Search Results (79)

While material extrusion via fused filament fabrication (FFF) offers design flexibility and rapid prototyping, its practical use in engineering is limited by mechanical challenges, including residual stresses, geometric distortions, and potential interlayer debonding. These issues arise from the dynamic thermal profiles during FFF, including temperature gradients, non-uniform hardening, and rapid thermal cycling, which lead to uneven internal stress development depending on fabrication parameters and object topology. These problems can compromise the structural integrity and mechanical properties of FFF parts, especially when the load-bearing capacity and geometric accuracy are critical. This study focuses on polylactic acid (PLA) due to its widespread application in engineering. It introduces a computational framework for coupled thermo-mechanical simulations of the FFF process using ABAQUS (Version 2020) finite element software. A key innovation is an automated subroutine that converts G-code into a time-resolved event series for finite element activation. The simulation framework explicitly models the sequential stages of printing, cooling, and detachment, enabling prediction of adhesive loss and post-process warpage. A transient thermal model evaluates the temperature distribution during FFF, providing boundary conditions for a mechanical simulation that predicts residual stresses and warping. Uniquely, the proposed model incorporates the detachment stage, enabling a more realistic and experimentally validated prediction of warpage and residual stress release in FFF-fabricated components. Although the average deviation between predicted and measured displacements is about 10.6%, the simulation adequately reflects the spatial distribution and magnitude of warpage, confirming its practical usefulness for process optimization and design validation. Full article

In this research, the thermal and structural responses of high-performance dielectric strength polymers in the injection molding process for multi-pin electrical connectors were thoroughly studied using Moldflow simulations and optimized via a Box–Behnken experimental design under the Response Surface Methodology (RSM). Injection molding analyses were performed on Polyether-ether-ketone (PEEK), Polyetherimide (PEI), and Polyamide-imide (PAI) polymers using the MS3102A 16S-1P electrical connector model. In the conducted simulations, the melt temperature, injection time, and mold open time were evaluated as three fundamental process parameters through multivariate analysis. The volumetric shrinkage, sink mark depth, residual stress, warpage, and surface temperature homogeneity were considered as the major output qualities. According to the results, the PAI material provided superior thermal stability with an average heat removal capacity of 0.127 kW, whereas the PEI material exhibited the most homogeneous cooling behavior with a surface temperature of 45.5 °C. The minimum warpage was found to be 0.254 mm, whereas the sink mark depth was recorded within the range of 0.018–0.031 mm and the rate of volume shrinkage was between 1.03% and 1.41% in the investigations. The PAI material gave the maximum residual stress of 81.9 MPa in oriented regions of the mold. This study fills a considerable gap in the field by investigating material choice and process parameter adjustments via multivariate analysis, particularly for decision making in the production of high-reliability electrical components. Full article

During the cooling phase of injection molding, the conformal cooling channel system optimizes the uniformity of mold temperature, diminishes warping deformation, and contributes substantially to heightened product precision. The injection molding process involves complex process parameters that may result in uneven cooling between components, leading to prolonged cycle times, increased shrinkage depth, and warping deformation of the plastic parts. These manifestations negatively impact the surface quality and structural strength of the final product. This article combined theoretical algorithms with finite element simulation (CAE) methods to optimize complex injection molding processes. Firstly, the characteristics of six different types of materials were examined. Melt temperature, mold opening time, injection time, holding time, holding pressure, and mold temperature were chosen as optimization variables. Meanwhile, the warpage deformation and shrinkage depth of the formed sample were selected as optimization objectives. Secondly, an L27 orthogonal experimental design (OED) was established, and the signal-to-noise ratio was processed. The entropy weight method (EWE) was used to calculate the weights of the total warpage deformation and shrinkage depth, thereby obtaining the grey correlation degree. The influence of process parameters on quality indicators was analyzed using grey relational analysis (GRA) to calculate the range. A second-order polynomial regression model was established using response surface methodology (RSM) to investigate the effects of six factors on the warpage deformation and shrinkage depth of injection molded parts. Finally, a comprehensive comparison was made on the impact of various optimization methods and models on the forming parameters. Analyze according to different optimization principles to obtain the corresponding optimal process parameters. The research results indicate that under the principle of prioritizing warpage deformation, the effectiveness ranking of the three optimization analyses is RSM > OED > GRA. The minimum deformation rate is 0.1592 mm, which is 27.37% lower than before optimization. Under the principle of prioritizing indentation depth, the effectiveness ranking of the three optimization analyses is OED > GRA > RSM. The minimum depth of shrinkage is 0.0312 mm, which is 47.21% lower than before optimization. This discovery provides strong support for the optimal combination of process parameters suitable for production and processing. Full article

