Joining sheets made from dissimilar materials by hole hemming

This research work presents a new joining process based on the hemming process for attaching sheets made from dissimilar materials with very different mechanical properties. The process is termed ‘hole hemming’ and consists in producing a mechanical interlock between pre-drilled holes which can be made anywhere on the sheets. The process is carried out in a two-stage operation including flanging the hole of an outer sheet and bending the flange over the hole of an inner sheet. First, the joining stages and the required tools are designed. Then, the joining of DP780 steel and AA6061-T6 aluminium alloy sheets, which are applied to manufacture lightweight structures in the automotive industries, is investigated using finite element analysis. Results show that the hole hemming process is able to successfully join these materials without fracture. The hole-hemmed joint withstood the maximum forces of 2.5 and 0.5 kN in single-lap shear and peel tests, respectively, and failed with hole bearing mode which is known as a gradual failure mode. The results demonstrate the applicability of the hole hemming process for joining dissimilar materials.

Bamboo Connection Technology for Lightweight Structures

Bamboo construction is often related to traditional and vernacular architecture, which is found mostly in rural areas, where, for the construction, local people apply diverse techniques learned in an empirical way and passed on from generation to generation. However, in the last years, many modern constructions with bamboo have been developed around the world. At the same time, many connections have been designed for permanent and ephemerals lightweight structures. However, most of them do not have standardization and mechanical testing, because it is expensive or there are no means to do it. Therefore, it is required to create a technology classification for the most used existing connections, starting with the traditional way to join canes until the contemporary connections developed with high technology. In this context, connections are a challenge to be developed, as currently there is no normative in bamboo to follow and create standardization.

TOPOLOGY OPTIMIZATION OF LIGHTWEIGHT STRUCTURES WITH APPLICATION TO BONE SCAFFOLDS AND 3D PRINTED SHOES FOR DIABETICS

An automatic complex topology lightweight structure generation method (ACTLSGM) is presented to automatically generate 3D models of lightweight truss structures with a boundary surface of any shape. The core idea of the ACTLSGM is to use the PIMesh, a mesh generation algorithm developed by the authors, to generate node distributions inside the object representing the boundary surface of the target complex topology structures; raw lightweight truss structures are then generated based on the node distributions; the resulting lightweight truss structure is then created by adjusting the radius of the raw truss structures using an optimization algorithm based on finite element truss analysis. The finite element analysis-based optimization algorithm can ensure the resulting structures satisfy the design requirements on stress distributions or stiffness. Three demos, including a lightweight structure for a cantilever beam, a femur bone scaffold, and a 3D shoe sole model with adaptive stiffness that can be used to adjust foot pressure distributions for patients with diabetic foot problems, are generated to demonstrate the performance of the ACTLSGM. The ACTLSGM is not limited to generating 3D models of medical devices, but can be applied in many other fields, including 3D printing infills and other fields where customized lightweight structures are required.

Inverting the structure–property map of truss metamaterials by deep learning

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.

Fabrication of Tailor-Made Metallic Structures for Lightweight Applications and Mechanical Behaviour

Tailor-made metallic structures are fabricated by welding, adhesive bonding, and mechanical joining methods. Here the aim is not only to fabricate lightweight structures, but also to develop novel methods of joining. Lightweight structures are advantageous in several ways including reduction of fuel consumption and vehicle emissions. Developing novel methods of joining is advantageous due to the possibility of joining of dissimilar materials, improved mechanical performance, and microstructures. In the chapter, initially, tailor-welded blanks (TWB) are introduced, and after that, fabrication of TWBs by laser welding, friction stir welding, and friction stir additive manufacturing are elaborately discussed. Some critical issues in modeling the deformation during fabrication of TWBs is also discussed. A brief account of mechanical behavior of adhesive bonded sheets and mechanical joining are presented in the later part.

Experimental Work for Bar Straightness Effect Evaluation of Split Hopkinson Pressure Bar

Bar straightness is one of several factors that can affect the quality of the strain wave signal in a Split Hopkinson Pressure Bar (SHPB). Recently, it was found that the bar components of the SHPB at the Lightweight Structures Laboratory displayed a deviation in straightness because of manufacturing limitations. An evaluation was needed to determine whether the strain wave signals produced from this SHPB are acceptable or not. A numerical model was developed to investigate this effect. In this paper, experimental work was performed to evaluate the quality of the signal in the SHPB and to validate the numerical model. Good agreement between the experimental results and the numerical results was obtained for the strain rates and stress-strain relationship for mild steel ST37 and aluminum 6061 specimen materials. The recommended bar straightness tolerance is proposed as 0.36 mm per 100 mm.

DIGITAL FABRICATION AS A LEARNING MEDIA FOR LIGHTWEIGHT STRUCTURE WITH CASE STUDY OF SHELL STRUCTURE

The lightweight structure system is an effort to optimize the structure to distribute the load efficiently. Unfortunately, students often have difficulty imagining the learning outcomes application in the real world when studying light structural systems. However, the use of the scalar model can still explain several essential aspects of a lightweight structural system, one of which is the effect of connection and formation of material components on the structural capability. Therefore, this paper aims to bridge the learning process by utilizing digital devices from the concept stage of structural modeling with the help of software (Rhinoceros, Grasshopper, and Kangaroo) to the realization process using laser cutting. The method used is a semi-experimental method that applies Hooke's law principle, which produces a shell structure system with a digital fabrication approach that utilizes a lightweight material, namely, corrugated paper board, as the primary material. This paper concludes that digital technology and digital fabrication processes can help students understand the concept of lightweight structures because they can use computer simulations, cut them using laser cutting, and assemble them in the field in a series of simultaneous processes.

Hybrid manufacturing of topology optimized machine tool parts through a layer laminated manufacturing method

Load-oriented lightweight structures are commonly designed based on topology optimization. For machine tool parts, they enable the reduction of moving masses and therefore increase the resource and energy efficiency of production systems. However, this usually results in complex part structures that are difficult or impossible to produce using conventional manufacturing methods. In this paper, a hybrid layer laminated manufacturing (LLM) method is proposed enabling manufacturing of topology-optimized machine tool parts. The method is referred to as hybrid, as the subtractive structuring of metal sheets is combined with the additive joining of the sheets by adhesive bonding. This enables enclosed inner cavities without support structures, which are used to approximate the optimal density distribution from a topology optimization via manufacturing. The proposed LLM method is validated on the basis of a bearing block of a ball screw feed drive. A experimental study in the time and frequency domain on a test rig confirms the principle suitability of the LLM method for the production of industrial applicable lightweight components.

Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

Development of Fused Deposition Modeling Using Five-Axis Machine and Printing Methods

Fused deposition modeling (FDM) is one of most widely used 3D printing technologies due to inexpensive equipment and materials, and easy to operate. FDM forms a 3D geometry by slicing a model along the XY-plane and assembling the resulting individual layers along the Z-axis, with extruded thermoplastic filaments. FDM printed parts usually need supporting structures, have stair step effect, and unfavorable mechanical properties. In order to address these deficiencies, a five-axis 3D printer and corresponding printing methods are proposed and developed in this paper. The 3D printer was designed five degrees of freedom through adding a platform that can rotate and swing. Based on the obtained results from different case studies, the discussed machine and methods could become more significant in industrial applications such as low cost, fabricating parts with better surface quality and lightweight structures.