Incorporating additive manufacturing (AM) constraints in topology optimisation can lead to performance optimality while ensuring manufacturability of designs. Numerical techniques have been previously proposed to obtain support-free designs in AM, however, few works have verified the manufacturability of their solutions. Physical verification of manufacturability becomes more critical recalling that the conventional density-based topology optimisation methods will inevitably require post-processing to smooth the boundaries before sending the results to a 3D printer. This paper presents the smooth design of self-supporting topologies using the combination of a new Solid Isotropic Microstructure with Penalisation method (SIMP) developed based on elemental volume fractions and an existing AM filter. Manufacturability of selected simulation results are verified with Fused Deposition Modeling (FDM) technology. It is illustrated that the proposed method is able to generate convergent self-supporting topologies which are printable using FDM.

This paper aims to explore the effects of optimisation parameters (the filter radius, mesh size, and target volume fraction) on the compliance and manufacturability of smooth self-supporting topologies for additive manufacturing (AM); and conduct manufacturability experiments of selected simulation results with the Selective Laser Melting (SLM) technology. In this study, smooth self-supporting topologies are obtained with a combined method of the Solid Isotropic Microstructure with Penalization method (SIMP) with the smooth boundary representation and Langelaar's AM filter. Numerical examples illustrate that a higher filter radius can lead to simpler smooth self-supporting topologies, but curved top corners that violate the critical overhang angle of 45° are inevitably generated. Additionally, the mesh size is found to be not as an important factor as the filter radius in affecting the manufacturability and performance. As a result of the self-supporting constraint, a small volume fraction can easily result in structural discontinuity. The experimental results demonstrate that the self-supporting topologies obtained are all printable in SLM.

Topology optimization (TO) is a powerful technique capable of finding the optimal layout of material and connectivity within a design domain. However, designs obtained by TO are usually geometrically complex. Such complex designs cannot be fabricated easily by conventional manufacturing methods. Therefore, additive manufacturing (AM), a free-form manufacturing technique, is extensively coupled with TO. Like most techniques, AM has its own limitations. Consequently, a range of additive manufacturing oriented topology optimization (AM oriented TO) algorithms were proposed to generate the topologies suitable for AM. Due to existing trade-off relationships in AM oriented TO, investigating multi-objective AM oriented TO seems essential to obtain more practical solutions. This paper provides a review on the recent developments of MOTO, AM oriented TO, and trade-off relationships that exist in AM oriented TO. This review paper also discusses the challenges and future trends in these topics. It is hoped that this review paper could inspire both academics and engineers to make a contribution towards bridging together MOTO and AM.

Additive manufacturing (AM) technology has been developed for more than thirty years and has become a mainstream manufacturing process. Sustainability is becoming increasingly significant for human’s health. In recent years, studies have been carried out to make AM a sustainable manufacturing technique. In this paper, research in improving manufacturing sustainability of material extrusion AM is reviewed. Studies with the aim of saving material, production time and energy in material extrusion AM processes are discussed. Future potential research directions are also identified for making AM more sustainable in the future.

This work proposes a non-penalization Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm, which is a typical elemental volume fraction-based topology optimization method, by adopting discrete variable sensitivities for solid, void, and assumed boundary elements instead of the continuous variable sensitivities used in the penalization one. In the proposed non-penalized SEMDOT algorithm, the material penalization scheme is eliminated. The efficiency, effectiveness, and general applicability of the proposed non-penalized algorithm are demonstrated in three case studies containing compliance minimization, compliant mechanism design, and heat conduction problems, as well as thorough comparisons with the penalized algorithm. In addition, the length scale control approach is used to solve the discontinuous boundary issue observed in thin and long structural features. The numerical results show that the convergency of the newly proposed non-penalization algorithm is stronger than the penalization algorithm, and improved results can be obtained by the non-penalized algorithm.

The smooth design of self-supporting topologies has attracted great attention in the design for additive manufacturing (DfAM) field as it cannot only enhance the manufacturability of optimized designs but can obtain light-weight designs that satisfy specific performance requirements. This paper integrates Langelaar’s AM filter into the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm—a new element-based topology optimization method capable of forming smooth boundaries—to obtain print-ready designs without introducing post-processing methods for smoothing boundaries before fabrication and adding extra support structures during fabrication. The effects of different build orientations and critical overhang angles on self-supporting topologies are demonstrated by solving several compliance minimization (stiffness maximization) problems. In addition, a typical compliant mechanism design problem—the force inverter design—is solved to further demonstrate the effectiveness of the combination between SEMDOT and Langelaar’s AM filter.