The smoothing ball-burnishing process has commonly been used as a post-processing method to reduce the irregularities of machined surfaces. However, the mechanism of this process has rarely been examined. In this study, a simulation procedure is proposed to predict the surface roughness of a burnished workpiece under varying burnishing forces. The roughness of the workpiece surface was firstly approximated by parabolic functions. The burnishing process was then numerically simulated through two steps, namely the elastic–plastic indentation of the burnishing ball on the workpiece’s surface, and the sliding movement of the burnishing tool. The results of the simulation were verified by conducting small ball-burnishing experiments on oxygen-free copper (OFC) and Polmax materials using a load cell-embedded small ball-burnishing tool. For the OFC material, the optimal burnishing force was 3 N. The obtained experimental surface roughness was 0.18 μm, and the simulated roughness value was 0.14 μm. For the Polmax material, when the burnishing force was set at its optimal value—12 N, the best experimental and simulated surface roughness were 0.12 μm and 0.10 μm, respectively.

Injection molding is a widely used polymer manufacturing process, and integrating sustainability is essential. As material testing demand increases, efficient methods for producing multiple samples are crucial. Family molds enable multiple sample production in a single cycle but pose challenges in achieving balanced flow, leading to sink marks and shrinkage that affect test accuracy. This study addresses these issues by analyzing sink marks, shrinkage, and their variance among specimens. A Grey-based Taguchi method systematically evaluates the effects of injection speed, melt temperature, injection pressure, holding pressure, holding time, and cooling time. Optimal conditions were determined as 40 mm/s injection speed, 170°C melt temperature, 110 MPa injection pressure, 35 MPa holding pressure, and a 7-s holding time. ANOVA results indicate that melt temperature, holding time, and holding pressure significantly influence sink marks and shrinkage. Under optimal conditions, the GRD value improved from 0.638 to 0.643. Furthermore, material testing confirmed the reliability of the optimized specimens, with tensile and flexural strengths within a 5% error margin of datasheet values. These findings validate the effectiveness of the proposed method in enhancing sample quality and consistency in family mold injection molding.

Springback is a significant challenge that affects forming accuracy in the sheet metal industry. This phenomenon occurs because elastic materials tend to revert to their original shape after the removal of deforming forces, leading to differences between the desired and final shapes of the metal. Currently, two primary methods are employed to investigate springback: physical experiments and numerical simulations. Physical experiments involve actual deformation of sheet metals to observe and measure springback. While these experiments can provide useful data and insights, they are often time-consuming and expensive, making them less possible for extensive studies or frequent iterations. On the other hand, numerical simulations offer a virtual modeling platform that can predict and analyze springback without the need for physical trials. These simulations enable engineers to refine the metal forming process for better end-product accuracy. Building on these existing methodologies, this research uses ANSYS software to conduct Finite Element Analysis (FEA) and reconstruct simulation data for V-bending technique. This study first compares the reconstructed data with data available from literature to evaluate the accuracy and reliability of our simulations. Subsequently, the validated numerical technique is applied to the V-bending process with groove, which primarily aims to reduce bending radius. The influence of adding grooves in the V-bending process was examined to assess its effects on bending radius and springback angle. Numerical results from V-bending simulations with grooves indicate promising control over springback, thereby enhancing quality of final product. The effects of sheet thickness and groove depth were also analyzed, revealing that greater groove depth increases the springback angle and reduces the initial bending radius. Furthermore, increased sheet thickness was observed to slightly increase the radius after springback, emphasizing the role of thickness in shaping bending behavior in V-groove processes.

This study presents a machine learning-driven framework for the constrained multi-criteria optimization of family-mold processes, where the simultaneous production of geometrically dissimilar parts creates complex flow imbalances. Dimensional deformation (warpage) and shear stress are identified as critical quality characteristics affecting the functional performance and structural integrity of molded products, while product weight is treated as a practical manufacturing constraint to ensure material efficiency. Preliminary experimental trials are conducted to verify process feasibility and to determine realistic operating ranges for key injection molding parameters. Based on these ranges, a face-centered central composite design (FCCCD) is constructed, and high-fidelity computational fluid dynamics (CFD) data are generated using Moldex3D analysis. A Kriging-based surrogate model is then developed to represent the complex and nonlinear relationships between process parameters and quality responses. The surrogate model is integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform constrained multi-criteria optimization, where warpage and shear stress are simultaneously minimized under a prescribed weight constraint. The optimization process yields Pareto-optimal process parameter combinations that effectively balance dimensional accuracy and stress reduction while satisfying material usage requirements. Selected Pareto-optimal solutions are validated through detailed CFD simulations, yielding average relative errors of 1.56 % and 3.58 % for warpage and shear stress, respectively, demonstrating the predictive accuracy and robustness of the proposed framework. Overall, this study establishes an effective data-driven optimization strategy for quality-oriented design and decision support in intelligent family-mold processes.