Warpage Optimization Research Articles

Methodology of a thermal injection mold design with locally applied heating coatings on the cavity wall

AbstractThe thermal balance of injection molds is defined by the cooling channel layout. However, the fluid tempering of injection molds underlies certain restrictions concerning the thermal dynamic and the local limitation of the zone of influence. The optimal heat balance within the mold to minimize warpage cannot be solely achieved with conformal cooling channels. Ceramic heating coating systems applied by Thermal Spraying on the cavity wall are a novel tempering method for high heating rates and offer the possibility of a more local heat input. In terms of the thermal mold design, the position and size of local heating coating systems need to be determined. A methodology is proposed, which connects the optimization of part warpage by defining the necessary local heat flux within the mold with the determination of the heating coating placement. Due to the dependence of the coating placement on the heating time and the power, different decision criteria of these parameters are discussed with regard to the process efficiency. The developed methodology can calculate the necessary heating power distribution to homogenize the surface temperature and freeze time. A second step discretizes the heating power distribution to certain manufacturable heating zones.

Research on 3D Printing Accuracy Enhancement Techniques for Styling Design in the Intersection of Art and Engineering

Abstract 3D printing technology has subverted the traditional process manufacturing process. The main algorithms used in the model data processing is a key step that affects the accuracy of the model printing. This paper focuses on the 3D printing path planning algorithm. Analyze the realistic requirements for the coexistence of artistry and craftsmanship in 3D printing products and develop a design scheme. Visualize the 3D printing path planning suggested for contour information in the partition linear scanning corner filling algorithm. Set the 45° scanning direction of the X-axis, adjust the fitted spline curve function reciprocally, and even out the path of the printer in the corner of the partitioned scanning path. Improve printing accuracy. Combined with the experimental equipment to set up the 3D printer’s operating code, the proposed Hilbert filling curve method, combined with three groups of comparison tests, verified the accuracy improvement benefits of the composite scanning algorithm in this paper. Taking the experimental data of the uncovered box group as an example, the composite scanning algorithm of this paper changes the maximum warpage from 0.23mm to 0.16mm, which is an improvement of 30.43%, and the average warpage from 0.1675mm to 0.095mm, which is an improvement of 43.28%. The reduction and optimization of warpage not only shows the improvement of accuracy of 3D printing technology but also optimizes the shape of 3D products.

An effective solution to optimize the saddle-shape warpage in 3D IC applications by patterning laser treatment

The asymmetric saddle-shape warpage is one of the hardest limitations on the development of three-dimensional integrated circuits (3D ICs) as the vertical integration increases. To improve the product yield, an effective method for saddle-shape warpage reduction in 3D IC production by backside patterning laser annealing treatment with a multilayer structure is investigated in this work. The reduction of saddle-shaped warpage by patterning laser treatment is the first time in 3D NAND array wafer application and through simulations and experiments, the laser annealing pattern is determined. In addition, the materials of the multilayer structure are selected as amorphous Al2O3 and TiN according to experiments and theoretical analysis. As a result, a conformal 47.8 µm optimization of the saddle-shape warpage is successfully reached in a control wafer test by patterning laser annealing treatment.

Warpage Optimisation Using Recycled Polycar-bonates (PC) on Front Panel Housing.

Many studies have been done using recycled waste materials to minimise environmental problems. It is a great opportunity to explore mechanical recycling and the use of recycled and virgin blend as a material to produce new products with minimum defects. In this study, appropriate processing parameters were considered to mould the front panel housing part using R0% (virgin), R30% (30% virgin: 70% recycled), R40% (40% virgin: 60% recycled) and R50% (50% virgin: 50% recycled) of Polycarbonate (PC). The manufacturing ability and quality during preliminary stage can be predicted through simulation analysis using Autodesk Moldflow Insight 2012 software. The recommended processing parameters and values of warpage in x and y directions can also be obtained using this software. No value of warpage was obtained from simulation studies for x direction on the front panel housing. Therefore, this study only focused on reducing the warpage in the y direction. Response Surface Methodology (RSM) and Genetic Algorithm (GA) optimisation methods were used to find the optimal processing parameters. As the results, the optimal ratio of recycled PC material was found to be R30%, followed by R40% and R50% materials using RSM and GA methods as compared to the average value of warpage on the moulded part using R0%. The most influential processing parameter that contributed to warpage defect was packing pressure for all materials used in this study.

