Conventional and AI-Assisted Topology-Driven Workflows for Injection-Molded Lightweight Structures: A Quantitative Case Study

Introduction: Topology optimization has been widely used in the fields of mechanical and structural engineering. In the field of architecture, especially in the context of lightweight structures, a strong understanding of programming is essential for meaningful involvement. The purpose of the study was to establish a fundamental framework that facilitates the seamless implementation of topology optimization in the design of lightweight structures by architects. Methods: This work employed a deductive approach to analyze six case studies that involve the application of topology optimization in various lightweight constructions. The analysis was conducted based on a predefined set of criteria. Additionally, the deductive technique was used to establish a framework for implementing topology optimization in the design of lightweight structures. Finally, the framework was used to create an optimized lightweight structure (a pentagonal Roman vault). Findings: An analysis of all case studies was conducted using two distinct processes: the form-finding process and the fabrication process. This inquiry aimed to determine the procedural framework involved in the design and fabrication process of each case study. The underlying framework was derived through an analytical comparison of these six case studies. This framework enables the production of an optimized lightweight structure. Novelty: This study presents significant findings on topology optimization and its use in lightweight structures, offering essential insights for architects seeking to create aesthetically pleasing and distinctive architectural forms that prioritize high stiffness and low mass.

In recent years, 3D printing has extended its applications from conventional prototyping to direct fabrication of functional parts. The advantage of 3D printing in its design flexibility and complexity has driven novel design methods that can get over traditional manufacturing constraints, and has been used to designs of lightweight structures or functional products. In the present study, we developed an automatic design of lightweight structures for weight reduction and better ventilation of 3D printed parts. Two automatic design methods of lightweight structures based on finite element (FE) mesh were developed: porous shell structures based on 3D triangular mesh and lightweight cellular structures based on 3D tetrahedral mesh.

Generative design combined with topology optimization for lightweight product structure: An implementation path

This approach for reliability-based design optimization (RBDO) of buildings using surrogate models in artificial intelligence (AI). RBDO aims to find the optimal design of structural systems that satisfy performance requirements while considering uncertainties in the design variables and the loads. Traditional RBDO methods often require a large number of computationally expensive simulations, which hinder their practical applicability. The proposed approach utilizes surrogate models, trained using AI algorithms, to approximate the structural response and reliability analysis. This enables a significant reduction in computational cost while maintaining accuracy. The paper outlines the methodology, discusses the construction of surrogate models, describes the RBDO formulation, presents a case study, and provides insights into the efficiency and effectiveness of the proposed approach. Reliability-based design optimization (RBDO) plays a crucial role in enhancing the performance and safety of buildings by considering uncertainties associated with structural design. However, the conventional RBDO methods often suffer from high computational costs, making them impractical for real-world applications. This abstract presents an innovative approach that leverages surrogate models in artificial intelligence (AI) to achieve efficient and reliable design optimization for buildings. The proposed methodology integrates surrogate models, such as neural networks and Gaussian processes, with RBDO techniques to create a computationally efficient framework for optimizing building designs while accounting for uncertainties. Surrogate models serve as approximation functions that mimic the behaviour of complex structural analysis models, enabling rapid evaluations of the structural responses and associated reliability metrics. To establish accurate surrogate models, a set of training samples is generated by exploring the design space using a sampling strategy, such as Latin hypercube sampling or Monte Carlo simulation. These samples are used to train the surrogate models, which can then predict the response and reliability metrics for any given set of design variables, eliminating the need for repetitive and time-consuming evaluations of the full-fledged structural models. The surrogate models are integrated within an RBDO framework, which combines optimization algorithms, reliability analysis methods, and surrogate model-based response surface models. This integration enables the efficient exploration of the design space to identify the optimal design that minimizes cost or maximizes performance while satisfying reliability constraints. By utilizing surrogate models, the proposed approach significantly reduces the computational burden associated with RBDO of buildings, allowing for more extensive exploration of the design space within feasible timeframes. The surrogate-based optimization process achieves near-real-time design evaluations, enabling designers and engineers to make informed decisions promptly. The effectiveness and efficiency of the proposed methodology are demonstrated through case studies involving various building types and design objectives. The results highlight the significant computational savings achieved by surrogate models while maintaining accurate predictions of structural responses and reliability metrics. The integration of surrogate models provides a powerful tool for designers and engineers to enhance the structural performance and safety of buildings while considering uncertainties, paving the way for more advanced and practical design optimization methodologies in the field of civil engineering.

The lightweight design of bucket wheel stacker reclaimer structures is crucial for improving energy efficiency and reducing material costs in bulk material handling systems. Traditional optimization methods rely on iterative finite element analyses, which are computationally expensive and limit design space exploration capabilities. This study proposes an intelligent optimization framework for the wheel body structure based on Random Forest Regression machine learning algorithm. Nine structural design variables were identified, and 55 design samples were generated using Latin Hypercube Sampling to train surrogate models predicting equivalent stress, total deformation, and structural volume. The trained Random Forest Regression models achieved R-squared values exceeding 0.75 on testing datasets, demonstrating excellent predictive accuracy with minimal computational cost. Feature importance analysis identified inner rim radius, web thickness, and sector distance as the most influential parameters governing structural performance. Integration of the machine learning surrogate model with multi-objective genetic algorithm optimization yielded an optimal design configuration achieving 28.40 percent mass reduction compared to the original design, while maintaining maximum equivalent stress at 153 MPa and maximum deformation at 8.92 mm, both within allowable limits. The proposed machine learning-based optimization framework significantly reduces computational time from hours to milliseconds per design evaluation, enabling efficient design space exploration and providing a practical methodology for intelligent structural optimization in material handling equipment engineering.

Comparative study on life cycle assessment of components produced by additive and conventional manufacturing process

Topology-optimized hybrid solid-lattice structures for efficient mechanical performance

ABSTRACTWith increasing concern about cost reduction and environmental destruction, lightweight structural design is essential in the product design process. As a means of yielding products with more lightweight and efficient structures, in this paper a method of extracting useful features from a biological lightweight structure, that of the American lobster is proposed. Tensile tests were conducted on biological specimens using plastic clamps made by a 3D printer and clarified some rules for the design of lightweight and efficient structures. Using the rules of material design, a novel method for acquiring bio-inspired lightweight designs was proposed and its feasibility was shown by FEM simulation.

Lightweight structural design requires balancing conflicting objectives: minimizing mass while maximizing crashworthiness, energy absorption, and structural integrity. Conventional optimization techniques—such as gradient-based solvers, evolutionary algorithms, and surrogate modeling—often struggle with the nonlinear, multi-objective, and combinatorial nature of structural configurations, especially in applications like automotive chassis design, aerospace components, and protective systems. This study explores the use of quantum algorithms to accelerate and enhance the optimization of lightweight structures with respect to crash energy absorption. Leveraging quantum annealing, the Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantum–classical variational methods, the framework maps topology, material distribution, and structural geometry to quantum-encoded optimization landscapes. Early simulation results indicate that quantum-enabled solvers can identify higher-performing structural designs and navigate the trade-off between stiffness and crash energy dissipation more efficiently than classical algorithms. These findings highlight the potential of quantum computing to revolutionize structural engineering workflows by delivering faster, more robust, and more scalable optimization for next-generation lightweight designs.

Structure lightweight has always been a hot topic in the field of engineering. Lattice structure has characteristics of light weight, high specific stiffness and high specific strength because of its high porosity and low relative density. It has been widely used in structural lightweight design. In addition, lattice structure has broad application prospects in advanced industrial equipment, due to the potential of anti-vibration, anti-impact, heat transfer and heat dissipation, zero/negative thermal expansion, electromagnetic wave absorption, sound absorption and noise reduction. In this paper, a design method of function-structure integrated lattice structure is proposed, topology optimization and variable density lattice filling technology are studied, the research methods of multi- functional characteristics of lattice structures are discussed, and the related simulation and experimental methods are introduced, which is of certain reference significance to the development of technology in this field.

Design of light weight structures is an important aspect in the aircraft industry, since minimizing the weightof components improves the overall aircraft performance. However, conventional manufacturing methods work on standard geometries and shapes, and often lead to overdesigning of parts. Additive Manufacturing (AM) overcomes these issues by allowing more design freedom. The present study focuses on two aspects of AM: (1) part consolidation through topology optimization, and (2) addressing thermal distortion through reverse shape morphing. An assembly of two load bearing brackets is first amalgamated into a single Topology Optimized (TO) part, which satisfies the displacement and stress requirements of the original design. After a series of optimization iterations, the final TO part (278 g) weighs 69 % lesser than the original assembled design (909 g), still meeting the design constraints. The TO part thus eliminates the need of fasteners to join both the brackets, thereby, making the design simpler yet effective. Moreover, a homogeneous stress distribution in the optimized part allows for efficient material utilization. In order to overcome thermal distortion that results during the AM process, the shape of the TO part is transformed in a sense opposite to the distortions produced. This is achieved through reverse shape morphing technique, that reduces thermal distortions in the printed part to sub-micron levels, and the morphed TO part conforms to the requirements meeting the design constraints. Therefore, the implementation of topology optimization along with reverse shape morphing makes the design simple and efficient having reduced distortion. This is achieved without any need of modifications in the manufacturing system or equipment, and such a strategy can be replicated and implemented at industrial scale as well.

