Garments, one of the human basic needs, were customized and handmade before the Industrial Revolution. After the realization of mass production, the cost of a piece of clothing became lower, but some disadvantages arose. Garments were no longer made to measure and overproduction caused environmental problems. The new developments in digital garment design and digital customization target addressing these limitations.
The computational design of knitting attracted increased attention in recent years. In this dissertation, we consider the customized design and fabrication of 3D and 4D garments as knitwears. The 3D knitwear fits the target human body, and the 4D knitwear also considers comfort during body movement. The main research question (RQ) is: How to design customized 3D and 4D knitwear and generate instructions for a digital knitting machine?
In this dissertation, we researched computational knitwear design methods. We considered not only 3D fitting but also comfort during motion (4D). Our research can be applied in garment production (especially mass customization) or other knitting applications. Garment designers and other industrial designers can use the proposed methods to generate knitting instructions for free-form 3D surfaces. Our 4D design method helps designers place elastic or other varied knitting structures while keeping the intended 3D shape. This dissertation presents new perspectives on computational approaches to existing manufacturing techniques. It also provides enough details to further develop such design systems to be applied in practice.

Soft robots that are built from materials with mechanical properties similar to those of living tissues can achieve tasks like never before in comparison to conventional rigid robots. Powered by the compliance of soft materials and novel structure designs, complex motion (e.g., bending, twisting, and extension) can be accomplished in robotic bodies. We now see soft robots being used to grasp fragile objects and detect confined areas. However, conventional modeling and control approaches, which rely on the rigidity of the robot body, are less effective when directly applied to soft robotic systems. Therefore, new methods and algorithms need to be developed that allow modeling and kinematics control for soft robots.

In model-based robot control, kinematics comprise the fundamental knowledge that can be used to build the mathematical connection between control parameters and robot status. Unlike rigid robots, whose kinematics are well studied and have fast (analytical) solutions, effective and general kinematics computing methods for soft robot systems are still lacking. According to the modeling perspective (i.e., forward kinematics (FK)), predicting the whole-body shape of soft robots under actuation is a non-trivial task since the non-linear deformation in robot bodies and the hyperplastic properties of soft materials create challenges in balancing accuracy and computational costs in existing FK models. The lack of modeling tools further brings the difficulties in developing advanced algorithms to inverse kinematics (IK) and (statics) control thereafter. This Ph.D. project aims to develop a general soft robot kinematics computing pipeline, that can contribute to the effective control of soft robot systems to accomplish given tasks.

A fast numerical simulator for soft robots is firstly presented in this thesis, in which the shape of the robot body is discretely represented by volumetric elements. The development of this simulator was inspired by the fact that the hard-to-model actuation input (e.g., cable force, pressure, and electronic field) in soft robot systems can be directly modeled or transformed to fit the shape change in actuation elements. An optimization pipeline was built to minimize elastic energy in the body elements and compute the deformed shape with actuation parameters as input. As a general numerical simulator, it supports the modeling of various types of actuation, and the hyperelastic soft material properties are integrated. A fast collision checking and response model was added to predict the behavior of soft robots under robot-robot collisions and robot-environment interactions. The numerical computing process of our simulator shows good convergence, even for soft robots with large (rotational) deformation in their bodies, and can therefore balance the computational cost and model precision. In comparison to commercial \textit{finite element analysis} (FEA) software, this geometry-based simulator demonstrates a 20-fold faster computing speed, and the simulation result can well fit the shape that was captured from the physical setup.

The IK problem of soft robots is defined as computing proper actuation parameters that drive soft robots to accomplish given tasks. In this thesis, task-specific IK objectives (which are mainly geometrically defined) are formulated, and the optimal actuation parameters are detected using gradient-based iteration. Through the developed simulator, the gradients of objective functions are estimated using numerical differences. The sequence of motion can be successfully computed using this IK solver, and its efficiency has been verified in two case studies, which include path-following and object pick-and-place.

For the final stage of this Ph.D. project, the speed and precision of the IK solver are enhanced through machine learning. Fully connected neural networks are invited to fit functions of FK and the Jacobian of IK-related objectives. With the high efficiency in the forward propagation of networks (in analytical form), the gradient-based IK solver can run in real-time. Sim-to-real transfer learning is applied to eliminate the reality gap and make the computed actuation parameters more precise in physical setups. Applying sim-to-real transfer learning can also benefit the efficiency of the data generation process. In our pipeline, massive training data is first generated in a virtual environment using a fast simulator; thereafter, a lightweight network layer is employed to map the result of the simulation to the physical hardware. As a result, the amount of physical data can be reduced by 60% to train a network that accurately computes IK solutions.

