Purpose: Methods to optimize lattice structure design, such as ground structure optimization, have been shown to be useful when generating efficient design concepts with complex truss-like cellular structures. Unfortunately, designs suggested by lattice structure optimization methods are often infeasible because the obtained cross-sectional parameter values cannot be fabricated by additive manufacturing (AM) processes, and it is often very difficult to transform a design proposal into one that can be additively designed. This paper aims to propose an improved, two-phase lattice structure optimization framework that considers manufacturing constraints for the AM process. Design/methodology/approach: The proposed framework uses a conventional ground structure optimization method in the first phase. In the second phase, the results from the ground structure optimization are modified according to the pre-determined manufacturing constraints using a second optimization procedure. To decrease the computational cost of the optimization process, an efficient gradient-based optimization algorithm, namely, the method of feasible directions (MFDs), is integrated into this framework. The developed framework is applied to three different design examples. The efficacy of the framework is compared to that of existing lattice structure optimization methods. Findings: The proposed optimization framework provided designs more efficiently and with better performance than the existing optimization methods. Practical implications: The proposed framework can be used effectively for optimizing complex lattice-based structures. Originality/value: An improved optimization framework that efficiently considers the AM constraints was reported for the design of lattice-based structures.

Usage of discontinuous glass fibers in injection- and compression-molded resin components is rapidly increasing to improve their mechanical properties. Since added fiber contributes to more strength along the fiber direction compared with transverse direction, the mechanical properties of such components strongly depend on the fiber orientation. Therefore, it is important to estimate the fiber orientation distribution in such materials. In this article, we are presenting a recently developed method to estimate fiber orientation using micro computerized tomography (CT) scan-generated three-dimensional (3-D) image of fibers. However, the large size of the CT scan-generated 3-D image often makes it difficult to separate each fiber and extract end point information. In this article, a novel method to address this challenge is presented. The micro-CT images were broken into finite volume, reducing data size, and then each fiber was reduced to its own centerline, using Mimics^® Innovation Suite (Materialise NV), further reducing the data size. These 3-D centerlines were then used to quantify the second-order orientation tensor. The results from the proposed method are compared with the measurements using well-established industry standard approach called the method of ellipses for validation. The key challenges in estimating the fiber orientation are identified and future improvements are proposed.

Fe-Mn-Al-Ni is an Fe-based shape memory alloy (SMA) featuring higher stability and low temperature dependency of superelasticity stress over a wide range of temperatures. Additive manufacturing (AM) is a promising technique for fabricating Fe-SMA with enhanced properties, which can eliminate the limitations associated with conventional fabrication and allow for the manufacture of complicated shapes with only a single-step fabrication. The current work investigates the densification behavior and fabrication window of an Fe-Mn-Al-Ni SMA using laser powder bed fusion (LPBF). Experimental optimization was performed to identify the optimum processing window parameters in terms of laser power and scanning speed to fabricate Fe-Mn-Al-Ni SMA samples. Laser remelting was also employed to improve the characteristics of Fe-Mn-Al-Ni-fabricated samples. Characterization and testing techniques were carried out to assess the densification behavior of Fe-Mn-Al-Ni to study surface roughness, density, porosity, and hardness. The findings indicated that using a laser power range of 175–200 W combined with a scanning speed of 800 mm/s within the defined processing window parameters can minimize the defects with the material and lead to decreased surface roughness, lower porosity, and higher densification.

Its high-specific strength and stiffness with lower cost make discontinuous fiber-reinforced thermoplastic (FRT) materials an ideal choice for lightweight applications in the automotive industry. Compression molding is one of the preferred manufacturing processes for such materials as it offers the opportunity to maintain a longer fiber length and higher volume production. In the past, we have demonstrated that compression molding of FRT in bulk form can be simulated by treating melt flow as a continuum using the conservation of mass and momentum equations. However, the compression molding of such materials in sheet form using a similar approach does not work well. The assumption of melt flow as a continuum does not hold for such deformation processes. To address this challenge, we have developed a novel simulation approach. First, the draping of the sheet was simulated as a structural deformation using the explicit finite element approach. Next, the draped shape was compressed using fluid mechanics equations. The proposed method was verified by building a physical part and comparing the predicted fiber orientation and warpage measurements performed on the physical parts. The developed method and tools are expected to help in expediting the development of FRT parts, which will help achieve lightweight targets in the automotive industry.

The automotive industry has great interest in designing and producing lightweight high-performance components using fiber-reinforced polymers (FRPs), primarily due to their high specific strengths. Injection molding of FRP is one of the preferred processes to meet low-cost, high-volume objectives. It is imperative to account for shrinkage and warpage while designing the tools for injection molding. However, predicting shrinkage and warpage of injection-molded FRP parts remains a challenge. This is because both the structural and thermal properties depend on the condition of the fibers in the resin, that is, variation in the orientation, length, and concentration throughout the part. Additional challenges come from the fact that the material properties of polymers are a function of temperature, which varies as the parts cool. In this study, we are presenting a finite element-based semiempirical approach to address both these challenges and predict warpage due to cooling for a fiber-reinforced resin component in solid phase. The approach is demonstrated to predict warpage of an injection-molded flat plaque made of glass fiber-reinforced polypropylene, cooled from 160°C to room temperature of 23°C. First, the fiber orientation in the plaque is estimated. Next the material properties for the combined material, that is, glass and resin, are measured as a function of temperature. Then the combined material properties and calculated fiber orientations are used to estimate the ‘in-mold’ condition resin properties using reverse engineering. Finally, the warpage of the plaque is predicted using the estimated resin properties and fiber orientations. Warpage predictions using this method compare well with the measured experimental results. Our study demonstrates that valid predictions for shrinkage and warpage of injection-molded fiber-reinforced thermoplastic parts in solid phase can be made if accurate material properties are used.