Towards a CAD-based automatic procedure for patient specific cutting guides to assist sternal osteotomies in pectus arcuatum surgical correction

Abstract Pectus Arcuatum, a rare congenital chest wall deformity, is characterized by the protrusion and early ossification of sternal angle thus configuring as a mixed form of excavatum and carinatum features. Surgical correction of pectus arcuatum always includes one or more horizontal sternal osteotomies, consisting in performing a V-shaped horizontal cutting of the sternum (resection prism) by means of an oscillating power saw. The angle between the saw and the sternal body in the V-shaped cut is determined according to the peculiarity of the specific sternal arch. The choice of the right angle, decided by the surgeon on the basis of her/his experience, is crucial for a successful intervention. The availability of a patient-specific surgical guide conveying the correct cutting angles can considerably improve the chances of success and, at the same time, reduce the intervention time. The present paper aims to propose a new CAD-based approach to design and produce custom-made surgical guides, manufactured by using additive manufacturing techniques, to assist the sternal osteotomy. Starting from CT images, the procedure allows to determine correct resection prism and to shape the surgical guide accordingly taking into account additive manufacturing capabilities. Virtually tested against three case studies the procedure demonstrated its effectiveness. Highlights Patient-specific surgical guide improves the chances of success in sternal osteotomy. A CAD-based approach to design and produce custom-made surgical guides is proposed. The proposed framework entails both a series of automatic and user-guided tasks

Multidisciplinary design optimization to identify additive manufacturing resources in customized product development

Additive manufacturing (AM) techniques are ideal for producing customized products due to their high design flexibility. Despite the previous studies on specific additive manufactured customized products such as biomedical implants and prostheses, the simultaneous optimization of components, materials, AM processes, and dimensions remains a challenge. Multidisciplinary design optimization (MDO) is a research area of solving complex design problems involving multiple disciplines which usually interact with each other. The objective of this research is to formulate and solve an MDO problem in the development of additive manufactured products customized for various customers in different market segments. Three disciplines, i.e. the customer preference modeling, AM production costing, and structural mechanics are incorporated in the MDO problem. The optimal selections of components, materials, AM processes, and dimensional parameters are searched with the objectives to maximize the functionality utility, match individual customers’ personal performance requirements, and minimize the total cost. A multi-objective genetic algorithm with the proposed chromosome encoding pattern is applied to solve the MDO problem. A case study of designing customized trans-tibial prostheses with additive manufactured components is presented to illustrate the proposed MDO method. Clusters of multi-dimensional Pareto-optimal design solutions are obtained from the MDO, showing trade-offs among the objectives. Appropriate design decision can be chosen from the clusters based on the manufacturer׳s market strategy

Performance of wearables and the effect of user behavior in additive manufacturing process

Additive manufacturing (AM) which can be a suitable technology to personalize wearables is ideal for adjusting the range of part performance such as mechanical properties if high performance is not required. However, the AM process parameter can impact overall durability and reliability of the part. In this instance, user behavior can play an essential role in performance of wearables through the settings of AM process parameter. This review discusses parameters of AM processes influenced by user behavior with respect to performance required to fabricate AM wearables. Many studies on AM are performed regardless of the process parameters or are limited to certain parameters. Therefore, it is necessary to examine how the main parameters considered in the AM process affect performance of wearables. The overall aims of this review are to achieve a greater understanding of each AM process parameter affecting performance of AM wearables and to provide requisites for the desired performance including the practice of sustainable user behavior in AM fabrication. It is discussed that AM wearables with various performance are fabricated when the user sets the parameters. In particular, we emphasize that it is necessary to develop a qualified procedure and to build a database of each AM machine about part performance to minimize the effect of user behavior

Curve Skeleton and Moments of Area Supported Beam Parametrization in Multi-Objective Compliance Structural Optimization

