Structural Design Strategies for the Production of Internal Combustion Engine Components by Additive Manufacturing: A Case Study of a Connecting Rod

Topology optimization and lattice design strategies are excellent tools within the design for additive manufacturing (DfAM) workflow as they generate structurally optimal, lightweight, and complex features often difficult to produce by conventional manufacturing methods. Moreover, topology optimization approaches are quickly evolving to accommodate AM-related processes and geometric constraints. In this study, the re-design of the connecting rod of an internal combustion engine (ICE) is explored by topology optimization and lattice structures. In both topology optimization and lattice design, the objective is to maximize their structural performances while constraining material usage. Structural analyses are carried out on the optimized topologies to compare their mechanical performances with a benchmark design. Results show that the redesign of the connecting rod through topology optimization alone can realize 20% material savings with only a 5% reduction in the factor of safety. However, the combination of topology optimization and lattice structure design can result in over 50% material savings with a 21–26% reduction in the factor of safety. For manufacturability, the fast-predictive inherent strain model shows the designs through topology optimization and lattice design gives the lowest process-induced deformations before and after support structure removal

Additively manufactured metallic biomaterials

Metal additive manufacturing (AM) has led to an evolution in the design and fabrication of hard tissue substitutes, enabling personalized implants to address each patient's specific needs. In addition, internal pore architectures integrated within additively manufactured scaffolds, have provided an opportunity to further develop and engineer functional implants for better tissue integration, and long-term durability. In this review, the latest advances in different aspects of the design and manufacturing of additively manufactured metallic biomaterials are highlighted. After introducing metal AM processes, biocompatible metals adapted for integration with AM machines are presented. Then, we elaborate on the tools and approaches undertaken for the design of porous scaffold with engineered internal architecture including, topology optimization techniques, as well as unit cell patterns based on lattice networks, and triply periodic minimal surface. Here, the new possibilities brought by the functionally gradient porous structures to meet the conflicting scaffold design requirements are thoroughly discussed. Subsequently, the design constraints and physical characteristics of the additively manufactured constructs are reviewed in terms of input parameters such as design features and AM processing parameters. We assess the proposed applications of additively manufactured implants for regeneration of different tissue types and the efforts made towards their clinical translation. Finally, we conclude the review with the emerging directions and perspectives for further development of AM in the medical industry.National Institutes of Health || The Natural Sciences and Engineering Research Council of Canada || Network for Holistic Innovation in Additive Manufacturin

Design for Additive Manufacturing: Topology Optimization of Structures under Design-Dependent Loads and Manufacturing Constraints

The advances in additive manufacturing (AM) have opened new possibilities in design and manufacturing that were not previously attainable. AM has found application in several industries such as aerospace, medical, automotive, power, and a host of others. Of its many unique selling points, one of the most appealing is the design freedom it offers due to its capability to produce structurally complex parts. In design for additive manufacturing (DfAM), one of the most common structural design tools is topology optimization. Topology optimization is a mathematical tool that obtains the optimal structural layout of a design for an objective, and together with additive manufacturing, optimal structural designs are possible with almost no manufacturability concern so long certain constraints are adhered to. Many efforts have gone into topology optimization for design-independent loads such as point forces, restricted or design-independent pressure loads, torques, etc., including the considerations for additive manufacturing. However, fewer works exist for design-dependent loads, especially when multiple load cases are concerned. Furthermore, even fewer works have attempted to develop topology optimization models for design-dependent loads while considering manufacturability. In this research, frameworks have been proposed to handle topology optimization of structures under design-dependent loads (thermal stress load – TSL, centrifugal loads, and design-dependent pressure loads) and AM constraints (overhang and feature size control). The first framework for design-dependent loads is achieved by introducing the Boundary Identification and Load Evolution (BILE) model, load thresholding, and sensitivity scaling in a weighted multiobjective topology optimization process. The BILE model, specifically for pressure loads, resulted in optimal designs under 80 seconds and 100 iterations for 5,000 to 13,000 design elements using a regular desktop computing power. Load thresholding applied to thermal stress loads for a threshold η=0.8, resulted in a reasonably stable optimization process. Also, sensitivity scales were applied to sensitivity contributions from TSLs and centrifugal loads. This ensured that optimal solutions were obtained with fewer numerical instabilities while using simple optimizers at low optimization iterations. The second framework developed is a post-topology optimization process for overhang feature elimination. Aside from the fact that volume correction is possible in this framework, boundary identification and overhang elimination, being key stages in the model, consumed only 8% of the combined process of topology optimization and overhang feature control. Two design case studies were printed using Laser Powder Bed Fusion (LPBF) and Material Extrusion, known as Fused Deposition Modeling (FDM), for manufacturability validation. Additional performance validation studies to investigate the relationship between manufacturing constraints, residual surface stresses, and structural performance were carried out on the optimized LPBF parts. No meaningful effect of overhang and feature size control was discovered on the residual stresses formed for Hastelloy X parts. However, bending test results revealed a 30% decrease in the maximum load (and a 40% increase in compliance) when a 65° overhang angle constraint was placed on a Hastelloy X optimized part. Fortunately, the performances improve when smaller angles (45° or 50°) and feature size (5a) constraints are imposed. Finally, an image-based initialization and post-processing code for topology optimization is provided to aid research and teaching

