Maksudul Alam and Tsz Ho Kwok
Structural and Multidisciplinary Optimization, accepted.
[PDF][doi:10.1007/s00158-024-03845-4][SharedIt]

Abstract:
Creating functional midsoles for shoes is a challenging task that involves considering different aspects such as stability, comfort, manufacturability, and aesthetics. No single approach exists to design a midsole that meets all these objectives effectively. Therefore, this study aims to introduce a multidisciplinary optimization method to develop custom shoe midsole structures. Our approach involves utilizing generative methods to generate diverse structures and leveraging swarm intelligence to search for optimal designs. Without loss of generality, we use tetrahedral mesh generation to create midsole structures because tetrahedral structures are renowned for their exceptional strength. To enhance the swarm's exploration of the design space and discover more local optima, we developed a swarm behavior that promotes diversity. Furthermore, we created a quantitative measurement tool to evaluate various objectives. In order to test the effectiveness of our generative approach, we analyzed the midsoles generated from our design exploration that performed the best and the worst in relation to each objective. Our findings revealed a substantial difference between them, with scores differing by two to four times. Additionally, when compared to other lattice structures, the tetrahedral midsole structure created by our method demonstrated superior compliance with the foot and better redistribution of plantar stress. The multidisciplinary optimization technique we have proposed is a valuable resource for engineers and designers in the footwear industry, allowing them to develop high-performance midsole structures that meet the needs of both consumers and athletes. Furthermore, this method can be applied to optimize other complex structures in various industries, such as civil, automotive, and aerospace engineering.

Shiva Shokri, Pooria Sedigh, Mehdi Hojjati, Tsz Ho Kwok
Transactions of the Canadian Society for Mechanical Engineering, accepted. Open Access.
[doi:10.1139/tcsme-2024-0035]

Abstract:
This study introduces a novel approach to enhance the surface properties of fiber-reinforced polymer composites through thermal spray coatings, utilizing a metal mesh as an anchor to improve coating adhesion. A critical step in this process is achieving optimal exposure of the metal mesh by sandblasting prior to coating. To address this challenge, we propose a closed-loop control system designed to inspect and blast parts effectively. Our method leverages top-view microscope images as inputs, employing a convolutional neural network (CNN) to correlate these images with the corresponding exposure levels of the metal mesh, measured via a destructive method. Upon training, the CNN model accurately estimates the exposure level solely from the top-view images, facilitating real-time feedback to guide subsequent sandblasting operations. Unlike traditional manual inspection methods, which demand expertise and experience, our automated approach streamlines the inspection process using a cost-effective portable digital microscope. Experimental findings validate the efficacy of our method in successfully discerning surface preparation status with an accuracy rate of 95% and demonstrate its practical utility in closed-loop control. Our study not only offers a robust methodology for quantifying surface preparation data but also presents a significant advancement in automating the inspection process. Moreover, the broader implications of our approach extend to various manufacturing sectors, where defect detection and closed-loop control are crucial for optimizing production efficiency and product quality.

Tsz Ho Kwok and Tom Gaasenbeek
Engineering Research Express, 5(4):045019, 2023. Open Access.
[doi:10.1088/2631-8695/acfeca]

Abstract:
Due to the recent pandemic, our factory operations have experienced significant setbacks, prompting the need for factory automation to maintain productivity. However, most of our factories rely heavily on human input and oversight and cannot operate remotely. Automating our factories has revealed technological gaps that fall short of our expectations, needs, and vision. Therefore, the purpose of this paper is to bridge this gap by introducing practical methodologies and applied technology that can enhance legacy factories and their equipment. Our proposed solution is the ORiON Production Interface (OPI) unit, which can function as a smart networked edge device for virtually any machine, allowing the factory to operate efficiently. We have incorporated various computer vision algorithms into the OPI unit, enabling it to autonomously detect errors, make decentralized decisions, and control quality. Despite the concept of Industry 4.0 (I4.0) being known, many machines in use today are closed source and unable to communicate or join a network. Our research offers a viable solution to implement Industry 4.0 in existing factories, and experimental results have demonstrated various applications such as process monitoring, part positioning, and broken tool detection. Our intelligent networked system is novel and enables factories to be more innovative and responsive, ultimately leading to enhanced productivity. All manufacturing companies interested in adopting Industry 4.0 technology can benefit from it, and the OPI, being an IoT device, is also an appealing option for developers and hobbyists alike.

