Additive Manufacturing In Construction Research Articles

Design, calibration and performance evaluation of a small-scale 3D printer for accelerating research in additive manufacturing in construction

Additive manufacturing (AM) in construction emerges as a promising and sustainable alternative to traditional construction methods. 3D printing in construction presents numerous advantages, such as geometric flexibility, cost and time savings, incorporation of recycled and sustainable materials, and reduced waste, thereby reducing the construction sector's environmental impact. Despite these advantages, widespread adoption of AM in construction faces hurdles, primarily due to the prohibitive costs of large-scale concrete printers — typically ranging from $180,000 to over $1 million — and by technological constraints that impede research and development efforts within the construction sector. Addressing these challenges, our study focuses on designing, developing, calibrating and evaluating an affordable lab-scale 3D printer specifically tailored for cement-based materials, aiming to lower the entry barrier for AM research in construction. This paper presents a proof-of-concept for a simple, yet functional printing technology tailored for cement-based materials that meet the requirements for research studies. The study details the development process, from the conceptual design to the calibration of printing parameters. The development process included the assessment of preliminary extrusion system designs integrated with the motion systems from the fused deposition modelling 3D printer. Subsequently, material studies were carried out to determine optimal cement-based mix compositions and ratios. A comprehensive calibration of printing parameters using statistical analysis has been proposed to ensure consistent and quality printing. The printability and applicability of the proposed small-scale 3D printer were assessed by printing samples and testing thermal properties. Cost analysis showed that the proposed 3D printer, with a cost of $273, offers benefits compared to existing market alternatives. The study illustrates the potential of small-scale 3D printers to facilitate construction research and practices, thereby helping to boost the development of sustainable construction methods.

Influence of printing parameters on the thermal properties of 3D-printed construction structures

3D printing in the construction sector is a promising, sustainable approach with the potential to build energy-efficient buildings. However, there is a lack of studies on the thermal properties of 3D-printed structures, which are fundamental to their energy efficiency. This study investigates the impact of printing parameters on thermal conductivity and defect development in 3D-printed concrete structures. Heat flow meter, scanning electron microscope and infrared imaging techniques were employed to assess the thermal properties and microstructure of 3D-printed samples. The results demonstrate that printing parameters significantly influence the porosity, void formation, and microstructural characteristics of 3D-printed concrete, ultimately affecting thermal properties. An increase in extrusion rate leads to higher thermal conductivity, whereas an increase in speed and standoff distance reduces thermal conductivity. Excessive spacing between printed lines can induce voids and gaps, contributing to lower thermal conductivity, while minimal spacing can promote higher porosity and larger pores. Infrared imaging revealed nonuniform thermal distribution in 3D-printed structures caused by the layered structure and voids in interlayer gaps. This study provides valuable insights for designing and constructing energy-efficient 3D-printed buildings, contributing to the development of sustainable building practices and the advancement of additive manufacturing in construction.

Artificial intelligence powered real-time quality monitoring for additive manufacturing in construction

In the manufacturing process of 3D Concrete Printing (3DCP), defects and anomalies have a significant impact on both the success rate and the quality of the final products, underscoring the need for real-time monitoring. Currently, monitoring is primarily based on manual observation and existing automated methods are limited in real-time performance and accuracy. This study introduced a real-time and highly accurate defect detection and measurement system for using deep learning (DL) and computer vision (CV) techniques. A range of improvement methods were applied in YOLOv7, showing better capacities of accuracy and speed for detecting defects in 3DCP than current cutting-edge detectors such as YOLOv8. Notably, the virtual high-fidelity data were produced by DL based data augmentation strategy and their effects were assessed. Replacing real data as the training dataset, the generated virtual data were used in the models to improve measurement accuracy. Applying the proposed method, the comprehensive insights into 3DCP defects were obtained. Consequently, the relationship formula between defect frequency and printer parameters was investigated by the proposed method, guiding operators in effectively controlling printer parameters and preventing breakpoint defects during the printing process.