This work addresses the end-to-end virtual automation of structural optimization up to the derivation of a parametric geometry model that can be used for application areas such as additive manufacturing or the verification of the structural optimization result with the finite element method. A holistic design in structural optimization can be achieved with the weighted sum method, which can be automatically parameterized with curve skeletonization and cross-section regression to virtually verify the result and control the local size for additive manufacturing. is investigated in general. In this paper, a holistic design is understood as a design that considers various compliances as an objective function. This parameterization uses the automated determination of beam parameters by so-called curve skeletonization with subsequent cross-section shape parameter estimation based on moments of area, especially for multi-objective optimized shapes. An essential contribution is the linking of the parameterization with the results of the structural optimization, e.g., to include properties such as boundary conditions, load conditions, sensitivities or even density variables in the curve skeleton parameterization. The parameterization focuses on guiding the skeletonization based on the information provided by the optimization and the finite element model. In addition, the cross-section detection considers circular, elliptical, and tensor product spline cross-sections that can be applied to various shape descriptors such as convolutional surfaces, subdivision surfaces, or constructive solid geometry. The shape parameters of these cross-sections are estimated using stiffness distributions, moments of area of 2D images, and convolutional neural networks with a tailored loss function to moments of area. Each final geometry is designed by extruding the cross-section along the appropriate curve segment of the beam and joining it to other beams by using only unification operations. The focus of multi-objective structural optimization considering 1D, 2D and 3D elements is on cases that can be modeled using equations by the Poisson equation and linear elasticity. This enables the development of designs in application areas such as thermal conduction, electrostatics, magnetostatics, potential flow, linear elasticity and diffusion, which can be optimized in combination or individually. Due to the simplicity of the cases defined by the Poisson equation, no experts are required, so that many conceptual designs can be generated and reconstructed by ordinary users with little effort. Specifically for 1D elements, a element stiffness matrices for tensor product spline cross-sections are derived, which can be used to optimize a variety of lattice structures and automatically convert them into free-form surfaces. For 2D elements, non-local trigonometric interpolation functions are used, which should significantly increase interpretability of the density distribution. To further improve the optimization, a parameter-free mesh deformation is embedded so that the compliances can be further reduced by locally shifting the node positions. Finally, the proposed end-to-end optimization and parameterization is applied to verify a linear elasto-static optimization result for and to satisfy local size constraint for the manufacturing with selective laser melting of a heat transfer optimization result for a heat sink of a CPU. For the elasto-static case, the parameterization is adjusted until a certain criterion (displacement) is satisfied, while for the heat transfer case, the manufacturing constraints are satisfied by automatically changing the local size with the proposed parameterization. This heat sink is then manufactured without manual adjustment and experimentally validated to limit the temperature of a CPU to a certain level.:TABLE OF CONTENT III I LIST OF ABBREVIATIONS V II LIST OF SYMBOLS V III LIST OF FIGURES XIII IV LIST OF TABLES XVIII 1. INTRODUCTION 1 1.1 RESEARCH DESIGN AND MOTIVATION 6 1.2 RESEARCH THESES AND CHAPTER OVERVIEW 9 2. PRELIMINARIES OF TOPOLOGY OPTIMIZATION 12 2.1 MATERIAL INTERPOLATION 16 2.2 TOPOLOGY OPTIMIZATION WITH PARAMETER-FREE SHAPE OPTIMIZATION 17 2.3 MULTI-OBJECTIVE TOPOLOGY OPTIMIZATION WITH THE WEIGHTED SUM METHOD 18 3. SIMULTANEOUS SIZE, TOPOLOGY AND PARAMETER-FREE SHAPE OPTIMIZATION OF WIREFRAMES WITH B-SPLINE CROSS-SECTIONS 21 3.1 FUNDAMENTALS IN WIREFRAME OPTIMIZATION 22 3.2 SIZE AND TOPOLOGY OPTIMIZATION WITH PERIODIC B-SPLINE CROSS-SECTIONS 27 3.3 PARAMETER-FREE SHAPE OPTIMIZATION EMBEDDED IN SIZE OPTIMIZATION 32 3.4 WEIGHTED SUM SIZE AND TOPOLOGY OPTIMIZATION 36 3.5 CROSS-SECTION COMPARISON 39 4. NON-LOCAL TRIGONOMETRIC INTERPOLATION IN TOPOLOGY OPTIMIZATION 41 4.1 FUNDAMENTALS IN MATERIAL INTERPOLATIONS 43 4.2 NON-LOCAL TRIGONOMETRIC SHAPE FUNCTIONS 45 4.3 NON-LOCAL PARAMETER-FREE SHAPE OPTIMIZATION WITH TRIGONOMETRIC SHAPE FUNCTIONS 49 4.4 NON-LOCAL AND PARAMETER-FREE MULTI-OBJECTIVE TOPOLOGY OPTIMIZATION 54 5. FUNDAMENTALS IN SKELETON GUIDED SHAPE PARAMETRIZATION IN TOPOLOGY OPTIMIZATION 58 5.1 SKELETONIZATION IN TOPOLOGY OPTIMIZATION 61 5.2 CROSS-SECTION RECOGNITION FOR IMAGES 66 5.3 SUBDIVISION SURFACES 67 5.4 CONVOLUTIONAL SURFACES WITH META BALL KERNEL 71 5.5 CONSTRUCTIVE SOLID GEOMETRY 73 6. CURVE SKELETON GUIDED BEAM PARAMETRIZATION OF TOPOLOGY OPTIMIZATION RESULTS 75 6.1 FUNDAMENTALS IN SKELETON SUPPORTED RECONSTRUCTION 76 6.2 SUBDIVISION SURFACE PARAMETRIZATION WITH PERIODIC B-SPLINE CROSS-SECTIONS 78 6.3 CURVE SKELETONIZATION TAILORED TO TOPOLOGY OPTIMIZATION WITH PRE-PROCESSING 82 6.4 SURFACE RECONSTRUCTION USING LOCAL STIFFNESS DISTRIBUTION 86 7. CROSS-SECTION SHAPE PARAMETRIZATION FOR PERIODIC B-SPLINES 96 7.1 PRELIMINARIES IN B-SPLINE CONTROL GRID ESTIMATION 97 7.2 CROSS-SECTION EXTRACTION OF 2D IMAGES 101 7.3 TENSOR SPLINE PARAMETRIZATION WITH MOMENTS OF AREA 105 7.4 B-SPLINE PARAMETRIZATION WITH MOMENTS OF AREA GUIDED CONVOLUTIONAL NEURAL NETWORK 110 8. FULLY AUTOMATED COMPLIANCE OPTIMIZATION AND CURVE-SKELETON PARAMETRIZATION FOR A CPU HEAT SINK WITH SIZE CONTROL FOR SLM 115 8.1 AUTOMATED 1D THERMAL COMPLIANCE MINIMIZATION, CONSTRAINED SURFACE RECONSTRUCTION AND ADDITIVE MANUFACTURING 118 8.2 AUTOMATED 2D THERMAL COMPLIANCE MINIMIZATION, CONSTRAINT SURFACE RECONSTRUCTION AND ADDITIVE MANUFACTURING 120 8.3 USING THE HEAT SINK PROTOTYPES COOLING A CPU 123 9. CONCLUSION 127 10. OUTLOOK 131 LITERATURE 133 APPENDIX 147 A PREVIOUS STUDIES 147 B CROSS-SECTION PROPERTIES 149 C CASE STUDIES FOR THE CROSS-SECTION PARAMETRIZATION 155 D EXPERIMENTAL SETUP 15

