Explore the vast range of titles available.

To address the challenges related to excessive deformation of the bottom plate during the opening and closing of the steam-baking integrated machine door, a comprehensive approach that incorporates finite element simulation and experimental testing was employed to investigate the plate’s deformation characteristics. Various structural parameters influencing the bottom plate were evaluated, and a series of enhancement strategies for the bottom plate structure were proposed. Following the optimization process, subsidence deformation of the bottom plate was reduced by 64%, and warpage deformation was reduced by 13.6%. Thus, a solid groundwork was established for subsequent structural stiffness analysis and optimization endeavors in the realm of embedded steam-baking integrated machines.

Additive manufacturing (AM), commonly known as 3D printing, has gained significant popularity for its ability to produce intricate parts with high precision. However, the presence of residual stresses and warpage deformation are common issues affecting the quality and functionality of 3D-printed parts. This study conducts a comprehensive finite element analysis (FEA) to investigate the material-dependent impact of key printing parameters on residual stress and warpage deformation in 3D printing. The research focuses on three distinct materials: polyetherimide (PEI), acrylonitrile butadiene styrene (ABS), and polyamide 6 (PA6). Various printing parameters are systematically varied, including printing temperature, printing speed, bed temperature, infill density, layer thickness, and infill pattern. The study employs the Taguchi L27 orthogonal array and employs the analysis of variance (ANOVA) statistical technique to assess the significance of the input parameters. The obtained results reveal that certain parameters exhibit a greater sensitivity to material differences, whereas the layer thickness parameter demonstrates a relatively lower sensitivity. Notably, infill density and printing temperature play a crucial role in reducing residual stress for PA6, while the infill pattern parameter proves to be a significant contributor to minimizing warpage deformation across all three materials. These findings underscore the importance of conducting material-specific analyses to optimize 3D printing parameters and achieve the desired quality outcomes while mitigating residual stress and warpage deformation.

When using selective laser sintering to print parts with thin-walled structures, the thermal action of the laser can cause thermal stresses that lead to plastic deformation, resulting in large warpage and dimensional deviations. To address this issue, this study proposes a bottom support method for selective laser sintering. The impact of lattice-type, concentric-type, and cross-type support structures with varying filling densities and thicknesses on the suppression of warpage and dimensional errors was investigated. The optimal process parameters for each support structure were then determined through optimization. The findings of this study demonstrated a reduction in Z-axis dimensional errors of the workpiece following the addition of supports. The reduction amounted to 33.809%, 86.160%, and 66.214%, respectively, compared to the original workpiece. Moreover, the corresponding warpage was reduced by 35.673%, 46.189%, and 46.059% for each respective case, showcasing an improvement in the printing precision. Therefore, the bottom support effectively reduces dimensional and shape errors in thin-walled parts printed by selective laser sintering. Specifically, the results obtained indicated that the concentric type of support is more effective in reducing dimensional errors and enhancing the shape accuracy of the printed workpiece. Conversely, the cross type of support demonstrated superior capabilities in minimizing the consumption of printing materials while still delivering satisfactory results. Thus, this study holds promise for contributing to the advancement of thin-walled part quality using selective laser sintering technology. This research can contribute to achieving greater accuracy in the fabrication of parts through 3D printing.

Quality control through defect minimization has been the central theme in plastic injection molding research. This study contributes to this course through the introduction of an alternative predictive modelling strategy for injection molding defects. Through multi-stage design of experiments, Computer Aided Engineering simulations, and intelligent algorithms, the study developed a warpage and shrinkage defects predictive model based on processing parameters. In the factorial design of experiment stage, the mains effect sizes, interaction effect sizes, and ANOVA were used for process parameter screening. Next, a Taguchi L25 design was used for the generation of predictive model training data. Fuzzy logic models were then developed to predict warpage and shrinkage defects based on given process parameters and the predictive capability of triangular and Gaussian membership functions was investigated. A pattern search algorithm was utilized to tune the developed predictive models. The resulting predictive model had root mean square error (RMSE) of 0.04, standard error of regression (S) of 9.6, and coefficient of determination (R 2 ) of 98.7% for shrinkage prediction. The respective model metrics for warpage prediction were 0.005, 1.2, and 96.3%. The triangular membership function model had lower RMSE indicating a higher predictive accuracy whereas the Gaussian membership function model had lower S indicating a higher model reliability. Tuning of the predictive models using a pattern search algorithm reduced the RMSE and S and increased the models’ R 2 . The approach can be adopted by plastic processing industries to predict and control such (and related) defects for quality products and maximum productivity.

The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed.

Volume shrinkage and warpage deformation are very critical quality indicators in the plastic injection molding (PIM) of box-shaped thin-walled plastics. These two performance indexes are greatly affected by the molding parameters. Therefore, in this paper, six optimization algorithms and inverse-deformation designs (IDD) are used to reduce volume shrinkage and warpage deformation. Firstly, six important molding parameters, namely filling time (A), plasticity temperature (B), mold temperature (C), holding time (D), maximum holding pressure (E) and cooling time (F), are determined, and the L 25 (5 6 ) orthogonal experimental design (OED) is established. Taguchi grey correlation (TGC) theory analysis is used to determine the optimal combination of molding parameters. Secondly, different combinations of Box-Behnken design (BBD) response surface method, BP neural network (BPNN) training, and NSGA-II genetic algorithms are used to generate four combined optimization algorithms, in order to perform multi-objective optimization of the six molding parameters. The result shows that the effectiveness of four optimization analyses are ranked as follows: BPNN-BBD-NSGA-II > BPNN-BBD > BBD-NSGA-II > BBD. The BPNN-BBD-NSGA-II method holds the best prediction results. Finally, a global optimization platform based on NX/Moldex3D is established considering the IDD theory to simulate the molding process. Optical scanning instruments are used to examine the molding quality. The result proves that the warpage deformation in box-shaped thin-walled injection-molded products is almost completely eliminated and a high molding quality can be achieved. This research is favorable for designing the molding process and guiding the molding of box-shaped thin-walled injection-molded products.