Editorial: Additive manufacturing in construction

Editorial: Additive manufacturing in construction

Dynamic Winding Process of Individualized Fibre Reinforcement Structures for Additive Manufacturing in Construction

The integration of load path compliant fibre reinforcement structures into additive manufactured concrete elements opens up new potential in the field of construction. The new design language made possible by 3D concrete printing requires reinforcement structures to be provided in a highly individual shaped manner. Digital and robot-based production processes make it possible to produce on-site, on demand, fully automated and just-in-time. In this paper, a concept for an on-site ready fibre reinforcement production is presented. Based on previous works a Dynamic Winding Machine (DWM) for the on-demand production of individualizable reinforcement strands is developed. The concept and technical functionalities of the machine are presented in detail. The functionality is validated based on the production of single reinforcement bars as well as the production of entire, additively manufactured and reinforced concrete structures. With an industrial robot and adjusted end effectors, freely shaped reinforcement structures can be produced automatically. Different concepts for the use of the DWM with mobile robots are discussed. Due to the flexibility of the process, both filigree reinforcement structures, e.g. for use in particle bed printing, and large structures, e.g. for combination with Shotcrete 3D Printing, can be produced.

A novel surrogate modelling approach for additive manufacturing processes

AbstractAdditive manufacturing (AM) enables the production of complex and customised components with minimal waste. However, to achieve the desired geometry and quality, a lot of process optimisation is needed (often by experimental trial and error). Especially, in the case of AM with concrete materials, the time‐dependent behaviour of the fresh concrete is a large challenge to achieve process stability. Simulations taking into account the time‐dependent material are able to predict the printing result. Since these simulations are very time consuming, a proper orthogonal decomposition (POD)‐based reduced order model for AM is presented. Based on snapshots along the build‐up process, a surrogate model is constructed that is able to predict the printing result with respect to the underlying process and materials. Inspired by the POD approximation, a novel surrogate modelling approach using the initial waypoints of the printing trajectory as input is investigated. In this case, it is possible to analyse the manufacturing process for arbitrary geometries. The proposed method is demonstrated for the use case of additive manufacturing in construction.

The effects of nozzle diameter and length on the resulting strand properties for shotcrete 3D printing

Additive manufacturing in construction (AMC) enables new design methods and strategies within the construction industry. In particular, Shotcrete 3D Printing (SC3DP) offers a high degree of design freedom by enabling the deposition of concrete at variable layer orientation based on a wet-mix shotcrete process. However, the mechanical properties and geometry of the printed layers are dependent on the material and process parameters used. In this context, the effects of air and concrete flow rates, path planning parameters, and material parameters have been investigated in previous research. The presented study investigates the influence of the nozzle geometry on the resulting strand properties, e.g. strand geometry, layer bond strength, and compressive strength, to evaluate nozzle diameter and length as control parameters for the SC3DP process. Experimental investigations were performed with fixed nozzle diameters between 10 and 30 mm and nozzle lengths ranging from 100 to 200 mm. The results show a significant influence of the nozzle diameter on the resulting strand geometry as well as the mechanical properties. Finally, concepts for a nozzle with a controllable outlet diameter were developed and evaluated.

Explorations of efficient design solutions for Wire-and-Arc Additive manufacturing in construction

The digitalization of the construction sector could potentially produce more efficient structures, reduce material waste and increase work safety. Current strategies for the realization of automated steel constructions see the application of metal Additive Manufacturing processes (and in particular Wire-and-Arc Additive Manufacturing, WAAM) as an opportunity to build a new generation of efficient steel structures with reduced material use. This, though, requires advanced multidisciplinary knowledge in manufacturing, metallurgy, structural engineering and computational design. Recent effort has been made in order to combine computational design with current digital fabrication procedures to realize resource-efficient steel structures for the future. The present work aims at providing an integrated design approach to develop resource-efficient structural elements combining computational design algorithms with considerations on the WAAM fabrication process, structural considerations and verifications. The idea comes from the preliminary results achieved in terms of new structural optimization theories, fabrication of large-scale elements with WAAM and structural verification of first prototypes. The approach is applied to two classes of structural elements (beam and column). The results aim at increasing the application of metal Additive Manufacturing in construction, through the development a new generation of resource-efficient structural members.