Mohsen Rasoulivalajoozi, Mojtaba Rasouli, Carmela Cucuzzella, Tsz Ho Kwok
International Journal of Industrial Ergonomics, Volume 98, November 2023, 103504.
[PDF][doi:10.1016/j.ergon.2023.103504]

Abstract:
Work-related musculoskeletal disorders (WRMSDs) lead to fatigue and decreased productivity in workers, resulting in the need for many affected individuals to seek medical treatment annually. Beekeepers, like other agricultural workers, are susceptible to WRMSDs due to the continuous demands of their work and the repetitive movements involved. Thus, the objective of this study is to determine the prevalence of WRMSDs and assess the level of risk associated with different postures among beekeepers to improve their musculoskeletal health. To achieve this, a cross-sectional study was conducted involving 33 beekeepers, consisting of two stages. Firstly, the Nordic Questionnaire was utilized to assess the prevalence of WRMSDs. Subsequently, the Ovako Working Posture Analysis System (OWAS) was employed to analyze and categorize the riskiest postures into four levels of corrective measures. The findings indicate that the most commonly affected areas were the back (51.5%) and waist (45.4%). The occurrence of WRMSDs in various body regions was significantly associated with the beekeepers' years of experience and weekly working hours. Additionally, the prevalence of neck and back pain was significantly related to their body mass index (BMI). The OWAS postural analysis revealed that the back (36.75%) and arm (21.08%) regions required corrective measures as soon as possible (level III), while the back (26.47%) and legs (14.70%) fell under the category of corrective measures needed in the near future (level II). Combining the postural analysis results, 28.43% were classified as Action Levels (AL) II, 37.73% as level III, and 0.98\% as level IV. This study demonstrates that WRMSDs are relatively common among beekeepers, primarily due to their extensive work experience and the adoption of awkward postures during their tasks. As a result, recommendations regarding ergonomics and physiotherapy are provided to alleviate pain and reduce the strain on critical postures.

Baanu Payandehjoo, Tsz Ho Kwok
Journal of Manufacturing Processes, Volume 100, August 2023, Pages 1-10.
[PDF][doi:10.1016/j.jmapro.2023.05.017]

Abstract:
Flexible sensor applications have increasingly focused on ionically conductive hydrogels due to their notable deformability and easily tunable properties compared to rigid materials. These hydrogels possess electrical properties, thanks to their high water content and porous structure that facilitate effective ion transfer. Despite their attractive features, hydrogels have limitations in terms of water retention and shape fidelity, and they are more typically inspected as two dimensional films and patches. In this paper, 3D printed thermoplastic polyurethane (TPU) elastomer frames with various geometries were injected with ionic conductive polyacrylamide (PAAm) based hydrogels to create durable, robust soft mechanical sensors for detecting strain, pressure, and bending through changes in their electrical resistance. After the effectiveness of the TPU encasement in maintaining the hydrogel water content was demonstrated, hydrogel embedded frames with varying geometries were designed. Their response to mechanical loading was investigated in relation to their dimensions and geometric shape. Finally, glove-shaped frames were fabricated to fit human fingers and injected with ionic hydrogel for sensing abilities. The wearable sensors accommodated free movement of the fingers in multiple directions and were able to detect simultaneous and independent bending and stretching of the fingers. Through comprehensive observation of the electrical behavior of all soft ionic sensors in response to different kinds of mechanical stimuli, it was concluded that the resistance change following mechanical loading was dependent on the specific geometric features of each individual hybrid sensor. Thus, ionic hydrogel-embedded TPU encasement could be designed with targeted geometry to dictate the type and direction of mechanical sensing with regard to its application. This work presents a facile approach to fabricating multi-component soft geometric sensors with potential to be used for wearable electronics and human-machine interactions.

Tsz Ho Kwok
Structural and Multidisciplinary Optimization, 65, 202 (2022).
[PDF][doi:10.1007/s00158-022-03295-w][GitHub]

Abstract:
Although additive manufacturing can produce nearly any geometry, users have limited choices in the designs. Topology optimization can create complex shapes, but it provides only one solution for one problem, and existing design exploration methods are ineffective when the design space is huge and high-dimensional. Therefore, this paper develops a new generative design method to improve the diversity of topology-optimized designs. Based on the observation that topology optimization places materials along the principal directions to maximize stiffness, this paper creates a rule of principal direction and applies it to swarm intelligence for form-finding. The shapes got by the swarming process possess both randomness and optimality. After they are further optimized, the final designs have high diversity. This is the first time integrating structural stiffness as a swarm principle to influence the collective behavior of decentralized, self-organized systems. The experimental results show that this method can generate interesting designs that have not been seen in the literature. Some results are even better than those got by the original topology optimization method, especially when the problem is more complex. This work not only allows users to choose unique designs according to their preference, but also helps users find better designs for their application.

