonstrated throughout this research, the foundation of this technology allows for digitally designed 3D models to be autonomously fabricated layer upon layer using robotic systems. Today's construction 3D printing technologies are inconsistent and unreliable, necessitating the employment of skilled robotics operators and precise material preparation. To improve printability and mechanical performance, materials formulation has been a primary research emphasis to date. However, quality monitoring and control for C3DP have received little attention. Given that a construction product failure might have fatal repercussions, quality and control become an absolute prerequisite for streamlining C3DP. Consequently, this research is focused on developing an adaptive intelligent extrusion system for additive manufacturing in construction that pursues an eco-innovative approach to reduce waste and time while increasing the quality and versatility of printing a cementitious based 3D structure. Accomplishing this aim required several developments that are rooted in three different areas yet serve the same purpose - to accelerate the advancement of additive manufacturing technologies toward becoming the autonomous, sustainable digital construction manufacturing method of the future. The first area focused on the mechanical aspect of developing a medium lab-sized 3D printers to investigate the usage of AM in construction. Two main contributions are realised. The first is the creation of a robotic automation systems to digitalize construction by replacing dangerous manual labour with digital and numerically controlled robots. The developed robotic platforms were critical to understanding additive manufacturing mechanisms and developing novel construction systems and materials in a cost-effective manner. The second is the development of robust extrusion systems for advanced cementitious materials. Several prototypes are developed concurrently with the materials to achieve the highest level of compatibility and control- contributing to the broader understanding of extruding cementitious materials for C3DP. Following that the research focused on overcoming the challenge of formulating sustainable printable materials and moving away from cement-based mixtures— thus contributed to the understating of developing fly-ash based concrete for C3DP. In addition, we explored, for the first time, the use of NGPs nano-additives to improve the mechanical and printable behaviours of cementitious materials. The last area focuses on two fundamental components to construct an innovative intelligent adaptive extrusion system that satisfies the aim of the research and contributes directly to the knowledge of C3DP real-time quality monitoring and control methodologies. The first component is a novel realtime monitoring approach for a crucial printing parameter that uses a 3D vision sensor (RGB-D). By incorporating a modular 3D vision camera into an otherwise blind printing robot, the proposed system and algorithms enable the monitoring of printing defects more robustly and accurately. For the first time, the presented experimental studies using the newly developed 3D depth measuring technique demonstrate the robustness and responsiveness of using a single 3D vision system to effectively monitor the width of a multi-layer print and estimate the layer's height in real-time. The second component is a novel intelligent closed-loop controller that harnesses human expert knowledge of the printing/material process into a programmable fuzzy logic controller. By integrating the developed robust extrusion, monitoring systems, and the intelligent controller, an adaptive intelligent extrusion system capable of detecting and automatically correcting defects in construction 3D printed layers is devised. An application case study is presented that uses the built methods to produce an adaptive width control (AWC) solution. The proposed AWC excelled in maintaining the width of each layer while printing within 5% of the desired layer width. Furthermore, the preliminary results show that adopting such AWC system can improve the 3D printing process by up to 6 times when compared to open-loop systems used today in C3DP.EPSR