In the last few years, scattered experiences of the application of additive manufacturing in the construction of buildings using 3D printing with robots or automated equipment have emerged around the world. These use a variety of procedures and suggest relevant advantages for the construction industry. In order to identify the different processes and features in development in this field and to guide future research and applications, this article presents a review of the literature on the main aspects involved in the use of 3D printing in the construction sector. The review includes state-of-the-art material mixtures, printing technologies, and potential uses, as well as a novel analysis of building strategies, management systems, and benefits stated about this new approach for construction. It reveals progressive experimentation regarding diverse features, with challenges related to the consolidation of procedures and this technology’s readiness to participate in the building market.

Innovation projects has established as an emerging field of research, a number of issues have emerged; among the others, measurability and predictability of product development. Moreover, whilst great relevance has been given to aspects such as innovation factors, product results, cognitive knowledge, poor attention has been given to other fundamental factors: product development process and effectiveness of design process as a consequence of complex problem situation. A formalization in affordances of design process may help overcome said issues and enhance product effectiveness, by identifying and highlighting potential designers’ workload perception during the Product Development Process. In this scenario, this paper describes an approach for continuously monitoring and measuring the complexity of design process during the creation of a new product. After an overview of the related work, this study proposes a common instructional design model for Product Development Process, which was split in order to be measured by using NASA-TLX. This research propose that the use of NASA-TLX to predict and measure the designer workload during the design process contributes to a better understanding of the relationships among the different design phases and designer workload. After having shown a case study, a discussion about the possible developments, as well as the limitations of the proposed method, concludes this work.

In the field of construction, human-robot collaboration and mixed reality (MR) open new possibilities. However, safety and reliability issues persist. The lack of flexibility and adaptability in current preprogrammed systems hampers real-time human-robot collaboration. A key gap in this area lies in the ability of the robot to interpret and accurately execute operations based on the real-time visual instructions and restrictions provided by the human collaborator and the working environment. This paper focuses on an MR-based human-robot collaboration method through visual feedback from a vision-based collaborative industrial robot system for use in wood stereotomy which we are developing. This method is applied to an alternating workflow in which a skilled carpenter lays out the joinery on the workpiece, and the robot cuts it. Cutting operations are instructed to the robot only through lines and conventional “carpenter’s marks”, which are drawn on the timbers by the carpenter. The robot system’s accuracy in locating and interpreting marks as cutting operations is evaluated by automatically constructing a 3D model of the cut shape from the vision system data. A digital twin of the robot allows the carpenter to previsualize all motions that are required by the robot for task validation and to know when to enter the collaborative workspace. Our experimental results offer some insights into human-robot communication requirements for collaborative robot system applications in timber frame construction.

3D-printed construction allows elaborating building elements with diverse shapes that are digitally controlled. This paper exposes the modeling of 3D-printed curved walls through parametric programming in building information modeling (BIM) in order to support a cost-efficient building design. The advantage of using curved walls is based on the possibility of reducing their thickness with respect to straight walls of similar length given their higher resistance to overturning forces. The programming developed here can propose a considerable set of solutions using curved walls for a rectangular enclosure of dimensions given by the user. A case study for a vehicle sale pavilion is shown, for which a set of 1,600 solutions with curved walls of different curvature angles and lengths is generated and subsequently analyzed. From this analysis, those models with lower material consumption and execution time are selected to be more thoroughly studied in the design process. Thus, a novel strategy is provided to researchers and practitioners for developing more efficient and expressive building designs based on 3D-printed construction. The most efficient solution identified in the example reduces material consumption by 61%, with an estimated cost saving of 53%.

This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards.