Eujin Pei

Lightweight parametric design optimization for 4D printed parts

4D printing is a technology that combines the capabilities of 3D printing with materials that can transform its geometry after being produced (e.g. Shape Memory Polymers). These advanced materials allow shape change by applying different stimulus such as heating. A 4D printed part will usually have 2 different shapes: a programmed shape (before the stimulus is applied), and the original shape (which is recovered once the stimulus has been applied). Lightweight parametric optimization techniques are used to find the best combination of design variables to reduce weight and lower manufacturing costs. However, current optimization techniques available in commercial 3D CAD software are not prepared for optimization of multiple shapes. The fundamental research question is how to optimize a design that will have different shapes with different boundary conditions and requirements. This paper presents a new lightweight parametric optimization method to solve this limitation. The method combines the Latin Hypercube design of experiments, Kriging metamodel and specifically designed genetic algorithms. The optimization strategy was implemented and automated using a CAD software. This method recognizes both shapes of the part as a single design and allows the lightweight parametric optimization to retain the minimum mechanical properties

A study of 4D printing and functionally graded additive manufacturing

Purpose The purpose of this paper is to extend existing knowledge of 4D printing, in line with Khoo et al. (2015) who defined the production of 4D printing using a single material, and 4D printing of multiple materials. It is proposed that 4D printing can be achieved through the use of functionally graded materials (FGMs) that involve gradational mixing of materials and are produced using an additive manufacturing (AM) technique to achieve a single component. Design/methodology/approach The latest state-of-the-art literature was extensively reviewed, covering aspects of materials, processes, computer-aided design (CAD), applications and made recommendations for future work. Findings This paper clarifies that functionally graded additive manufacturing (FGAM) is defined as a single AM process that includes the gradational mixing of materials to fabricate freeform geometries with variable properties within one component. The paper also covers aspects of materials, processes, CAD, applications and makes recommendations for future work. Research limitations/implications This paper examines the relationship between FGAM and 4D printing and defines FGAM as a single AM process involving gradational mixing of materials to fabricate freeform geometries with variable properties within one component. FGAM requires better computational tools for modelling, simulation and fabrication because current CAD systems are incapable of supporting the FGAM workflow. Practical implications It is also identified that other factors, such as strength, type of materials, etc., must be taken into account when selecting an appropriate process for FGAM. More research needs to be conducted on improving the performance of FGAM processes through extensive characterisation of FGMs to generate a comprehensive database and to develop a predictive model for proper process control. It is expected that future work will focus on both material characterisation as well as seamless FGAM control processes. Originality/value This paper examines the relationship between FGAM and 4D printing and defines FGAM as a single AM process that includes gradational mixing of materials to fabricate freeform geometries with variable properties within one component.

Future Challenges in Functionally Graded Additive Manufacturing

This chapter presents an overview of Functionally Graded Additive Manufacturing (FGAM) that is a layer-by-layer fabrication technique which involves gradationally varying the material organisation within a component to meet an intended function. The use of FGAM offers designers and engineers a huge potential to produce variable-property structures by strategically controlling the density of substances and blending materials that could lead to an entirely new class of novel applications. However, we are currently constrained by the lack of comprehensive ‘materials-product-manufacturing’ knowledge, guidelines and standards for best practices. We are on the cusp of a paradigm shift and suitable methodologies need to be established to fully exploit and enable the true potential of FGAM on a commercial and economic scale. As FGAM technology matures, a multidisciplinary approach is needed to train the next generation of Additive Manufacturing experts.

Functional, Technical and Economical Requirements Integration for Additive Manufacturing Design Education

Additive Manufacturing (AM) enables designers to consider the benefits of digital manufacturing from the early stages of design. This may include the use of part integration to combine all required functions, utilizing multiple materials, moving assemblies, different local properties such as colour and texture, etc. Cost analysis can also be factored in throughout the entire value chain, from design to the finishing operations in comparison to traditional processes and conventional ways of working. Therefore, the concept of Design for Additive Manufacturing (DfAM) is more than a geometrical issue on a CAD system, and not limited only to topological optimization or lattice integration.