Optimization and prediction of volume shrinkage and warpage of injection-molded thin-walled parts based on neural network

Warpage and volume shrinkage are important indicators of the quality of thin-walled parts during injection molding. In this study, the optimization goals are warpage and volume shrinkage. Design parameters include mold temperature, melt temperature, injection time, holding time, cooling time, and holding pressure. Based on the orthogonal experimental design and response surface experimental design, the MoldFlow software is applied to the simulation of the thin-walled part injection molding process. The importance of various parameters on warpage and volume shrinkage was analyzed by using analysis of variance. Based on the simulation results, a two-layer hidden-layer back propagation (BP) neural network model is established and the genetic algorithm (GA) is used to optimize the weights and thresholds of the back propagation neural network (BPNN) model to reduce warpage and volume shrinkage by optimizing the design parameters significantly. A support vector machine (SVM) combined with GA-BP was used to build a prediction model for predicting warpage and volume shrinkage. Taking the automobile wire harness protection frame as an example, and verified by numerical simulation, the GA-optimized two-layer hidden-layer BP neural network combination method is an effective method for injection molding to reduce warpage and volume shrinkage of thin-walled parts. SVM-BP-GA can accurately provide predictions for optimization goals; the amount of warpage and the volume shrinkage were 0.93% and 1.9%, respectively.

Simulation and optimization for a plastic component

This paper presents the simulation of injection moulding process of the subject component and noting the deflection results using finite element software. Subsequently some changes were made to the tool and part design of the subject component and the modifications which gave the minimum deflection were incorporated and used for further optimization of warpage using design of experiments.

Study of Warpage Evolution and Control for Six-Side Molded WLCSP in Different Packaging Processes

A six-side molded wafer-level chip scale package (mWLCSP) is a novel and attractive packaging method for smart phone applications due to its lower cost, better electrical performance, and higher long-term reliability. Nevertheless, wafer warpage in different manufacturing processes is a significant issue for the six-side mWLCSP. This article focuses on wafer warpage evolution in different packaging processes and the development of control method for the six-side mWLCSP. A quarter wafer-level finite-element (FE) model and a strip-level FE model were established to simulate the actual packaging processes by element birth and death method and restart technology. Both the FE models were proved quite efficient and accurate for warpage prediction compared to the experimental results. The effects of geometric parameters and material parameters on the maximum warpage value were studied based on the two established FE warpage models, and some recommendations were given for warpage optimization. Based on the above studies, an improved manufacturing process flow for the six-side mWLCSP was presented and proved feasible to control the maximum warpage value under 3 mm. It offers an insight work for the development and warpage control of the six-side mWLCSP and other wafer-level molded packages.

Warpage optimization and influence factors analysis of 3D printing personalized JJY tablets

To explore the feasibility of preparing traditional Chinese medicine using 3 D printing technology and reduce warpage commonly occurs in large-size tablets, we investigated the prescription, warpage optimization and influence factors of 3 D printing Jiuxiang Jianpi Yangwei (JJY) tablets. The procedures used conformed to the requirements of the 2015 edition of the Chinese Pharmacopeia. The results of the prescription screening showed that 75% ethanol and HPMC (9%) could be adhesives. Meanwhile, stevia (0.5%) and citric acid (0.5%) improved the taste of the 3 D printed JJY tablets. To ensure the quality and appearance of the printed tablets, the best parameters were as follows: drying at room temperature, 40% of the filling density, a 3 mm model height, two outer ring numbers and a printing speed of 15 mm/s. The optimized printed tablets had a smooth appearance, suitable hardness, with the weight uniformity in accordance with the Pharmacopeia. We also prepared personalized JJY cartoon tablets (which contained images of a big-headed pig and a small yellow duck) which were designed to increase the compliance of children when taking their medications. In conclusion, this study reported that 3 D printing technology has great potential for preparing traditional Chinese medicines, and it provided guidance for 3 D printing tablets without warpage.

Simulation and Optimization of Warpage of Fiber Reinforced using Human Behavior Based Optimization

Warpage is one of the major defects in injection molding and this affects the quality of the materials. Some techniques are used to minimize the warpage by the changing the parameter settings. The optimization techniques were applied to the parameter to find the optimized value. The popular method in optimizing the parameter is Genetic Algorithm (GA) and this has the limitation of big stochastic components. The main objective of this research is to propose the Human Behavior Based Optimization (HBBO) in the warpage. This method doesn’t have large stochastic and has a fast convergence rate. The orthogonal Array is used to measure the warpage for the different parameter settings. The fiber reinforced component is used to measure the performance of the proposed method. The Back Propagation Neural Network is used to find the relationship between the warpage and other factors. Then optimization technique is applied to find the parameter value. The experimental result of the proposed HBBO method in Warpage optimization is compared with other existing method in warpage optimization. The HBBO method has the warpage of 0.0858 and the GA method has the warpage of 0.0953.

Warpage optimisation using virgin and recycled polycarbonates: a case study of front panel housing

Plastic product are commonly produced using an injection molding process. A large amount of product can produce within a very short time. One of the defect that often being encountered in the production is warpage. By determine the appropriate parameter setting by using optimisation method, the quality of the moulded part can be improve. The warpage can be minimise but difficult to eliminate. The part can be smaller than the actual size cause by the warpage. Due to this defect will cause the plastic part difficult to be assembled. Considering the increasing application of these thermoplastic materials, the use of recycled polymers has increased too. Environmental issues, the subsequent tendency to save resources and the high costs of polymers have made the recycling an inevitable option. This study provides a scientific knowledge that will be useful to plastic manufacturing industries in injection moulding process especially by using virgin and recycle material. The parameter that being significant from the studied that been review were mostly melt temperature, packing time, cooling time and packing pressure.

Warpage Optimisation On The Front Panel Housing Moulded By Acrylonitrile Butadiene Styrene (ABS) (Virgin and Recycle) Material: A Review

Injection molding process is well known in plastic molding industries. The development of this process had causes the materials used to mold plastic product to increase. However, this downside of this large amount of plastic consumption also leads to the fact that plastic waste fills the landfill. This cause researches on recycling plastic to increase. Even though injection molding process has many advantages, but the occurrence of defects on plastic part molded is one of the most important issues to be resolved. Warpage defect is one of the defect that normally occurred on molded part. This paper presents a review of the current research on the outlook of injection molding process and the effect of parameter processing on plastic molded part. The effect of parameter processing on mechanical properties of plastic mold parts with a mixture of virgin and recycled material has been numerously studied. However, studies on the physical appearance and quality of plastic mold parts by a mixture of virgin and recycled material have yet to be explored.

Experimental Verification and Optimization Analysis of Warpage for Panel-Level Fan-Out Package

Nowadays, fan-out package is regarded as one of the latest and most potential technologies because it possesses lower cost, thinner profile, and better electrical performance and thermal performance. However, thermally induced warpage in the molding process is a critical issue due to the larger wafer or panel size, the shrinkage of epoxy mold compound (EMC) during the curing stage, and the mismatch of coefficient of thermal expansion (CTE) among the constituent materials during the cooling stage, which needs to be controlled effectively for successful subsequent process of the fan-out package. In this paper, a novel 320 × 320-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> panel-level fan-out package based on “Die Last” process is developed. A coreless substrate with redistribution layer is fabricated and bonded onto a low-CTE and high-glass-transition-temperature (Tg) FR4 carrier through thermal release film. The thermally induced warpage issue in the molding process is investigated. A warpage simulation method is presented and verified by Shadow Moiré experiment. The error between the simulation and experimental results is about 4.8%. For the warpage optimization analysis, the effect of geometry structure on the warpage is first investigated by the design of simulation approach. Full factor experiment is conducted, and Minitab statistical software is utilized to analyze the effect of the geometry structure on warpage. It is found that decreasing die thickness and molding thicker EMC can effectively decrease the warpage. Then, the effects of molding temperature and in-plane CTE of FR4 on warpage are studied, respectively. When molding temperature is 120 °C and inplane CTE of FR4 decreases to 10.5 ppm/°C, the thermally induced warpage in the molding process is only about 0.31 mm, thus subsequent process of fan-out package can be conducted successfully.