Irfan Mustafa, Tsz-Ho Kwok
Micromachines, 13(5):773, 2022. Open Access.
[doi:10.3390/mi13050773]

Abstract:
One major concern of multi-material additive manufacturing (MMAM) is the strength at the interface between materials. Based on the observation of how nature puts materials together, this paper hypothesizes that overlapping and interlacing materials with each other enhance the interface bonding strength. To test this hypothesis, this research develops a new slicing framework that can efficiently identify the multi-material regions and develop interlaced infills. Based on a ray-tracing technology, we develop layered depth material images (LDMI) to process the material information of digital models for toolpath planning. Each sample point in the LDMI has an associated material and geometric properties that are used to recover the material distribution in each slice. With this material distribution, this work generates an interlocking joint and an interlacing infill in the regions with multiple materials. The experiments include comparisons between similar materials and different materials. Tensile tests have shown that our proposed infill outperforms the interlocking joint in all cases. Fractures occur even outside the interlacing area, meaning that the joint is at least as strong as the materials. The experimental results verify the enhancement of interface strength by overlapping and interlacing materials. In addition, existing computational tools have limitations in full use of material information. To the best of our knowledge, this is the first time a slicer can process overlapped material regions and create interlacing infills. The interlacing infills improve the bonding strength, making the interface no longer the weakest area. This enables MMAM to fabricate truly functional parts. Also, the new LDMI framework has rich information on geometry and material, and it allows future research in multi-material modeling.

Christopher Richard, Tsz-Ho Kwok
Materials, 14(17):4867, 2021. Open Access.
[doi:10.3390/ma14174867][Data - CAD models]

Abstract:
This paper aims at designing lattice structures for rapid investment casting (RIC), and the goal of the design methodology is to minimize casting defects that are related to the lattice topology. RIC can take full advantage of the unprecedented design freedom provided by AM. Since design for RIC has multiple objectives, we limit our study to lattice structures that already have good printability, i.e., self-supported and open-celled, and improve their castability. To find the relationship between topological features and casting performance, various lattice topologies underwent mold flow simulation, finite element analysis, casting experiments, and grain structure analysis. From the results, the features established to affect casting performance in descending order of importance are relative strut size, joint number, joint valence, and strut angle distribution. The features deemed to have the most significant effect on tensile and shear mechanical performance are strut angle distribution, joint number, and joint valence. The practical application of these findings is the ability to optimize the lattice topology with the end goal of manufacturing complex lattice structures using RIC. These lattice structures can be used to create lightweight components with optimized functionality for various applications like aerospace and medical.

Ankhy Sultana, Tsz-Ho Kwok,  Hoi Dick Ng
Mathematical Biosciences and Engineering, 18(4), 4429-4449, 2021. Open Access.
[doi:10.3934/mbe.2021224]

Abstract:
Energy can be represented in the form of deformation obtained by the applied force. Energy transfer is defined in physics as the energy is moved from one place to another. To make the energy transfer functional, energy should be moved into the right direction. If it is possible to make a better use of the energy in the right direction, the energy efficiency of the structure can be enhanced. This idea leads to the concept of directional energy transfer (DET), which refers to transferring energy from one direction to a specific direction. With the recent development of additive manufacturing and topology optimization, complex structures can be applied to various applications to enhance performances, like a wheel and shoe midsole. While many works are related to structural strength, there is limited research in optimization for energy performance. In this study, a theoretical approach is proposed to measure the directional energy performance of a structure, which can be used to measure the net energy in an intended direction. The purpose is to understand the energy behavior of a structure and to measure if a structure is able to increase energy in the desired direction.