Concrete Additive Manufacturing in Construction: Integration Based on Component-Related Fabrication Strategies

Additive manufacturing (AM) with concrete, also known as concrete 3D printing, is one of the most interesting approaches for disrupting the construction industry and is currently subject to numerous research activities worldwide. AM has great potential to decrease labour costs and increase the material efficiency and geometric complexity of non-standardised building components. Although prior investigations have shown various fields of application for AM with concrete, the full potential with respect to different structural component types has not been covered yet. With this paper, an up-to-date review of fabrication strategies for the main structural components, (1) walls, (2) columns, (3) slabs, and (4) beams, is provided to identify trends and existing challenges. Therefore, firstly, AM methods and their underlying principles and characteristics for concrete components are presented, and secondly, fabrication strategies for each AM method are shown. The investigation uncovers different AM strategies (direct part vs. indirect “permanent formwork”; in situ, on-site, or off-site), which are currently being used. As a result, future applications of AM will require a hybrid manufacturing strategy combining conventional and additive manufacturing to fully explore its potential.

The Application of Finite Element Simulation and 3D Printing in Structural Design within Construction Industry 4.0

Three-dimensional (3D) printing, or additive manufacturing (AM), is a production can be utilised to fabricate 3D shapes from a simulated file. This technology has gained global popularity in the construction industry since 2014 due to its wide range of applications. AM promotes a more automated, innovative, flexible, and sustainable construction method, making it an integral part of the Construction Industry 4.0. However, there need to be more detailed studies regarding the effectiveness of AM as the future direction in the construction industry. This paper investigates the application of the finite element method (FEM) in assessing 3D-printed structures to get insight into the performance of these structures. Three leading 3D-printed structures were selected, including Dubai Future Foundation in the United Arab Emirates, Apis Cor house in Russia and PERI house in Germany. Structural and thermal analyses, including linear static, natural frequency, spectral response, and steady state heat, were performed using Strand7 to assess the effectiveness of AM in construction and the reliability of FEM in analysing 3D-printed structures. Although there are limited standards and regulations for 3D-printed structures in most countries, it was concluded that 3D-printed structures presented a similar strength to traditional ones. Moreover, FEM can be used to provide a reasonable analysis of the performance of these structures, while complying with the relevant standards. This paper presents a novel numerical procedure to assess the performance of small-scale 3D-printed structures under various mechanical and thermal loadings by checking against the relevant standards.

Implementation of a surrogate model for a novel path‐based finite element simulation for additive manufacturing processes in construction

AbstractAdditive manufacturing in large‐scale construction is an ongoing research topic that shows significant potential to overcome the challenges in terms of efficient material usage and process automation in construction. A large challenge in deposition based additive manufacturing processes of concrete material is to ensure the structural stability while printing. Due to the weak material properties of the fresh concrete, it has to be ensured that during the printing process the not fully cured printed structure is able to carry its own weight. This requires process stabilization and a proper process control to prevent a collapse of the structure. Therefore, a numerical model of the printing process that takes into account the time dependent material behavior of the applied concrete as well as the printing path and the process parameters is necessary to support the process planning. Within the framework of project B04 of the collaborative research center TRR277 – Additive Manufacturing in Construction, a novel path‐based finite element simulation was developed in which the simulated geometry is constructed directly from the printing trajectory. Additionally, this approach allows the time‐dependent material properties of fresh concrete to be modeled directly and efficiently into the mesh of the printed structure. Since the computation of large scale printing processes with finite element simulations is quite expensive, there exists a need for a much faster computational model. In this contribution, the implementation of a surrogate model based on a neural network and its deployment to optimize the interlayer waiting time is presented.

Design for Manufacture and Assembly of Digital Fabrication and Additive Manufacturing in Construction: A Review

Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research and development. The DfMA’s conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concepts and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures for Dfab, DfAM, and AM assembly processes were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. As Dfab and DFAM are innovated, practical DFMA techniques begin to develop to a great extent. Large research gaps in the DfMA for Dfab and DfAM can be filled in terms of integrating them with product structural performance, management, studied cases, building information modeling (BIM), and machine learning to increase operational efficiency and sustainable practices.

