The need for spatial reconfiguration has been identified by investigating user demands that were captured through a series of behavioral observations in the library space. These included the analysis of current occupancy patterns in terms of number of occupants and type of activities performed over one week. Additional unstructured observations were implemented ahead of the study by the students as they were themselves users of the library. Patterns of stagnation and movement were observed to understand how long users spend in various sections of the library and how users interact with others and various concerns were identified as for instance the reduced reconfigurability options of the furniture and lack of insight into available furniture meeting requirements for various activities and their location.

The occupancy pattern of the library showed large variability in terms of number of occupants during the year (with strong fluctuations in exam vs. vacation periods) and activities. However, even if the largest number of occupants were single users, the space was also used by groups of people for joint activities and one of the problems was the lack of furniture that could accommodate and adapt to user demands during respective activities. Reconfigurability and adaptability emerged therefore as the key objective of the presented design solution.

Reconfiguration has various challenges in terms of function, form, and requirements for lightweight. While precedents such as Media Block Chair presented interactive features, Cloud City, Office Excavate, FRGM: 1, and Voronoi Helmet³ showcased the potential of Voronoi-based design at building, component, and material scale. Hence, these approaches were adopted and enhanced using AI.

Aspects of computational design were addressed by generating a Voronoi-based design at macro (i.e., building), meso (i.e., component), and micro (i.e., material) scales. This implies that the Voronoi-based material design of components and the assembly of components into a larger structure to facilitate spatial reconfiguration is scalable.

In order to optimize production and assembly of the furniture components, functional, structural, and material design studies were implemented. To optimize functional requirements in relation to activity patterns, reconfiguration by dis−/ reassembly of furniture components was chosen. To structurally optimize the Voronoi-based furniture components, a Karamba3D simulation that provided insight into the forces and deformations of the components, both for the isolated wireframe as for the surface elements (Figure 2) was implemented. The Voronoi-based material design with various cell densities is mapped onto the frame and surface elements according to the optimization results. The variable densities pattern is efficient in terms of the material used versus the resulting degree of stiffness of the object and weight reduction.

The pattern is calculated based on the stress distribution (Figure 3) and the load cases that are used are the combination of the loads from the overall model and the forces on the component due to its own weight. The main aim of this study was to improve the mechanical performance of the components while keeping them as lightweight as possible. After all, excessive weight would considerably impact their movability.

The considered material is thermoplastic elastomer (TPE) with a density of 1.1 g/cm3. The paneling geometry is thickened to 10 mm and based on this volume distributed loads are applied to the surfaces of the panels. Loads applied are line loads based on the weight of the panels. The resulting principal stress concentrations are translated into a point cloud with a higher density around the edges. The framework is thickened and calculated separately. Since the booths are freestanding simple supporting points are used for the structural analysis that creates a point cloud based on the relative stress densities. The point cloud is then transformed into a Voronoi pattern.

An additional structural analysis is run to determine the final values for the cell sizes and wireframe thicknesses. The Voronoi edges are thickened, and the volume is calculated. The applied loads are based on the weight of TPE and the volume of the wireframe structure. The structural analysis shows the deformation of the wireframe. The final thickness of the edges and cell size parameters are determined by a resulting deflection of less than 10 mm for each cell.

The primary goal to create a user-centered spatial experience and provide effective and adaptive spatial configurations to meet current user needs in the library includes consideration for accommodating changing use (single or group use, with various levels of privacy) and user-furniture interaction through responsive lighting in order to communicate users the potential availability of furniture components (Figure 1).

The user-light interaction system was conceptually designed to provide visual comfort and information to users. The use of indoor lighting for communicating with users while providing appropriate indoor visual conditions was developed and tested in previous work (Ackerly and Brager, 2013; Meerbeek et al., 2016; Luna-Navarro et al., 2020) and has been applied to this case study. While Light-emitting Diodes (LEDs) are integrated into the furniture to provide comfortable direct lighting, various, changing colors are used to communicate if it is available. Using an active system of pressure sensors integrated into the seats, the furniture responds to various types of use. Outward lighting signals availability, whereas inward lighting illuminates the occupied components while in use.