Shrinkage and Warpage Optimization of Expanded‐Perlite‐Filled Polypropylene Composites in Extrusion‐Based Additive Manufacturing

A major challenge in extrusion‐based additive manufacturing is the lack of commercially available materials compared to those in well‐established processes like injection molding or extrusion. This study aims at expanding the material database by evaluating the feasibility of polypropylene, which is one of the most common and technologically relevant semicrystalline polymers. Expanded‐perlite‐filled polypropylene and ternary blends with amorphous polyolefins are evaluated to establish an understanding of their processability and their printability. A detailed study on the shrinkage behavior, as well as on the thermal, mechanical, morphological, and warpage properties is performed. It is found that smaller sized fillers result in a tremendous warpage and shrinkage reduction and concurrently improved mechanical properties than compounds filled with bigger sized fillers. Based on the optimal properties profile, a ternary blend that can overcome the shrinkage and warpage of printed parts is suggested.

Numerical Optimization of Shrinkage and Warpage on the Injection Molding Process Parameters of Electrical Connector

Integrated with orthogonal design method and numerical simulation, injection molding process of the Y-type electrical connectors was conducted to study the influence of process parameters on volume shrinkage rate and maximum warpage, which are regarded as product quality indices. The multi-indices valuation model for the main influencing factors of the process is developed. The influencing sensitivity to the multi-objective of the processing parameters, such as melt temperature, mold temperature, injection time and holding pressure, is determined by range analysis. Through analyzing the diagrams of influential factors, the optimized process parameter diagram is obtained and verified by simulation. The optimum parameters minimizing the warpage defect and shrinkage are: melt temperature (528K), mold temperature (338K), filling time (0.6s), holding pressure (100%) and holding time (10s). The results show that it is effective to balance the impact of process parameters on the shrinkage and warpage. The work can provide optimal design and process reference for the quality control and assembly precision.

Pressure Analysis of Dynamic Injection Molding and Process Parameter Optimization for Reducing Warpage of Injection Molded Products.

Plastic injection molding technology is one of the important technologies for the manufacturing industry. In this paper, a numerical dynamic injection molding technology (DIMT) is presented, which is based on the finite element (FE) method. This numerical simulation method introduces a vibrational force into the conventional injection molding technology (CIMT). Some meaningful work has been executed for investigating the mechanical evolution behavior of DIMT. As the basis for illustrating the mechanism in warpage optimization results, dynamic parameter analysis on the rule of pressure response is performed in detail. In the warpage optimization work, three kinds of structure with different molding materials are selected as the comparison. The final warpage of each product is efficiently minimized by using a Gaussian process-based sequential optimization method. From the further discussions, the features of DIMT in improving the molding quality are revealed, indicating that it may not be appropriate for geometrically large structures. This study has significant meaning for the actual injection molding industry.

Warpage optimization of fiber-reinforced composite injection molding by combining back propagation neural network and genetic algorithm

Warpage is an important index in measuring part quality in the short fiber-reinforced composite injection molding process. In addition to process parameters, fiber parameters have an important influence on part warpage. In this study, the objective function is a minimum problem of the warpage. The design parameters include fiber aspect ratio, fiber content, injection time, melt temperature, mold temperature, and holding pressure. The combined back propagation neural network (BPNN) and genetic algorithm (GA) approach is proposed to optimize the fiber-reinforced composite injection molding process. On the basis of orthogonal experiment design, Moldflow software is applied in the short fiber-reinforced composite injection molding process. The importance of various parameters on the influence of warpage is researched by the range analysis method. Thereafter, a back propagation neural network model is developed on the basis of the simulation results to map the complex nonlinear relationship between design parameters and warpage. The GA is interfaced with this predictive model to improve the warpage significantly by optimizing the design parameters. A case study of a part is presented. The optimum values of design parameters are determined to minimize the warpage. Results show that the combined BPNN/GA approach is an effective method for the warpage optimization of fiber-reinforced composite injection molding.

Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II

In this paper, the parameters optimization of plastic injection molding (PIM) process was obtained in systematic optimization methodologies by two stages. In the first stage, the parameters, such as melt temperature, injection velocity, packing pressure, packing time, and cooling time, were selected by simulation method in widely range. The simulation experiment was performed under Taguchi method, and the quality characteristics (product length and warpage) of PIM process were obtained by the computer aided engineering (CAE) method. Then, the Taguchi method was utilized for the simulation experiments and data analysis, followed by the S/N ratio method and ANOVA, which were used to identify the most significant process parameters for the initial optimal combinations. Therefore, the range of these parameters can be narrowed for the second stage by this analysis. The Taguchi orthogonal array table was also arranged in the second stage. And, the Taguchi method was utilized for the experiments and data analysis. The experimental data formed the basis for the RSM analysis via the multi regression models and combined with NSGS-II to determine the optimal process parameter combinations in compliance with multi-objective product quality characteristics and energy efficiency. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in product length deviation, but also reduces the product weight and energy consuming in the PIM process. It is an emerging trend that the multi-objective optimization of product length deviation and warpage, product weight, and energy efficiency should be emphasized for green manufacturing.

Study of the filling mechanism and parameter optimization method for vacuum casting

Warpage is the most common quality defect in parts produced by vacuum casting (VC). In this paper, the VC process and mold filling mechanism are analyzed, and a mathematical model of the filling process is constructed, incorporating fluid dynamical relationships. The filling process is simulated using a numerical simulation algorithm. A warpage optimization strategy combining response surface methodology (RSM) with the artificial fish-swarm algorithm (AFSA) is then proposed, in which the main function of the RSM is to fit the relationship between process parameters and warpage, while that of the ASFA is to obtain the optimized process parameters. Finally, the implementation of the optimization strategy is presented in detail, together with a specific example, and simulations and a real experiment are carried out. The experimental results verify the accuracy of the model and the reliability of the algorithm. In conclusion, the optimization method can reduce the warpage of VC parts and improve product quality, providing a reliable new low-cost method for optimizing the process parameters in VC technology.

Warpage optimization with dynamic injection molding technology and sequential optimization method

Warpage is one of the serious defects for thin wall plastic injected component. This paper employs dynamic filling and packing process parameters as the new design variables to perform the optimization on warpage for the first time. In this paper, several numerical implementations for dynamic filling and packing are discussed in detail. The unclear functional relationship between the objective (maximum warpage) and 12 process parameters is characterized by the Kriging surrogate model approximately. These process parameters not only include the conventional process parameters, like melt temperature, filling time, packing pressure etc., but also include the new dynamic process parameters involving amplitude, frequency, and phase of vibration. Subsequently, the efficient global optimization method (expected improvement-based method) is used for searching the optimum solution sequentially. Final results suggest a set of process parameters of the dynamic injection molding technology with which the maximum warpage of a plastic part is greatly reduced. By comparison with the corresponding conventional injection, injecting pressure of the optimized system fluctuates with the same frequency as dynamic flow rate during filling, which plays a significant role for the warpage reduction.

A Warpage Optimization Method for Injection Molding Using Artificial Neural Network Combined Weighted Expected Improvement

Abstract A surrogate-based warpage optimization for injection molding is proposed in this study. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters, i.e., the mold temperature, melt temperature, injection time, packing time, packing pressure, and cooling time are the design variables. The warpage values are reduced by optimizing the process parameters. A new optimization iteration scheme based on artificial neural network (ANN) combined weighted expected improvement (WEI) is employed to speed up the optimization process and to ensure very rapid and steady convergence. The ANN is used to build an surrogate warpage function for the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is executed by the WEI function, which is an infilling sampling criterion. Although the design of experiment (DOE) size is small, this criterion can precisely balance the local and global search and tend to find the global optimal design. As examples, a TV cover and a scanner are investigated. The results show that the proposed approach can effectively reduce the warpage of the injection molding parts.