A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning

PurposeMost of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.Design/methodology/approachThe research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.FindingsThe TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.Originality/valueIn related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.Practical implicationsThe results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.

Development of Cementitious Mortars for Aerial Additive Manufacturing

Additive Manufacturing (AM) methods in the construction industry typically employ ground-based deposition methods. An alternative to transform the role of AM in construction is to introduce an aerial capability. A recent project titled Aerial Additive Manufacturing (AAM), the first AM system to use untethered, unmanned aerial vehicles (or ‘drones’), has demonstrated the 3D-printing of cementitious materials during flight. An autonomous aerial system would minimise requirements for working at height, thus reducing safety risks and release AM from ground-based constraints. This study investigates viscous cementitious mortars for AAM. To assess workability and buildability, a robotic arm representing UAV movement in three-dimensional space moved a lightweight deposition device to extrude multiple layers. Constituents such as Pulverised Fuel-Ash, Silica fume, polyol resin, limeX70 and Polypropylene fibres were added to cement-based material mixes. Sand:binder ratios were a maximum of 1.00 and Water:binder ratios ranged from 0.33–0.47. Workability and buildability of mixes were evaluated using performance parameters such as power required for extrusion, number of layers successfully extruded, the extent of deformation of extruded layers and evaluation of mechanical and rheological properties. Rheology tests revealed mortars with a suitable workability-buildability balance possessed a Complex modulus of 3–6 MPa. Mechanical tests showed that resistance to deformation and buildability positively correlate and indicate compressive strengths in excess of 25 MPa. This study has demonstrated that structural cementitious material can be processed by a device light enough to be carried by a UAV to produce an unsupported, coherent multiple-layered object and further demonstrated the feasibility of untethered AAM as an alternative to ground-based AM applications in construction.

A Cost Model for Additive Manufacturing in Construction

The construction industry faces several challenges every day, which are pushing this sector to start a process of innovation. In this contest, Additive Manufacturing (AM) represents an innovation and digitalization opportunity; on this basis, this work will focus on an assessment of AM as an enabling technology for this industry, especially from a cost perspective. In particular, since there is not yet a suitable cost model for AM in construction, a cost model will be developed to assess whether it is cost effective to use 3D printing in the building industry. The results obtained from the application of the cost model on a case study are very promising as they open up new avenues for considering 3D printing as a valid alternative to traditional construction methods.

A Review on Application of 3D Printing Principles in Infrastructure Industry and its Impact on Evolution of the Industry

As 3D printing emerging to be a much-matured technology, its range of uses are now seemed to be infinite. 3D printing is now beyond the stage where it was only observed as a prototyping solution. From a simple artwork and playing toys to ready to live in buildings and also transplantable organs, the technology could potentially last until our imaginations die. From automobile to consumer goods manufacturing industries, organizations across various industries are trying to observe the advantages 3D printing has got to offer for production. With such acknowledgements, organizations are now trying to find their ways to incorporate this technology in their respective industries, whose applications could potentially extend from tooling to spare/replacement parts and sometimes till a full-fledged end-use ready product. While 3D printing looks like a most exciting new normal for organizations who are planning to streamline their prototyping technology, its prospects for the non-tech consumer world is also evolving rapidly. Additive Manufacturing in construction can be seen as an option that could contribute towards complete automation in the infrastructure industry. The method offers advantages in the aspects of design, sustainability and also efficiency. This work scopes for a comprehensive approach to advance the technology in construction and proposes research potentials, challenges, and future scope. The potential of 3dcp for automation advances all other practices in prospects like exclusion of form work, precise design execution, waste reduction and extremely low consumption of time. The real-time status and futuristic approaches to be adopted are briefed in the paper and also the scope for sustainability over other practices are detailed in the paper.