The challenges related to the integration of micro-controllers, sensors and actuators, i.e., LEDs into the furniture components involved ensuring that the physical and virtual subcomponents communicate data to the cloud and data is processed fast enough to allow for smooth uninterrupted interaction. This has been, however, only developed conceptually in this study, with the physical prototyping focusing on the materialization of variable stiffness with cushioning based on structural and ergonomic considerations.

Component and material design was Voronoi-based and involved consideration with respect to stability, lightweight, and comfort requiring variable stiffness and 3D printing without support.⁴ The variable stiffness concept developed in previous research (Hidding et al., 2019) has been implemented in this case study in order to create cushioning for the seating areas while keeping the structural integrity of the furniture components, i.e., booths intact. The cushioning principle was prototyped by robotically milling into Expanded Polystyrene (EPS) that in this case was emulating the support surface, which has high stiffness. On top of the support surface is the cushioning surface with low stiffness and is implemented by 3D printing with Thermoplastic Polyurethane (TPU). The imprint of the Voronoi-based pattern designed to serve as cushioning surface was milled out of the EPS so the 3D printed part could be fitted onto the surface (Figure 4).

The variable stiffness design of the cushion is based on structural analysis, where the stiffness is controlled by the cell sizes implying that the smaller the cells, the denser the material and, therefore, the stiffer the structure. The design of the cells with their varying size and distribution depends on the structural analysis as well as the requirements for printing without support. In this context, the material design and prototyping approach relied on optimized material distribution based on structural optimization ensuring minimized material use and reduced production time. When compared with other production methodologies, the robotic prototyping of the Voronoi-based components has several major advantages. Firstly, it allows the production of complex geometries by linking the parametric design with the production data of the robotic equipment (D2RP). In the case of robotic 3D printing, the angles of the geometry have to be optimized to be within the angle constraints of −45 to +45 degrees for printing without support material (Hidding et al., 2019). Also, the size of the components and subcomponents are optimized to fit in the bounding box determined by the reachability of the robotic arm. The printability of the material, TPE, and its recyclability have to be considered as well.

The CV task was initially cast as a 3D puzzle reconstruction, where all the components are considered unique pieces of the constellation having their own individual base configuration. The problem could be formulated as a visual placement recognition for machine vision, given adequate training data with various configurations of missing pieces in the constellation (Figure 5). Even though an exciting CV challenge, the 3D puzzle reconstruction appeared out of the scope for this student project. The problem has then been reformulated to object recognition which is a well-known computer vision task with available model architectures. The components are labelled with ID numbers that are the keys to the correct placement in the constellation. Thus, the challenge is to predict the object’s ID number given an input image of the component taken by the user.

A synthetic training and validation dataset is created to develop a deep learning model for the object detection task. Due to the specific configuration for this problem, no pre-trained model was available for effective deployment, hence, the students trained an object detection model from scratch to meet the requirements for this project. For the training set, Voronoi-based components are randomly distributed in a 3D model of the library (Figure 6), and around 3,000 images were generated from the scattered objects from different viewpoints. 80% of the data was used for training and 20% for evaluation. The synthetic dataset is curated by simulating a realistic scenario where the images are taken from real objects at the library. A generalization gap is, however, expected due to the distribution mismatch caused by lighting, shadowing, and occlusion, which are present in a real setting. This gap is increased by the differences between the 3D model and the 3D-printed elements.

The differences between the digital files and physical objects after 3D printing could be significant enough for the AI to perform poorly on photographs of these components. Creating 3D models that have the 3D-printed layers in the models could help to create training images that more closely resemble the produced components. Labelled photographs of the produced components could also be used to re-train the model in the case of poor performance. This has been, however, not the focus of this study and will be implemented in the next step.

The Yolo object detection model (Redmon et al., 2016) is trained on the created dataset to predict the object’s ID number for the Voronoi-based components with acceptable accuracy and confidence. An online demo⁶ and screenshots from the demo (Figure 6) show randomly placed components that are detected in real time using the trained object detection model. The probability of specific components being present within the bounding box is shown in the right text box next to the box (Figure 6 right). These probabilities are used to classify an object as a true positive (correctly detected) or false positive (wrongly detected). Setting a confidence level as a hyperparameter for measuring the precision score for the object detection model. In this case, a confidence level of 0.95 from 1.00 has been achieved. There is a trade-off between precision and recall, therefore for such a high confidence level false negatives are increasing.