Jida Huang, Tsz-Ho Kwok
ASME Journal of Computing and Information Science in Engineering, 21(6): 061007, Dec 2021.
[PDF][doi:10.1115/1.4050758][JCISE Spotlight]

Abstract:
Wireframes have been proved useful as an intermediate layer of the neural network to learn the relationship between the human body and semantic parameters. However, the definition of the wireframe needs to have anthropological meaning and is highly dependent on experts' experience. Hence, it is usually not easy to obtain a well-defined wireframe for a new set of shapes in available databases. An automated wireframe generation method would help relieve the need for the manual anthropometric definition to overcome such difficulty. One way to find such an automated wireframe generation method is to apply segmentation to divide the models into small mesh patches. Nevertheless, different segmentation approaches could have various segmented patches, thus resulting in various wireframes. How do these different sets of wireframes affect learning performance? In this paper, we attempt to answer this research question by defining several critical quantitative estimators to evaluate different wireframes' learning performance. To find how such estimators influence wireframe-assisted learning accuracy, we conduct experiments by comparing different segmentation methods on human body shapes. We summarized several meaningful design guidelines for developing an automatic wireframe-aware segmentation method for human body learning with such verification.

Eder Sales, Tsz-Ho Kwok, Yong Chen
Journal of Manufacturing Processes, Volume 64, April 2021, Pages 1420-1433.
[PDF][doi:10.1016/j.jmapro.2021.02.050]

Abstract:
Additive manufacturing (AM) has been widely used in many different areas due to its unique advantages, such as the possibility of creating complex shapes, no specific tools required, relatively fast, and less material waste with light-weight designs. The design freedom enabled by AM also allows a component to be highly optimized on its topology and shape according to its function. Currently, there are advanced algorithms that enable designers to perform topology optimization (TO) in the computer-aided design (CAD) phase. However, the optimization results are not considered during the downstream AM process planning like toolpath generation, and the optimized structure may lose its designed performance. Instead of only considering TO in the CAD phase, this work presents a breakthrough in adopting the TO principles in the toolpath planning process and considering the toolpath characteristics presented in the AM processes. Since toolpaths are lines, this paper applies a line-based TO method that uses the principal stress line (PSL) as the guidance to the generation of toolpaths to improve structural rigidity. The PSL-based method is efficient, controllable, and able to consider the characteristics of the AM process. The computation results can be directly converted into toolpaths that can be faithfully fabricated and achieve the component function specified in the design phase. Structural tests were performed on the developed method. The experimented results demonstrate that the strategy of applying the PSL-based toolpath planning is a promising direction to incorporate topology optimization from the CAD phase to computer-aided manufacturing (CAM). To the best of our knowledge, this study is the first to explore the use of PSL in the AM’s toolpath planning.

Christopher-Denny Matte, Tsz-Ho Kwok
ASME Journal of Computing and Information Science in Engineering, 21(5): 050903, Oct 2021.
[PDF][doi:10.1115/1.4050045]

Abstract:
The simulation of complex geometries and non-linear deformation has been a challenge for standard simulation methods. There has traditionally been a trade-off between performance and accuracy. With the popularity of additive manufacturing and the new design space it enables, the challenges are even more prevalent. Additionally, multiple additive manufacturing techniques now allow hyperelastic materials as raw material for fabrication and multi-material capabilities. This allows designers more freedom but also introduces new challenges for control and simulation of the printed parts. In this paper, a novel approach to implementing non-linear material capabilities is devised with negligible additional computations for geometry-based methods. Material curves are fitted with a polynomial expression, which can determine the tangent modulus, or stiffness, of a material based on strain energy. The moduli of all elements are compared to determine relative shape factors used to establish an element's blended shape. This process is done dynamically to update a material's stiffness in real-time, for any number of materials, regardless of linear or non-linear material curves.

Xiaodong He, Christopher-Denny Matte, Tsz-Ho Kwok
Springer Nature - Applied Sciences, 3, 133, 2021. Open Access.
[doi:10.1007/s42452-020-04018-w]

Abstract:
The paper presents a novel manufacturing approach to fabricate origami based on 3D printing utilizing digital light processing. Specifically, we propose to leave part of the model uncured during the printing step, and then cure it in the post-processing step to set the shape in a folded configuration. While the cured regions in the first step try to regain their unfolded shape, the regions cured in the second step attempt to keep their folded shape. As a result, the final shape is obtained when both regions’ stresses reach equilibrium. Finite element analysis is performed in ANSYS to obtain the stress distribution on common hinge designs, demonstrating that the square-hinge has a lower maximum principal stress than elliptical and triangle hinges. Based on the square-hinge and rectangular cavity, two variables - the hinge width and the cavity height - are selected as principal variables to construct an empirical model with the final folding angle. In the end, experimental verification shows that the developed method is valid and reliable to realize the proposed deformation and 3D development of 2D hinges.