This study attempts to investigate the warpage behavior of a flip chip package-on-package (FCPoP) assembly during fabrication process. A process simulation framework that integrates thermal and mechanical finite element analysis (FEA), effective modeling and ANSYS element death-birth technique is introduced for effectively predicting the process-induced warpage. The mechanical FEA takes into account the viscoelastic behavior and cure shrinkage of the epoxy molding compound. In order to enhance the computational and modeling efficiency and retain the prediction accuracy at the same time, this study proposes a novel effective approach that combines the trace mapping method, rule of mixture and FEA to estimate the effective orthotropic elastic properties of the coreless substrate and core interposer. The study begins with experimental measurement of the temperature-dependent elastic and viscoelastic properties of the components in the assembly, followed by the prediction of the effective elastic properties of the orthotropic interposer and substrate. The predicted effective results are compared against the results of the ROM/analytical estimate and the FEA-based effective approach. Moreover, the warpages obtained from the proposed process simulation framework are validated by the in-line measurement data, and good agreement is presented. Finally, key factors that may influence process-induced warpage are examined via parametric analysis.

Purpose The purpose of this paper is to review research studies on process optimisation and machine development that lead to the enhancement of final products in various aspects of the fused deposition modelling (FDM) process. Design/methodology/approach An overview of the literature, focussing on process parameters, machine developments and material characterisations. This study investigates recent research studies that studied FDM capabilities in printing a vast range of materials from thermoplastics to metal alloys. Findings FDM is one of the most common techniques in additive manufacturing (AM) processes. Many parameters in this technology have effects on three-dimensional printed products. Therefore, it is necessary to obtain the optimum elements, for example, build orientation, layer thickness, nozzle diameter, infill pattern and bed temperature. By selecting a proper variable range of parameters, the layers adhere strongly and building end-use products of high quality are achievable. A vast range of materials and their properties from polymers to composite-based polymers are presented. Novel techniques to print metal alloys and composites are examined to increase the productivity of the FDM process. Additionally, defects such as shrinkage and warpage are discussed to eliminate the system’s limitations and improve the quality of final products. Multi-axis and mobile machines brought enhancements throughout the process to eliminate obstacles such as staircase defects in the conventional FDM process. In brief, recent developments were identified and a summary of major improvements was discussed in this study for future research. Originality/value This paper is an overview that provides information about research and developments in FDM. This review focusses on process optimisation and obstacles in printing polymers, composites, geopolymers and novel materials. Therefore, machine characteristics were examined to find out the accessibility of printing novel materials for different applications.

Warpage is one of the most challenging defects occurring in plastic injection moulded parts. Various approaches to overcome this issue have been proposed in the literature, but they all provide only partial solutions to the problem. This paper proposes a new method for the compensation and minimisation of warpage. The method is based on Mould Cavity (MC) correction. In contrast to other similar methods, here the MC correction is accomplished through a direct comparison of the local deviations of the warped part’s geometry to the desired geometry of the part. Modifying the MC shape accordingly yields parts with a lower shape discrepancy from the desired geometry compared to the nonadjusted shape. The key novelty of the paper is the development of software that iteratively adjusts the MC shape to minimise local deviations. In every iteration, the warped part is compared to the desired geometry in order to compute local deviations between both geometries. Computation is done first by determining the point normal vector of the warped geometry mesh and its piercing point through desired geometry mesh. Second, distance between the base point and the piercing point is calculated. After all local deviations are determined, the MC geometry is adjusted accordingly. Two case studies demonstrate the method’s capabilities. In the first case we present a curved thin-walled plate part. The maximum warpage value of 0.005 mm (0.7% of the initial maximum warpage) was reached after three iterations of MC geometry correction and remained stable afterwards. In the second case the method was tested on the box-shaped part. The maximum warpage dropped from initial 0.711 mm to 0.066 mm after three iterations.

The hybrid wafer bonding technique is drawing much interest in relation to three-dimensional integration technology, and its areas of application are expanding from image sensors to semiconductor memory packages. In hybrid bonding, the bond strength and void formation are the main issues influencing the performance, reliability, and yield of the bonding. In this study, we systematically investigate several parameters that affect both the bond strength and void formation, including the plasma gas, plasma power, and surface roughness. In particular, the effects of the wafer warpage on void formation were investigated. As O2 gas was used during plasma activation, the highest oxide growth rate and strongest bond strength were achieved. The bond strength improved when the oxide thickness was increased. An increase in the low-frequency plasma power improved the bond strength. However, when the plasma power increased further, the surface roughness increased due to the ion bombardment effect during the use of the plasma, resulting in a reduction in the bond strength. Therefore, optimization of the plasma power is required to improve the bond strength. It was found that the wafer warpage was also an important parameter which affected the formation of edge voids. The wafers with residual compressive stress exhibited fewer edge voids than those with tensile stress. Several methods to minimize edge void formation in wafers are proposed. The present study will provide practical guidelines to enhance the quality and yield of the bonding process and devices.