Towards Zero-Waste Furniture Design

In traditional design, shapes are first conceived, and then fabricated. While this decoupling simplifies the design process, it can result in inefficient material usage, especially where off-cut pieces are hard to reuse. The designer, in absence of explicit feedback on material usage remains helpless to effectively adapt the design -- even though design variabilities exist. In this paper, we investigate {\em waste minimizing furniture design} wherein based on the current design, the user is presented with design variations that result in more effective usage of materials. Technically, we dynamically analyze material space layout to determine {\em which} parts to change and {\em how}, while maintaining original design intent specified in the form of design constraints. We evaluate the approach on simple and complex furniture design scenarios, and demonstrate effective material usage that is difficult, if not impossible, to achieve without computational support

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and collision avoidance, our method employs the score-based diffusion model to learn a series of gradient fields. These gradient fields encode the correlations between constraint satisfaction and the spatial relationships of polygons, learned from teacher examples. During the testing phase, packing solutions are generated using a coarse-to-fine refinement mechanism guided by the learned gradient fields. To enhance packing feasibility and optimality, we introduce two key architectural designs: multi-scale feature extraction and coarse-to-fine relation extraction. We conduct experiments on two typical industrial packing domains, considering translations only. Empirically, our approach demonstrates spatial utilization rates comparable to, or even surpassing, those achieved by the teacher algorithm responsible for training data generation. Additionally, it exhibits some level of generalization to shape variations. We are hopeful that this method could pave the way for new possibilities in solving the packing problem

Printgets: an Open-Source Toolbox for Designing Vibrotactile Widgets with Industrial-Grade Printed Actuators and Sensors

International audienceNew technologies for printing sensors and actuators combine the flexibility of interface layouts of touchscreens with localized vibrotactile feedback, but their fabrication still requires industrial-grade facilities. Until these technologies become easily replicable, interaction designers need material for ideation. We propose an open-source hardware and software toolbox providing maker-grade tools for iterative design of vibrotactile widgets with industrial-grade printed sensors and actuators. Our hardware toolbox provides a mechanical structure to clamp and stretch printed sheets, and electronic boards to drive sensors and actuators. Our software toolbox expands the design space of haptic interaction techniques by reusing the wide palette of available audio processing algorithms to generate real-time vibrotactile signals. We validate our toolbox with the implementation of three exemplar interface elements with tactile feedback: buttons, sliders, touchpads

String-Actuated Curved Folded Surfaces

Curved folded surfaces, given their ability to produce elegant freeform shapes by folding flat sheets etched with curved creases, hold a special place in computational Origami. Artists and designers have proposed a wide variety of different fold patterns to create a range of interesting surfaces. The creative process, design, as well as fabrication is usually only concerned with the static surface that emerges once folding has completed. Folding such patterns, however, is difficult as multiple creases have to be folded simultaneously to obtain a properly folded target shape. We introduce string actuated curved folded surfaces that can be shaped by pulling a network of strings, thus, vastly simplifying the process of creating such surfaces and making the folding motion an integral part of the design. Technically, we solve the problem of which surface points to string together and how to actuate them by locally expressing a desired folding path in the space of isometric shape deformations in terms of novel string actuation modes. We demonstrate the validity of our approach by computing string actuation networks for a range of well-known crease patterns and testing their effectiveness on physical prototypes. All the examples in this article can be downloaded for personal use from http://geometry.cs.ucl.ac.uk/projects/2017/string-actuated/

Generative design for agile robot based additive manufacturing for sustainable aesthetic furniture products

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe Furniture manufacturing industry has been slow to adopt the latest manufacturing technologies, relying heavily upon specialised conventional machinery. This approach not only requires high levels of specialist knowledge, training and capital investment, but also suffers from significant traditional subtractive manufacturing waste and high logistics costs due to centralised manufacturing, with high levels of furniture product not re-cycled or re-used at the end of its life cycle. This doctoral research aims to address these problems by establishing a suitable digital manufacturing technology framework concept to create step changes in the furniture design to manufacturing pathway. The design stage has the potential to contribute massively to the environmental impact of products. In this research, a Robot Base Additive Manufacturing Concept cell for future furniture manufacturing is reported. Generative design illustrates its potential contribution to waste reduction, increased manufacturing efficiency, optimised product performance and reduced environmental impact constituting a truly lean and progressive future for Furniture Manufacturing Design. Through case studies the research will show the potential for exploiting Single Minute Exchange of Die (SMED) concepts through the rule-based AI generative design post-processing of geometry for robot manufacturing, examination of different methodologies for printing and thus the resultant potential for ‘Mass Customised’ Furniture. Aesthetics, structures and the use of Smart Materials not previously economic to manufacture will be considered to demonstrate the potential to flatten the traditional Bill of Materials (BOM) and reduce logistical issues. The Furniture Industry has developed from an artisan driven craft industry, whose pioneers saw themselves reflected in their crafts and cherished the sense of pride in the originality of their designs, now largely re-configured to an anonymous collective mass output. Digital technologies and smart materials enhancement allow innovative structural fabrication, presenting a plethora of potential for networked artisan craft industries to create extraordinary aesthetics and customisable product designs. Integrating these developments with the computing power of generative design provides the tools for practitioners to create concepts which are well beyond the insight of even the most consummate traditional designers. This framework is becoming an active area of research for application in many different industries. The step changes are empowering artisans to revolutionise the design to manufacture workflow, giving momentum to the concept of conceiving a pre-industrial model of manufacturing with bespoke sustainable design at its heart. The elements of the framework will be described and illustrated using case study models highlighting the potential for creating unique aesthetics for sustainable furniture products. The research presents the methodology to create and compare iterations employing different rule sets through a commercial generative design application and how these outputs can be further customised using parametric strategies in NURBS modellers, with the ultimate goal of creating aesthetic ‘Lean’ and sustainable innovative furniture of the future, thus illustrating how the creative use of digital networks in linking individual practitioners in the making of aesthetic customised products, manufactured local to their markets, could be achieved using this framework. This research shows a robust ‘green revolution’ is evidently necessary to satisfy the needs of an ever-growing population, allowing the world to thrive within the means of this planet. New approaches to the use of technologies can achieve these changes in Furniture Manufacturing and establish a truly enhanced Circular Economy. Governments around the World are encouraging these initiatives and these approaches are identified and rationalised alongside the drivers for change which will have major impacts on this manufacturing sector. This research critically examines the Furniture Design and Manufacturing technologies presented through a TRIZ framework against the desired outcomes. Using this approach together with the physical development of a robotic test cell, combined with case study data significant contributions to knowledge in the focused area of Furniture Manufacturing are identified, detailed and enhance Furniture Design, Manufacturing and Environmental Impact for the future. The focused approach also serves to highlight areas requiring further research

Computational fabrication guided by function and material usage

This thesis introduces novel computational design paradigms for digital fabrication guided by function and material usage. With these approaches, the users can design prototypes of mechanical objects by specifying high-level functions of the objects, instead of manipulating low-level geometric details. These methods also provide the users with design suggestions which minimise material wastage during the design process. The benefit of these approaches is that the users can focus on the exploration of the design space without worrying about the realisability of the design or efficient material usage. The shallow exploration of the design space due to the lack of guidance of the users in terms of function and material usage has been one of the most critical obstacles to achieving good designs using existing design tools. We verify this hypothesis by designing and fabricating a variety of objects using our computational tools. The main contributions of the thesis are (i) clearly defined sets of constraints regarding function and material usage in the design and fabrication process, (ii) novel optimisation methods for generating designs subject to the constraints and (iii) computational tools which guide the users to design objects that satisfy the constraints