Jida Huang, Hongyue Sun, Tsz-Ho Kwok, Chi Zhou, Wenyao Xu
ASME Journal of Manufacturing Science and Engineering, 142(6), 061003 (Apr 13, 2020).
[PDF][doi:10.1115/1.4046746]

Abstract:
Many industries, such as human-centric product manufacturing, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes flexible design and manufacturing possible. However, the personalized designs bring challenges for the shape matching and analysis, owing to the high complexity and shape variations. Traditional shape matching methods are limited to spatial alignment and finding a transformation matrix for two shapes, which cannot determine a vertex-to-vertex or feature-to-feature correlation on the two shapes. Hence, such a method cannot measure the deformation of the shape and interested features directly. To measure the deformations widely seen in the mass customization paradigm and address the issues of alignment methods in shape matching, we identify the geometry matching of deformed shapes as a correspondence problem. The problem is challenging due to the huge solution space and nonlinear complexity, which is difficult for conventional optimization methods to solve. According to the observation that the well-established massive databases provide the correspondence results of the treated teeth models, a learning-based method is proposed for the shape correspondence problem. Specifically, a state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected deformed shapes. Through learning the deformations of the models, the underlying variations of the shapes are extracted and used for finding the vertex-to-vertex mapping among these shapes. We demonstrate the application of the proposed approach in the orthodontics industry, and the experimental results show that the proposed method can predict correspondence fast and accurate, also robust to extreme cases. Furthermore, the proposed method is favorably suitable for deformed shape analysis in mass customization enabled by 3D printing.

Guoxin Fang, Christopher-Denny Matte, Rob B. N. Scharff, Tsz-Ho Kwok, Charlie C. L. Wang
IEEE Transactions on Robotics, Volume: 36 , Issue: 4 , Aug. 2020.
[PDF][doi:10.1109/TRO.2020.2985583][video][GitHub]

Abstract:
Robots fabricated with soft materials can provide higher flexibility and thus better safety while interacting in unpredictable situations. However, the usage of soft material makes it challenging to predict the deformation of a continuum body under actuation and therefore brings difficulty to the kinematic control of its movement. In this paper, we present a geometry-based framework for computing the deformation of soft robots within the range of linear material elasticity. After formulating both manipulators and actuators with geometry elements, deformation can be efficiently computed by solving a constrained optimization problem. Based on its efficiency, forward and inverse kinematics for soft manipulators can be effectively solved by an iterative algorithm. Meanwhile, components with multiple materials can also be geometrically modeled in our framework with the help of a simple calibration. Numerical and physical experimental tests are conducted on soft manipulators driven by different actuators with large deformation to demonstrate the performance of our approach.

Yuen-Shan Leung, Tsz-Ho Kwok, Huachao Mao, Yong Chen
Computer-Aided Design, Volume 114, September 2019, Pages 224-235.
[PDF][doi:10.1016/j.cad.2019.05.031]

Abstract:
Recent multi-material additive manufacturing (AM) technologies enable the fabrication of an object with accurate deposition of different types of materials. Hence, in addition to geometric shapes, it is possible to use different material compositions to optimize the mechanical properties of a component for given design requirements. However, current AM processes have a limitation on the number of materials that can be deposited during the fabrication process. Due to the constraint, it is critical to optimize the material distribution using the limited base materials; however, an extremely large design space exists in a design domain that is enabled by the AM technologies. In this paper, we introduce a digital material design framework to generate digital material compositions that can be printed and be able to achieve the desired behavior. We take analog material composition as the input and perform the analog-to-digital conversion using an exemplar-based approach based on a pre-computed material library. The patterns in the library are constructed with different combinations of the given base materials, and their mechanical properties are computed using finite element simulation. Accordingly, the design goal of the analog-to-digital conversion is to find material composition in the design domain with matching mechanical properties. A tensor-based error diffusion algorithm has been developed to reduce the approximation error during the conversion effectively. Experimental tests based on the design framework have been performed. The test results demonstrate our framework can quickly find effective solutions for various multi-material design problems.

Tsz-Ho Kwok
ASME Transactions - Journal of Computing and Information Science in Engineering, 19(4), 044502 (Jun 13, 2019).
[PDF][doi:10.1115/1.4043672]

Abstract:
In Additive Manufacturing (AM), slicing is a crucial step in process planning to convert a Computer-Aided Design (CAD) model to a machine-specific format. Digital Light Processing (DLP) printing is an important AM process that has a good surface finish, high accuracy and fabrication speed, and is widely applied in many dental and engineering industries. However, as DLP uses images for fabrication different from other toolpath-based processes, its process planning is understudied. Therefore, the main goal of this paper is to study and compare the slicing technologies for DLP printing. Three slicing technologies are compared: contour, voxelization, and ray-tracing.