Warpage is among the most prevalent defects affecting injection molded parts. In this study, we aimed to develop methods to minimize warpage through mold design. Common strategies include matching the cavity geometry to the intended shape of the part, adjusting cavity dimensions to offset material shrinkage, and optimizing the cooling system and critical injection molding parameters. These optimization methods can offer significant improvements, but recently introduced methods that optimize the molded part and mold cavity shape result in higher levels of warpage reduction. In these methods, optimization of the shape of the molded part is achieved by shaping it in the opposite direction of warpage—a method known as inverse contouring. Inverse contouring of molded parts is a design technique in which mold cavities are intentionally modified to incorporate compensatory geometric deviations in regions anticipated to exhibit significant warpage. The final result after molded part ejection and warpage is a significant reduction in deviations between the warped and reference molded part geometries. In this study, a two-step approach for minimizing warpage was used: the first step was optimizing the most significant injection molding parameters, and the second was inverse contouring. In the first step, Response Surface Methodology (RSM) and Autodesk Moldflow Insight 2023 simulations were used to optimize molded part warpage based on three processing parameters: melt temperature, target mold temperature, and coolant temperature. For improved accuracy, a Computer-Aided Design (CAD) model of the warped molded part was exported into ZEISS Inspect 2023 software and aligned with the reference CAD geometry of the molded part. The maximal warpage value after the initial simulation was 1.85 mm based on Autodesk Moldflow Insight simulations and 1.67 mm based on ZEISS Inspect alignment. After RSM optimization, the maximal warpage was 0.73 mm. In the second step, inverse contouring was performed on the molded part, utilizing the initial injection molding simulation results to further reduce warpage. In this step, the CAD model of the redesigned, inverse-contoured molded part was imported into Moldflow Insight to conduct a second iteration of the injection molding simulation. The simulation results were exported into ZEISS Inspect software for a final analysis and comparison with the reference CAD model. The warpage values after inverse contouring were reduced within the range of ±0.30 mm, which represents a significant decrease in warpage of approximately 82%. Both steps are presented in a case study on an injection molded part made of polybutylene terephthalate (PBT) with 30% glass fiber (GF). Full article

Nowadays, Fused Filament Fabrication (FFF) 3D printing offers promising opportunities for the customized manufacturing of ankle–foot orthoses (AFOs) targeted towards rehabilitation purposes. Polypropylene (PP) represents an ideal candidate in orthotic applications due to its light weight and superior mechanical properties, offering an excellent balance between flexibility, chemical resistance, biocompatibility, and long-term durability. However, Additive Manufacturing (AM) of AFOs based on PP remains a major challenge due to its limited bed adhesion and high shrinkage, especially for making large parts such as AFOs. The primary innovation of the present study lies in the optimization of FFF 3D printing parameters for the fabrication of functional, patient-specific orthoses using PP, a material still underutilized in the AM of medical devices. Firstly, a thorough thermomechanical characterization was conducted, allowing the implementation of a (thermo-)elastic material model for the used PP filament. Thereafter, a Taguchi design of experiments (DOE) was established to study the influence of several printing parameters (extrusion temperature, printing speed, layer thickness, infill density, infill pattern, and part orientation) on the mechanical properties of 3D-printed specimens. Three-point bending tests were conducted to evaluate the strength and stiffness of the samples, while additional tensile tests were performed on the 3D-printed orthoses using a home-made innovative device to validate the optimal configurations. The results showed that the maximum flexural modulus of 3D-printed specimens was achieved when the printing speed was around 50 mm/s. The most significant parameter for mechanical performance and reduction in printing time was shown to be infill density, contributing 73.2% to maximum stress and 75.2% to Interlaminar Shear Strength (ILSS). Finally, the applicability of the finite element method (FEM) to simulate the FFF process-induced deflections, part distortion (warpage), and residual stresses in 3D-printed orthoses was investigated using a numerical simulation tool (Digimat-AM^®). The combination of Taguchi DOE with Digimat-AM for polypropylene AFOs highlighted that the 90° orientation appeared to be the most suitable configuration, as it minimizes deformation and von Mises stress, ensuring improved quality and robustness of the printed orthoses. The findings from this study contribute by providing a reliable method for printing PP parts with improved mechanical performance, thereby opening new opportunities for its use in medical-grade additive manufacturing. Full article

Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage. Full article

High-Bandwidth Memory (HBM) enables the bandwidth required by modern AI and high-performance computing, yet its three dimensional stack traps heat and amplifies thermo mechanical stress. We first review how conventional solutions such as heat spreaders, microchannels, high density Through-Silicon Vias (TSVs), and Mass Reflow Molded Underfill (MR MUF) underfills lower but do not eliminate the internal thermal resistance that rises sharply beyond 12layer stacks. We then synthesize recent hybrid bonding studies, showing that an optimized Cu pad density, interface characteristic, and mechanical treatments can cut junction-to-junction thermal resistance by between 22.8% and 47%, raise vertical thermal conductivity by up to three times, and shrink the stack height by more than 15%. A meta-analysis identifies design thresholds such as at least 20% Cu coverage that balances heat flow, interfacial stress, and reliability. The review next traces the chain from Coefficient of Thermal Expansion (CTE) mismatch to Cu protrusion, delamination, and warpage and classifies mitigation strategies into (i) material selection including SiCN dielectrics, nano twinned Cu, and polymer composites, (ii) process technologies such as sub-200 °C plasma-activated bonding and Chemical Mechanical Polishing (CMP) anneal co-optimization, and (iii) the structural design, including staggered stack and filleted corners. Integrating these levers suppresses stress hotspots and extends fatigue life in more than 16layer stacks. Finally, we outline a research roadmap combining a multiscale simulation with high layer prototyping to co-optimize thermal, mechanical, and electrical metrics for next-generation 20-layer HBM. Full article

For automotive GFRP structural components, beyond structural design, the warpage, residual stress/strain, and fiber orientation inevitably induced during the injection molding process significantly compromise their service performance. These factors also diminish the reliability of performance assessments. Thus, it is imperative to develop a process–structure co-optimization approach for GFRP components. In this paper, the performance of a front-end module is evaluated through topological structure design, injection molding process optimization, and simulation with mapped injection molding history, followed by experimental validation and analysis. Under ±1000 N loading, the initial design shows excessive displacement at the latch mounting points (2.254 mm vs. <2.0 mm limit), which is reduced to 1.609 mm after topology optimization. By employing a sequential valve control system, the controls of the melt line and fiber orientation are is superior to thatose of conventional gating systems. The optimal process parameter combination is determined through orthogonal experiments, reducing the warpage to 1.498 mm with a 41.5% reduction compared to the average warpage of the orthogonal tests. The simulation results incorporating injection molding data mapping (fiber orientation, residual stress–strain) show closer agreement with experimental measurements. When the measured displacement exceeded 0.65 mm, the average relative error $E_r$, range $R$, and variance $s^2$ between the experimental results and mapped simulations were 11.78%, 14%, and 0.002462, respectively, validating the engineering applicability of this method. The methodology and workflow can provide methodological support for the design and performance assessment of GFRP automotive body structures, which enhances structural rigidity, improves control over injection molding process defects, and elevates the reliability of performance evaluation. Full article

Wire Arc Additive Manufacturing (WAAM), a specific type of Directed Energy Deposition (DED) additive manufacturing, has recently gained widespread attention for manufacturing industrial components. WAAM has many advantages compared to other metal AM processes such as powder bed fusion. It is not only cost-efficient and easily accessible, but also capable of manufacturing large-scale industrial components in a short period of time. However, due to the inherent layered nature of the process and significant heat accumulation, parts can experience severe warping, often leading to part rejection. Predicting these anomalies prior to manufacturing would allow for process parameter adjustments to reduce or eliminate residual stresses and large deformations. In this study, we develop a simulation-based model capable of accurately predicting final deformations and unintended warpages. A Johnson–Cook plasticity model with isotropic hardening is implemented through a UMAT user subroutine in Abaqus. The proposed model is then utilized to predict the residual stresses and deformations in WAAM-fabricated parts. Simple wall geometries with 4, 8, and 20 layers deposited on build plates of varying thicknesses, are tested to assess the performance of the model. Combined Johnson–Cook plasticity and isotropic hardening for the WAAM process were implemented for the first time in this study, and the model was validated against experimental data, showing a maximum deviation of 4%. Thermal analysis of a four-layer-high wall took 12 min, while structural analysis using the proposed model took 1 h and 40 min. In comparison, thermo-mechanical analysis of the same geometry reported in the literature takes 14 h. The results demonstrate that the proposed model is not only highly accurate in predicting warpage but also significantly faster than other methodologies reported in the literature. Full article