Habilitador para colaboração no design para a customização em massa em micro e pequenas empresas

Orientador: Prof. Dr. Adriano HeemannTese (doutorado) - Universidade Federal do Paraná, Setor de Artes, Comunicação e Design, Programa de Pós-Graduação em Design. Defesa : Curitiba, 18/12/2019Inclui referências: p. 117-133Resumo: O processo design de produtos voltados para a produção através da manufatura tradicional apresenta metodologias e ferramentas já consolidadas, assim como estudos que abordam a colaboração entre os atores desse processo. Porém, o processo tradicional de design apresenta limitações para conseguir responder as exigências necessárias para que seja possível implementar o design para customização em massa (CM) nas empresas atualmente. Uma das formas de facilitar o processo da customização em massa é utilizando-se do design colaborativo. Considerando a inexistência de conhecimento estruturado que habilite a colaboração no design para customização em massa, um quadro teórico elaborado nesta tese evidencia uma lacuna na utilização concomitante destes conceitos, apesar da literatura indicar a importância da colaboração dos participantes do processo de design para customização em massa. Assim a presente pesquisa responde como viabilizar a colaboração entre os participantes no design para a customização em massa. Essa problemática é abordada de forma a descrever como habilitar a colaboração entre os participantes no design para a customização em massa e isso é alcançado através do desenvolvimento de um habilitador para o design colaborativo para customização em massa. Para tanto, este documento descreve a identificação de fatores críticos de sucesso para a customização em massa e para a colaboração no design de produtos. Essa identificação é feita por meio de uma revisão de literatura e de estudos de caso. Os fatores identificados são, então, utilizados para o desenvolvimento do habilitador. Esse habilitador se deu em formato de e-book e foi desenvolvido com o intuito de disseminar conhecimento principalmente para micro e pequenas empresas do Brasil que ofertam produtos customizados em massa. Palavras Chave: Design colaborativo. Customização em massa. Desenvolvimento de produto. Habilitador.Abstract: The product design process focused on production through traditional manufacturing has already consolidated methodologies and tools, as well as studies that address collaboration between the actors in this process. However, the traditional design process has limitations in order to be able to meet the necessary requirements so that it is possible to implement the design for mass customization (MC) in companies today. One of the ways to facilitate the mass customization process is by using collaborative design. Considering the lack of structured knowledge that enables collaboration in design for mass customization, a theoretical framework elaborated in this thesis highlights a gap in the concomitant use of these concepts, despite the literature indicating the importance of the collaboration of the participants in the design process for mass customization. Thus, this research answers how to enable collaboration between participants in the design for mass customization. This issue is addressed to describe how to enable collaboration between participants in the design for mass customization and this is achieved through the development of an enabler for collaborative design for mass customization. To this end, this document describes the identification of critical success factors for mass customization and collaboration in product design. This identification is made through a literature review and case studies. The identified factors are then used for the development of the enabler. This enabler took place in e-book format and was developed with the aim of disseminating knowledge mainly to micro and small companies in Brazil that offer mass customized products. Key words: Collaborative design. Mass customization. Product development. Enabler