Jida Huang, Tsz-Ho Kwok, Chi Zhou, and Wenyao Xu
The International Journal of Advanced Manufacturing Technology, Dec 2019, 105(9), 3593-3604.
[PDF][doi:10.1007/s00170-019-03792-1]

Abstract:
Support generation is one of the crucial steps in 3D printing to make sure the overhang structures can be fabricated. The first step of support generation is to detect which regions need support structures. Normal-based methods can determine the support regions fast but find many unnecessary locations which could be potentially self-supported. Image-based methods conduct a layer-by-layer comparison to find support regions, which could make use of material self-support capability; however, it sacrifices the computational cost and may still fail in some applications due to the loss of topology information when conducting offset and boolean operations based on the image. In order to overcome the difficulties of image-based methods, this paper proposes a surfel convolutional neural network (SCNN) based approach for support detection. In this method, the sampling point on the surface with normal information, named surfel (surface element), is defined through layered depth-normal image (LDNI) sampling method. A local surfel image which represents the local topology information of the sampling point in the solid model is then constructed. A set of models with ground-truth support regions is used to train the deep neural network. Experimental results show that the proposed method outperforms the normal-based method and image-based method in terms of accuracy, reliability and computational cost.

Christopher-Denny Matte, Michael Pearson, Felix Trottier-Cournoyer, Andrew Dafoe and Tsz-Ho Kwok
Rapid Prototyping Journal, Jun 2019, Vol. 25 No. 5, pp. 864-874.
[PDF][doi:10.1108/RPJ-08-2018-0211]

Abstract:
Digital light processing (DLP) printing uses a digital projector to selectively cure a full layer of resin using a mask image. One of the challenges with DLP printing is the difficulty of incorporating multiple materials within the same part. As the part is cured within a liquid basin, resin switching introduces issues of cross-contamination and significantly increased print time. In this paper, a novel technique for printing with multiple materials using the DLP method is introduced. The material handling challenges are investigated and addressed by taking inspiration from automated storage and retrieval systems, and utilizing an active cleaning solution. The material tower is a compact design to facilitate the storage and retrieval of different materials during the printing process. A spray mechanism is used for actively cleaning excess resin from the part between material changes. Challenges encountered within the multi-material DLP technology are addressed, and the experimental prototype validates the proposed solution. Our system has a cleaning effectiveness of over 90% in 15 seconds with the build area of 72 inches, in contrast to the previous work of 50% cleaning effectiveness in 2 minutes with only 6 inches build area. Our method can also hold more materials than the previous work. The techniques from automated storage and retrieval system (ASRS) is applied to develop a storage system, so that the time complexity of swapping is reduced from linear to constant. The whole system is sustainable and scalable by using a spraying mechanism. The design of the printer is modular and highly customizable, and the material waste for build materials and cleaning solution is minimized.

Yuen-Shan Leung, Tsz-Ho Kwok, Xiangjia Li, Yang Yang, Charlie C.L. Wang, and Yong Chen
ASME Transactions - Journal of Computing and Information Science in Engineering, 19(2), 021013 (Mar 18, 2019).
[PDF][doi:10.1115/1.4041913]

Abstract:
The revolution of additive manufacturing (AM) has led to many opportunities in fabricating complex and novel products. The increase of the printable materials and the emergence of the various fabricating processes continuously expand the capability of manufacturing. Our products are no longer limited to be single material, single scale or single function. In fact, a paradigm shift is taking place in the industries from geometry-centered usage to support functional demands, and hence it is expected to resolve wide range of complex and difficult problems. Although AM provides us higher design degree of freedom beyond the geometry to fabricate new objects with tailored properties and functions, there are only very few approaches for computational design in this new domain enabled by AM. The objectives of this study are to provide an overview on the current computer-aided design methodologies that are applied to multi-material, multi-scale, multi-form and multi-functional AM technologies. We summarize the difficulties encountered in the design approaches and emphasize the need for the future development. The study also introduces the related manufacturing processes, lists their present applications, and discusses their potential future trends.