Fused filament fabrication (FFF) is a 3D printing technology that has been successfully demonstrated aboard the International Space Station (ISS), proving its suitability for space applications. In this study, we aimed to apply FFF to the 3D printing of recycled space beverage packaging, made of LDPE and a PET-Aluminum-LDPE (PAL) trilaminate. To minimize material waste and optimize the experimental process, we first conducted numerical simulations of additive manufacturing. Using Digimat-AM 2021.1 software, we analyzed residual stresses and warpage in an LDPE/PAL composite with a 10 wt% filler content, processed through the FFF technique. Three key printing parameters, including printing speed and infill pattern, were varied across different levels to assess their impact. Once the optimal combination of parameters for minimizing residual stresses and warpage was identified, we proceeded with the experimental phase, printing objects of increasing complexity to validate the correlation between numerical predictions and the 3D-printed models. The successful fabrication of all geometries under optimized conditions confirmed the numerical predictions, particularly the reduction in warpage and residual stress, validating the material’s viability for additive manufacturing. These findings support the potential application of the LDPE/PAL composite for in situ resource utilization strategies in long-term space missions. Full article

During the temper rolling process, scratch defects on the lower surface of the steel strip caused by the tension measuring roller are a critical issue that affects the resulting product quality. Previous research has been limited to analyzing the mechanism and influencing factors of scratch generation, and has lacked a systematic design solution for analyzing and addressing scratch defects. This study deeply analyzed the mechanism of scratch formation, and it was determined that the fundamental cause lies in the relative sliding between the steel strip and the tension measuring roller. Specifically, the driving torque of the steel strip on the tension measuring roller is insufficient to overcome its total rotational resistance torque. This study identified the strip wrap angle, roller sleeve outer diameter, and wall thickness as key structural parameters that influence this torque balance. In optimizing the structural parameters of the tension measuring roller, this research had two main goals: eliminating scratch defects and avoiding strip warpage defects. To achieve this, a strip warpage calculation model and a scratch verification model were established. These were combined with structural connection safety verification. Based on these analyses, the optimal structural parameter combination was determined. This combination includes a roller sleeve outer diameter of 500 mm, a roller sleeve wall thickness of 20 mm, and a wrap angle of 30°. The consistent results that were obtained from theoretical analysis, finite element simulation, and actual production trials demonstrate that the optimized structural parameters of the tension measuring roller can effectively prevent relative sliding between the steel strip and the tension measuring roller, completely eliminating scratch defects on the lower surface of the steel strip and significantly improving product quality. This provides a reliable solution for the scratch problem in temper rolling. This solution also reduces scrap rate and production costs. Furthermore, it meets the strict surface quality demands of high-end applications. This leads to improved production efficiency and customer satisfaction. Ultimately, this method enhances product competitiveness and promotes industry technological progress. Full article

The development of chip manufacturing and advanced packaging technologies has significantly changed redistribution layers (RDLs), leading to shrinking line width/spacing, increasing the number of build-up layers and package size, and introducing organic materials such as polyimide (PI) for dielectrics. The fineness and complexity of structures, combined with the temperature-dependent and viscoelastic properties of organic materials, make it increasingly difficult to predict the thermo-mechanical behavior of wafer-level Cu-PI RDL structures, posing a severe challenge in warpage prediction. This study models and simulates the thermo-mechanical response during the manufacturing process of Cu-PI RDL at the wafer level. A cross-scale wafer-level equivalent model was constructed using a two-level partitioning method, while the PI material properties were extracted via inverse fitting based on thermal warpage measurements. The warpage prediction results were compared against experimental data using the maximum warpage as the indicator to validate the extracted PI properties, yielding errors under less than 10% at typical process temperatures. The contribution of RDL build-up, wafer backgrinding, chemical mechanical polishing (CMP), and through-silicon via (TSV)/through-glass via (TGV) interposers to the warpage was also analyzed through simulation, providing insight for process risk evaluation. Finally, an artificial neural network was developed to correlate the copper ratios of four RDLs with the wafer warpages for a specific process scenario, demonstrating the potential for wafer-level warpage control through copper ratio regulation in RDLs. Full article