Load prediction on metal forming process (tube sinking) using finite element method

This study focuses on the Bubnov-Galerkin finite element model used to obtain the stresses and pressure fields set up at various cross-sections of a blank during the metal forming process. Four Lagrange quadratic elements were assembled to represent the various blanks. The governing equation adopted is a one-dimensional differential equation describing the pressures and stresses exerted on the forming process. In conducting the analysis, the various blanks are divided into a finite number of elements and further applying the Bubnov-Galerkin-weighted residual method to obtain the weighted integral form; the finite element model is obtained in a matrix form, from the weighted residual boundary conditions which are now applied to obtain the pressure distribution across the cross-section of the various blanks. Finite element results are obtained for a value of the coefficient of friction, die angle, length, and blank radius. Solution of the finite element method was compared with the exact solution, along with an experimental test known as press-fit analysis using Ansys Workbench, a program-controlled mesh of polar hexahedral elements which was utilized to further validate the result. Furthermore, the use of a computer-aided software assists in visualizing the solution of the tube sinking problem. The results were all presented in both tabular and graphical modes. This study shows that there are potentials for using this approach in engineering practices to ensure that the strength of materials be considered before usage in design and fabrication. These results are expected to improve advance knowledge in manufacturing processes

High-resolution inherent strain method using actual layer thickness in laser powder bed fusion additive manufacturing with experimental validations

Laser powder bed fusion (LPBF) additive manufacturing (AM) broadens the horizons of design in academia and industry. However, LPBF contends with challenges like residual stresses and distortions due to uneven heating and cooling, leading to substantial resource wastage. Accurately predicting residual stresses and distortions remains a hurdle, primarily due to the need for high-resolution modeling. In this study, a high-resolution model of the inherent strain method (ISM) with actual layer thickness for a cantilever geometry in LPBF is proposed for the first time. Experimental and numerical findings indicate that as the number of layers increases, distortions tend to decrease, while the residual stresses on the top surface consistently remain constant and close to the material's yield stress. The model achieves good agreement (error of 7.1%) for deformation, while the prediction error of residual stresses is reduced from 69% to 32% compared to a traditional ISM model

Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook

Metal additive manufacturing is gaining immense research attention. Some of these research efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure–property characterisation, structural design optimisation, or equipment enhancements for cost reduction and faster throughputs. In this review, the focus is drawn on the utilisation of topology optimisation for structural design in metal additive manufacturing. First, the symbiotic relationship between topology optimisation and metal additive manufacturing in aerospace, medical, automotive, and other industries is investigated. Second, support structure design by topology optimisation for thermal-based powder-bed processes is discussed. Third, the introduction of capabilities to limit manufacturing constraints and generate porous features in topology optimisation is examined. Fourth, emerging efforts to adopt artificial intelligence models are examined. Finally, some open-source and commercial software with capabilities for topology optimisation and metal additive manufacturing are explored. This study considers the challenges faced while providing perceptions on future research directions