Tsz-Ho Kwok
IEEE Transactions on Automation Science and Engineering, vol. 16, no. 1, pp. 241-252, Jan. 2019.
[PDF][doi:10.1109/TASE.2018.2802725][code]

Abstract:
Rigid registration is a fundamental process in many applications that require alignment of different datasets. Iterative Closest Point (ICP) is a widely used algorithm that iteratively finds point correspondences and updates the rigid transformation. One of the key variants of ICP to its success is the selection of points, which is directly related to the convergence and robustness of the ICP algorithm. Besides uniform sampling, there are a number of normal-based and feature-based approaches that consider normal, curvature, and/or other signals in the point selection. Among them, Normal Space Sampling (NSS) is one of the most popular techniques due to its simplicity and low computational cost. The rationale of NSS is to sample enough constraints to determine all the components of transformation, but this study finds that NSS actually can constrain the translational normal space only. This paper extends the fundamental idea of NSS and proposes Dual-Normal Space Sampling (DNSS) to sample points in both translational and rotational normal spaces. Compared with NSS, this approach has similar simplicity and efficiency without any need of additional information, but has a much better effectiveness. Experimental results show that DNSS can outperform the normal-based and feature-based methods in terms of convergence and robustness. For example, DNSS can achieve convergence from an orthogonal initial position while no other methods can achieve.

Guoxin Fang, Christopher-Denny Matte, Tsz-Ho Kwok, and Charlie C.L. Wang
IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018.
[PDF][video][doi:10.1109/ICRA.2018.8461088]

Abstract:
Robots fabricated by soft materials can provide higher flexibility and thus better safety while interacting with natural objects with low stiffness such as food and human beings. However, as many more degrees of freedom are introduced, the motion simulation of a soft robot becomes cumbersome, especially when large deformations are presented. Moreover, when the actuation is defined by geometry variation, it is not easy to obtain the exact loads and material properties to be used in the conventional methods of deformation simulation. In this paper, we present a direct approach to take the geometric actuation as input and compute the deformed shape of soft robots by numerical optimization using a geometry-based algorithm. By a simple calibration, the properties of multiple materials can be modeled geometrically in the framework. Numerical and experimental tests have been conducted to demonstrate the performance of our approach on both cable-driven and pneumatic actuators in soft robotics.

Jida Huang, Tsz-Ho Kwok, and Chi Zhou
ASME Transactions - Journal of Mechanical Design, 139(11), 111403 (Oct 02, 2017).
[PDF][doi:10.1115/1.4037477]

Abstract:
With the advances in three-dimensional (3D) scanning and sensing technologies, massive human-related data are now available and create many applications in data-driven design. Similarity identification is one of basic problems in data-driven design and can facilitate many engineering applications and product paradigm such as quality control and mass customization. Therefore, reusing information can create unprecedented opportunities in advancing the theory, method, and practice of product design. To enable information reuse, different models have to be aligned so that their similarity can be identified. This alignment is commonly known as the global registration that finds an optimal rigid transformation to align two 3D shapes (scene and model) without any assumptions on their initial positions. The Super 4-Points Congruent Sets (S4PCS) is a popular algorithm used for this shape registration. While S4PCS performs the registration using a set of 4 coplanar points, we find that incorporating the volumetric information of the models can improve the robustness and the efficiency of the algorithm, which are particularly important for mass customization. In this paper, we propose a novel algorithm, Volumetric 4PCS (V4PCS), to extend the 4 coplanar points to non-coplanar ones for global registration, and theoretically demonstrate the computational complexity is significantly reduced. Experimental tests are conducted on a number of models such as tooth aligner and hearing aid to compare with S4PCS. The experimental results show that the proposed V4PCS can achieve a maximum of 20 times speedup and can successfully compute the valid transformation with very limited number of sample points. An application of the proposed method in mass customization is also investigated.

Dongping Deng, Tsz-Ho Kwok, and Yong Chen
ASME Transactions - Journal of Mechanical Design, 139(8), 081702 (Jun 22, 2017).
[PDF][doi:10.1115/1.4036996]

Abstract:
Traditional origami structures fold along pre-defined hinges, and the neighboring facets of the hinges are folded to transform planar surfaces into three-dimensional (3D) shapes. In this study, we present a new self-folding design and fabrication approach that has no folding hinges and can build 3D structures with smooth curved surfaces. This four-dimensional (4D) printing method uses a thermal-response control mechanism, where a thermo shrink film is used as the active material and a photocurable material is used as the constraint material for the film. When the structure is heated, the two sides of the film will shrink differently due to the distribution of the constraint material on the film. Consequently, the structure will deform over time to a 3D surface that has no folding hinges. By properly designing the coated constraint patterns, the film can be self-folded into different shapes. The relationship between the constraint patterns and their correspondingly self-folded surfaces has been studied in the paper. Our 4D printing method presents a simple approach to quickly fabricate a 3D shell structure with smooth curved surfaces by fabricating a structure with accordingly designed material distribution.