Topology optimization technology is often used in the design of lightweight structures under the condition that mechanical performance should be guaranteed, but a topology-optimized structure is often complicated and difficult to process using traditional machining technology. In this study, the topology optimization method, with a volume constraint and the minimization of structural flexibility, is applied to the lightweight design of a hinge bracket for civil aircraft. A mechanical performance analysis is conducted using numerical simulations to obtain the stress and deformation of the hinge bracket before and after topology optimization. The numerical simulation results show that the topology-optimized hinge bracket has good mechanical properties, and its weight was reduced by 28% compared with the original design of the model. In addition, the hinge bracket samples before and after topology optimization are prepared with additive manufacturing technology and mechanical performance tests are conducted using a universal mechanical testing machine. The test results show that the topology-optimized hinge bracket can satisfy the mechanical performance requirements of a hinge bracket at a weight loss ratio of 28%.

By means of finite element analysis and topology optimization methods, the structural optimization and lightweight design of the robotic arm for the installation of gas drainage pipelines are realized, so that it can realize more complex actions under the premise of ensuring stability. SolidWorks 3D modeling and Ansys Workbench analysis software are used to build models of the robotic arm and perform static analysis. The structural design is optimized through the deformation and stress distribution when grasping the maximum weight. The topology optimization mathematical model of the variable density method and the SIMP (Simplified Isotropic Material with Penalization) interpolation model was constructed, and the topology optimization of the robotic arm was performed in the Shape Optimization module in Ansys Workbench. On this basis, the structure after topology optimization is analyzed and studied. The results show that compared with before topology optimization, the mass of the manipulator is reduced by 29.65%, and the maximum deformation and maximum stress are both reduced by 10%, which confirms the feasibility of lightweight design and structural optimization of the manipulator.

Lightweight structural design is greatly valued in the aviation, aerospace, and automotive industries. Three-dimensional (3D) printing techniques provide viable and popular technical pathways for the rapid design and manufacturing of lightweight lattice structures. Unlike the conventional design idea of a geometrically homogenized lattice structure, this work provides a design method for structurally heterogeneous lattice according to the spatial stress state of 3D-printed parts. Following the quasi-static stress numerical simulations of solid components, finite element mesh units were inconsistently replaced by lattice units with different specific rigidities corresponding to the localized stress levels. Relying on the topology optimization further lightened the lattice structure under quasi-static stress after removing some parts with extremely low stress from the overall structure. As an embodiment of this design idea, face-centered cubic (FCC) lattice units with different strut diameters were employed to non-uniformly and adaptively fill a solid part under localized loading. The topological optimization was conducted on the solid part globally. Then, the topologically optimized solid and the heterogeneous lattice structure were subjected to the geometric Boolean operation. Stereolithographic 3D printing was utilized to fabricate the homogeneous and heterogeneous lattice structural parts for comparative tests of three-point bending. Three evaluation indicators were defined for the standardized assessment of the geometrically complex lattice structures for the performance evaluation. This demonstrated that the heterogeneous lattice part exhibited better comprehensive mechanical performance than the uniform lattice. This work proved the feasibility of this new perspective on 3D-printed lightweight structure design and topology optimization.

Lattice structure can realize excellent multifunctional characteristics because of its huge design space, and the cellular configuration directly affects the lattice structural performance and lightweight. A novel energy-absorbing multifunctional lattice structure with phononic bandgap is presented by topology and parameter optimization in this paper. First, the two-dimensional (2D) cellular configuration is lightweight designed by using independent continuous mapping (ICM) topology optimization method. The 2D cell is reconstructed by geometric parameters and rotated into a three-dimensional (3D) cell by using chiral shape to achieve bandgap. Subsequently, the surrogated model with energy absorption as the object and first-order natural frequency as the constraint is established to optimize a parametric 3D cell based on the Response Surface Methodology (RSM). Finally, the lattice structures are assembled with dodecagonal staggered arrangements to avoid the deformation interference among the adjacent cells. In addition, the lattice structural energy absorption and bandgap characteristics are analyzed and discussed. Compared to Kelvin lattice structure, the optimal lattice structure shows significant improvement in energy absorption efficiency. Besides, the proposed design also performs well in damping characteristics of the high-frequency and wide-bandgap. The lattice structural optimization design framework has great meaning to achieve the equipment structural lightweight and multi-function in the aerospace field.