In conclusion, this dissertation presents a pipeline that computes kinematics solutions for soft robots. A fast geometry-based simulator is presented to contribute to building an iteration-based numerical IK solver. Machine learning is applied to accelerate IK computing to real-time speed with enhanced precision. Task-specific kinematics control is realized in different soft robot systems to verify the effectiveness of the proposed method. The algorithms and code presented in this Ph.D. thesis are open-sourced for researchers and designers, and have the potential to become a general tool for designing and controlling soft robots. Future studies on the design optimization and high-level control of soft robots can all benefit from the research outcomes of this project.

This master thesis discusses the development of an arm protector for Historical European martial arts, H.E.M.A. in short. H.E.M.A. is a rapidly growing sport with currently over 40000 practitioners. The sport focuses on reconstructing and practicing the lost art of European sword fighting. The main problem of this sport however is that swords are designed to hurt people and good protection equipment is not available. The three main problems with the current protection are: (1) a lack in the freedom of movement, (2) protection and (3) fit provided by the product.

This thesis discusses these three topics in the analysis to determine the requirements to create an optimally functioning arm protector for the H.E.M.A. practitioner. During the concept development the arm protector is divided in different sections that are linked to specific requirements originating from the three research areas. Within these sections principle solutions are created, tested and selected to be combined in a preliminary design. The preliminary design is validated with experienced H.E.M.A. practitioners and further developed into the final design. The final design is a tailor made arm protector that exists out of stiff specifically shaped shells that can rotate relative to each other in order to create the necessary freedom of movement, protection and fit. This thesis is ended with recommendations discussing the further developments that are necessary to achieve a optimally functioning arm protector.

During this project, the possibilities between carbon fibres and glass were explored. It appeared that no significant improvement in mechanical properties can be obtained by reinforcing the glass. Therefore other directions were explored in the kiln, with hot glass, with glue and with electric current, both aesthetic and functional. Eventually a method, in which a weave of carbon fibres is used as a mould to blow glass in, was chosen as most promising.

This method has many values and advantages: the possibility to create non-withdrawable designs without seams (a new method with different restrictions), digitalisation in glass processing by generating sewing patterns, a fast, cheap and easy method to create easily adaptable moulds and finally a new aesthetic value by having a textile texture and in that way ‘soft’ glass. Possibilities have been explored and optimised and user evaluation showed that the contrast between textured and untextured areas was appointed as the most aesthetically pleasing aspect.

These values and results have been verified, proven and showcased in two final designs. A digitally generated sewing pattern was used to create a soft squirrel out of glass and the contrast in texture was applied in a planter design for Van Tetterode’s own product line, following the trend of bringing the outside inside.

The orthotics industry is ready to move towards a digital workflow but existing CAD software does not meet the industry’s needs.

Digital Plaster is a CAD solution for translating 3D hand scans into 3D-printed orthoses.

This project was developed at the ‘Advanced Manufacturing’ department at the faculty for Industrial Design Engineering, which focuses on improving new and promising customized design and fabrication techniques using computational solutions. The aim of this project was to create a new design for a 3d printed forearm brace. Implementing this new design could promote the radical move in fracture treatment from traditional plaster casting to 3d printed braces.

Background
Plaster casting has been the preferred treatment method for fractured bones since the 19th century, mainly because it’s cheap and effective. However practical, casting still has its flaws. Problems arise in terms of user-friendliness and hygiene (e.g. the smell, the feeling of being trapped and of course the endless itching). Today, almost two centuries later, new production methods provide the opportunity for a radical change in fracture treatment. 3D printing in combination with 3d scanning allows for the fabrication of a personalized and open structured cast, relieving the patient of some of the problems that arise with traditional casting. However, despite their potential there are still certain problems with these new braces which prevent practitioners from choosing them over traditional plaster casts.

Problem area
One of the main problem areas in both traditional casting and 3d printed braces turned out to be coping with swelling. For the current generation of 3d printed braces this swelling leads to ‘window edema’, which renders the brace useless during a large part of the healing process. These braces can only be used during the last stage of the healing process. In this stage the advantage of creating a perfect fit to the form arm which is possible through 3d scanning is lost. Additionally, there are already plenty of alternatives to casts in this stage in the form of ‘pre-manufactured braces’.

Solution
This design builds on the current generation of 3d printed braces and aims to prevent swelling from pushing through the open structure of the brace by attaching fabric on the inside of the brace. This design shows a new production technique which allows for fast and precise attachment of the fabric to the inside of the brace. More specifically, the advantage of 3d printing is to quickly create highly customized products. This design integrates the cutting and sizing of the fabric in such a way that it perfectly fits the customized brace.