Gang Xu, Tsz-Ho Kwok, and Charlie C.L. Wang
Computer-Aided Design, Volume 91, October 2017, Pages 1-13.
[PDF][doi:10.1016/j.cad.2017.04.002]

Abstract:
Volumetric spline parameterization and computational efficiency are two main challenges in isogeometric analysis (IGA). To tackle this problem, we propose a framework of computation reuse in IGA on a set of three-dimensional models with similar semantic features. Given a template domain, B-spline based consistent volumetric parameterization is first constructed for a set of models with similar semantic features. An efficient quadrature-free method is investigated in our framework to compute the entries of stiffness matrix by Bezier extraction and polynomial approximation. In our approach, evaluation on the stiffness matrix and imposition of the boundary conditions can be pre-computed and reused during IGA on a set of CAD models. Examples with complex geometry are presented to show the effectiveness of our methods, and efficiency similar to the computation in linear finite element analysis can be achieved for IGA taken on a set of models.

Tsz-Ho Kwok, Yong Chen
ASME Journal of Manufacturing Science and Engineering, 139(11), 111006 (Sep 13, 2017).
[PDF][doi:10.1115/1.4037429]

Abstract:
Four-dimensional (4D) printing is a new category of printing that expands the fabrication process to include time as the forth dimension, and its process planning and simulation have to take time into consideration as well. The common tool to estimating the behavior of a deformable object is the finite element method (FEM). Although FEM is powerful, there are various sources of deformation from hardware, environment, and process, just to name a few, which are too complex to model by FEM. This paper introduces Geometry-Driven Finite Element (GDFE) as a solution to this problem. Based on the study on geometry changes, the deformation principles can be drawn to predict the relationship between the 4D-printing process and the shape transformation. Similar to FEM, the design domain is subdivided into a set of GDFEs, and the principles are applied on each GDFE, which are then assembled to a larger system that describes the overall shape. The proposed method converts the complex sources of deformation to a geometric optimization problem, which is intuitive and effective. The usages and applications of the GDFE framework have also been presented in this paper, including freeform design, reserve design, and design validation.

Yunbo Zhang, and Tsz-Ho Kwok
Rapid Prototyping Journal, Vol. 23 Issue: 6, pp. 1136-1145, 2017.
[PDF][doi:10.1108/RPJ-08-2016-0129][video][program]

Abstract:
Additive Manufacturing (AM) enables the fabrication of three-dimensional (3D) objects with complex shapes without additional tools and refixturing. However, it is difficult for user to use traditional computer-aided design tools to design custom products. In this paper, we presented a design system to help user design custom 3D printable products on top of some freeform shapes. Users can define and edit styling curves on the reference model using our interactive geometric operations for styling curves. Incorporating with the reference models, these curves can be converted into 3D printable models through our fabrication interface. We tested our system with four design applications including a hollow-patterned bicycle helmet, a T-rex with skin frame structures, a face mask with Voronoi patterns, and an AM-specific night dress with hollow patterns. The executable prototype of the presented design framework used in the customization process is publicly available. 

Tsz-Ho Kwok, Hang Ye, Yong Chen, Chi Zhou, and Wenyao Xu
ASME Transactions - Journal of Computing and Information Science in Engineering, 17(2), 021009 (Feb 16, 2017).
[PDF][doi:10.1115/1.4034010]

Abstract:
Additive manufacturing, also known as 3D printing, enables production of complex customized shapes without requiring specialized tooling and fixture, and mass customization can then be realized with larger adoption. The slicing procedure is one of the fundamental tasks for 3D printing, and the slicing resolution has to be very high for fine fabrication, especially in the recent developed Continuous Liquid Interface Production (CLIP) process. The slicing procedure is then becoming the bottleneck in the pre-fabrication process, which could take hours for one model. This becomes even more significant in mass customization, where hundreds or thousands of models have to be fabricated. We observe that the customized products are generally in a same homogeneous class of shape with just a little variation. Our study finds that the slicing information of one model can be reused for other models in the same homogeneous group under a properly defined parameterization. Experimental results show that the reuse of slicing information have a maximum of 50 times speedup, and its utilization is dropped from more than 90% to less than 50% in the pre-fabrication process.