Key Engineering Materials Vol. 1016

Abstract: Metal additive manufacturing encompasses multiple techniques, among which Selective Laser Melting (SLM) is extensively employed for fabricating highly complex, precise, and uniquely shaped metal parts. However, obtaining accurate product characteristics often requires complex experimentation, which can potentially damage the products. Thus, there is a need to develop an automated method for predicting product characteristics. To forecast these attributes, details related to metal additive manufacturing products were documented, including process parameters and textural features. These features were extracted from product’s longitudinal sectional images and layer-by-layer images, using the gray-level co-occurrence matrix (GLCM). Subsequently, machine learning (ML) models such as Support Vector Regression (SVR), XGBoost, and LightGBM were employed to predict product properties and compare their performance. The experimental results indicated stronger correlations between process parameters and textural features in longitudinal section images compared to layer-by-layer ones. Moreover, the models demonstrated high predictive accuracy, particularly XGBoost and LightGBM, with R² score approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Furthermore, this method shows potential for accurately predicting a variety of product properties, fulfilling the needs of multiple application scenarios.

Abstract: Plastic water tanks made from FDA-approved Polyethylene (PE) are widely used due to their advantageous qualities, including being free of heavy metals, UV-stable, odorless, durable, and suitable for food-grade applications. A rotational molding process creates a smooth inner surface without corners, reducing dirt adhesion. However, a case study revealed pore defects on the outer surface of the tanks, compromising UV stability and customer satisfaction. This study aims to address these pore defects by establishing new standards and improving both internal processes and external factors affecting mold heating. Utilizing Six Sigma methodology, the study employs data analysis and design of experiments (DOE) to identify and regulate critical parameters affecting the manufacturing process. The Six Sigma approach, which encompasses the Define, Measure, Analyze, Improve, and Control (DMAIC) phases, was applied to enhance process capability, minimize variations and machine errors, reduce waste, and increase customer satisfaction. By effectively addressing the root causes of porosity defects, the study aims to improve processes and enhance product quality.

Abstract: One of the critical components in the rehabilitation of lower limb amputees is the prosthetic foot. The solid ankle cushion heel (SACH) foot is commonly prescribed to patients due to its ability to reduce impact loading at heel strike, as well as its durability and cost-effectiveness. This research focuses on developing a composite SACH foot using two different polymers. The keel was constructed from Nylon fiber, while the shell, designed to resemble an amputee's foot, was made from Polyurethane (PU) foam. The keel functions as a surrogate for the amputee's bone and is therefore specifically designed and embedded within the shell. The developed SACH foot underwent static testing according to the ISO 10328:2016 standard at various angles. Additionally, it was modeled and analyzed using the finite element method (FEM). Material tests were conducted on both the keel and shell to establish their material models for FEM. The finite element analysis (FEA) demonstrated an average error of less than 15.22%. Moreover, the FEA provided insights into the deformation and stress experienced by both the keel and the shell. This detailed investigation into the structural behavior of both SACH components offers valuable guidance for the future design and development of composite SACH feet.

Abstract: The damping property is a material's energy dissipation capacity, indicating its ability to resist vibrations. The parameters of damping characteristics can be evaluated using the traditional Fast Fourier Transformation (FFT) technique, which suffers from the loss of time. Therefore, Hilbert Transform (HT) and Wavelet Transform (WT) have been developed to overcome such problems and help comprehend damping properties precisely with time and frequency. This study evaluates and compares damping ratio assessment using HT, WT, and Log Decrement in linear and non-linear viscoelastic material models. To test the adapted HT and WT methods, we developed a homemade MATLAB code to evaluate the damping ratio of two data sets. Analytical data obtained from solving a linear viscoelastic material model and numerical data attained from the FE-model of a non-linear viscoelastic material were both subjected to vibration. The error percentages of the damping ratio estimated by HT and WT were 6.1 and 11.75, respectively, compared to 43 for Log Decrement. These results confirm that HT and WT can accurately predict the damping ratio of non-linear viscoelastic material models.