AUTOMATIC GEOMETRIC INSPECTION IN DIGITAL FABRICATION

Abstract. Additive Manufacturing in construction allows to create complex objects of different materials. Accordingly, an appropriate co-registration and comparison between the printed object and its digital model is needed for several purposes: quality control (QC) to ensure that tolerances are maintained and the realisation of a digital twin which holds the actual geometry. In this paper, we introduce an automated robotic data capturing and direct co-registration method. That is, a terrestrial laser scanner (TLS) is mounted on a robot to be moved freely in the printing room. In addition, various strategies are employed for quality control. The scanner is also mounted on the tripods, to validate the accuracy of our data capturing and co-registration solution. Furthermore, the experiment on real data is conducted for two different objects: a shotcrete printed object and a wax material object. Our experiments revealed that there is almost no influence on the scanner position accuracy compared to classical setup (mounting the TLS on tripod). The scanner position accuracy when mounted on tripods is 0.76 mm and the accuracy achieved by mounting the scanner on the robot is 1.03 mm. From the simulation it is noticed that C2M cannot detect missing extruded parts. To cope this problem, we used C2C and M3C2 algorithms. For real data scenario, without edge trimming and surface finishing it is challenging to interpret the data directly. However, M3C2 provided better results but it requires parameters tuning. In case of the surface finished object C2M and M3C2 algorithms have almost similar results.

Reinforcement Strategies for Additive Manufacturing in Construction Based on Dynamic Fibre Winding: Concepts and Initial Case Studies

Whereas entire houses are reported to be built by means of 3D Concrete Printing (3DCP), automated integration of reinforcement is still a vastly unresolved challenge and - undoubtfully - a crucial requirement for widespread adoption of 3DCP in construction practice. The disruptive technology of 3DCP opens up the chance to overcome insufficiencies of conventional steel reinforcement and its rationalised orthogonal use. This paper discloses a field of options for future construction by exploring possible reinforcement strategies for different kinds of cement-based Additive Manufacturing techniques using continuous textile fibres. A methodology is introduced to identify, compare and assess new processes for integration of fibre reinforcement based on robotic winding. Two novel approaches will be described regarding Large Particle 3D Concrete Printing and Shotcrete 3d Printing. Respective case studies will be presented in order to give a basic proof of concept.

Contour Tracking Control for Mobile Robots applicable to Large-scale Assembly and Additive Manufacturing in Construction

In the construction industry, as well as during the assembly of large-scale components, the required workspaces usually cannot be served by a stationary robot. Instead, mobile robots are used to increase the accessible space. Here, the problem arises that the accuracy of such systems is not sufficient to meet the tolerance requirements of the components to be produced. Furthermore, there is an additional difficulty in the trajectory planning process since the exact dimensions of the pre-manufactured parts are unknown. Hence, existing static planning methods cannot be exerted on every application. Recent approaches present dynamic planning algorithms based on specific component characteristics. For example, the latest methods follow the contour by a force-controlled motion or detect features with a camera. However, in several applications such as welding or additive manufacturing in construction, no contact force is generated that could be controlled. Vision-based approaches are generally restricted by varying materials and lighting conditions, often found in large-scale construction. For these reasons, we propose a more robust approach without measuring contact forces, which, for example, applies to large-scale additive manufacturing. We based our algorithm on a high-precision 2D line laser, capable of detecting different feature contours regardless of material or lightning. The laser is mounted to the robot’s end-effector and provides a depth profile of the component’s surface. From this depth data, we determine the target contour and control the manipulator to follow it. Simultaneously we vary the robot’s speed to adjust the feed rate depending on the contour’s shape, maintaining a constant material application rate. As a proof of concept, we apply the algorithm to the additive manufacturing of two-layer linear structures made from spray PU foam. When making these structures, each layer must be positioned precisely on the previous layer to obtain a straight wall and prevent elastic buckling or plastic collapse. Initial experiments show improved layer alignment within 10 % of the layer width, as well as better layer height consistency and process reliability.

Camera-based Process Monitoring for Powder Bed Additive Manufacturing in Construction

The detection of defects in the production of large-scale components is very important for the productivity of a machine, especially in fow production facilities. An effective approach is proposed to automatically detect insufficient layers in the additive manufacturing process of sand formworks for the construction industry. For this a method is introduced to integrate a camera based quality control into the powder bed binder jetting process. Results show the possibility to reduce reject parts by comparing an image of the current layer geometry to the according CAD model.