For the manufacturing of thin-walled connectors, warpage represents an inherent challenge in injection molding, significantly affecting dimensional accuracy and shape consistency. This study introduces an optimization methodology that combines Latin Hypercube Sampling (LHS), numerical simulation, a DBO-BP neural network prediction model, and integrated multi-objective optimization algorithms (NSGA-II). Initially, LHS is employed to select experimental sample points, followed by numerical simulations to evaluate the influence of process parameters on the response variables. Based on the simulation outcomes and response data, a DBO-BP neural network prediction model is developed to enhance the precision of multi-objective optimization. Subsequently, the NSGA-II algorithm is utilized for multi-objective optimization to analyze the effects of various process parameter combinations on warpage, shrinkage, and clamping force, ultimately identifying the optimal Pareto front solutions. The optimization results demonstrate that the model’s prediction accuracy for warpage and volume shrinkage is within 5%. The clamping force remains relatively high, with the optimal values for warpage, volume shrinkage rate, and clamping force being 0.173 mm, 7.5%, and 15.83 tons, respectively. This approach facilitates the optimization of injection molding process parameters while ensuring the quality of thin-walled connectors, thereby improving production efficiency and minimizing defects. Full article

The use of conformal cooling channels (CCC) in the injection molding process has revolutionized the polymer industry by enhancing part cooling uniformity and improving cooling efficiency, minimizing undesired defects such as warpage and shrinkage inherent to the process. This review paper provides a detailed investigation of the literature on CCC, with special focus on how computational fluid dynamics (CFD) has been employed to analyze and optimize the thermal performance of the cooling system. Additionally, key aspects of CCC design, including geometry optimization, surface roughness, and flow dynamics, are evaluated to improve cooling efficiency, reduce cycle time, and enhance product quality. Several CFD-based studies are reviewed to highlight commonly used simulation methods and CCC optimization approaches for heat transfer enhancement. Particular attention is given to how simulation tools contribute to design improvement and decision-making, addressing practical constraints related to thermal behavior and manufacturability. Key performance parameters such as pressure drop, temperature uniformity, cooling time, and manufacturing limitations are examined and compared, offering a foundation for future directions to advance CCC design and CFD analysis to optimize injection molding. Aiming at contributing to the academia and the industry, the novelty of this review paper lies in its integrative perspective, providing a comprehensive analysis of coupling designing tasks with CFD simulations. As a result, this paper serves as a valuable resource for researchers and industry professionals aiming to leverage CFD for the development of high-performance, energy-efficient CCC. Full article

Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objective optimization method for injection molding process parameters based on the BP neural network and NSGA-II algorithm is proposed to address the problem of product quality defects caused by unreasonable process parameter settings. Taking the junction box shell as the object, numerical simulation was carried out using Moldflow2019 software and a six-factor five-level orthogonal experiment was designed to explore the influence of injection molding process parameters, such as the mold temperature, melt temperature, injection pressure, holding pressure, holding time, and cooling time, on the volume shrinkage rate and warpage deformation of the junction box. Based on a numerical simulation, the BP neural network and NSGA-II algorithm were used to optimize the optimal combination of injection molding process parameters, volume shrinkage rate, and warpage deformation. The research results indicate that the melt temperature has the most significant impact on the quality of the injection molding of junction boxes, followed by the holding time, holding pressure, cooling time, injection pressure, and mold temperature. After optimization using the BP neural network and the NSGA-II algorithm, the optimal process parameter combination was obtained with a melt temperature of 230.03 °C, a mold temperature of 51.27 °C, an injection pressure of 49.13 MPa, a holding pressure of 69.01 MPa, a holding time of 15.48 s, and a cooling time of 34.91 s. At this time, the volume shrinkage rate and warpage deformation of the junction box were 6.905% and 0.991 mm, respectively, which decreased by 33.2% and 3.8% compared to the average volume shrinkage rate (10.34884%) and warpage deformation (1.030764 mm) before optimization. The optimization effect was significant. In addition, the errors between the volume shrinkage rate and warpage deformation predicted by BP-NSGA-II and the simulated values using Moldflow software were 1.9% and 3.4%, respectively, indicating that the optimization method based on the BP neural network model and NSGA-II algorithm is reliable. Full article