Effects of Molding Parameters on MIM’s Material Distribution using Numerical Simulation Method

Nowadays, micro metal injection molding has become among the promising method in powder metallurgy research to produce small-scale intricate part at an effective process and competitive cost for mass production. This paper investigated the optimization of highest green strength which plays an important characteristic in determining the successful of micro MIM. In this paper, stainless steel SS 316L with D50 = 5.96µm was used with composite binder, which consists of PEG (Polyethelena Glycol), PMMA (Polymethyl Methacrilate) and SA (Stearic Acid). Feedstock with 61.5% with several injection parameters were optimized which highly significant through screening experiment such as injection pressure(A), injection temperature(B), mold temperature(C), injection time(D) and holding time(E). Besides that, interaction effects between injection pressure, injection temperature and mold temperature were also considered to optimize in the Taguchi’s orthogonal array. Analysis of variance (ANOVA) in terms of signal-to-noise ratio (S/N-larger is better) for green density was also presented in this paper. Result shows that interaction between injection temperature and mold temperature(BxC) give highest significant factor followed by interaction between injection pressure and mold temperature(AxC). Single factor that also contributes to significant optimization are mold temperature(C) and injection time(D). This study shows that Taguchi method would be among the best method to solve the problem with minimum number of trials.

Micro metal injection molding which is a new develop technology has attract most researcher where it becomes among the promising method in powder metallurgy research to produce small-scale intricate part at an effective process and competitive cost for mass production. Due to highly stringent characteristics of micro MIM feedstock,the study has been emphasized in investigating the optimization of highest green strength which plays an important characteristic in determining the successful of micro MIM. Stainless steel SS 316L with D50 = 5.96μm was used with composite binder, which consists of PEG, PMMA and Stearic Acid. From rheological characteristic and highly significant parameter through screening experiment, feedstock with 61.5% with several injection parameters were optimized such as injection pressure(A), injection temperature(B), mold temperature(C), injection time(D) and holding time(E). Besides that, interaction effects between injection pressure, injection temperature and mold temperature were also considered to optimize in the Taguchi’s orthogonal array. Analysis of variance (ANOVA) in terms of signal-to-noise ratio (S/N-larger is better) for green strength was also presented in this paper. Result shows that interaction between injection temperature and mold temperature(BxC) give highest significant factor followed by interaction between injection pressure and injection temperature(AxB). Single factor that also contributes to significant optimization are mold temperature(C), injection time(D) and injection pressure(A). This study shows that Taguchi method would be among the best method to solve the problem with minimum number of trials.

Micro metal injection molding which is a new develop technology has attract most researcher where it becomes among the promising method in powder metallurgy research to produce small-scale intricate part at an effective process and competitive cost for mass production. Due to highly stringent characteristics of micro MIM feedstock,the study has been emphasized in investigating the optimization of highest green strength which plays an important characteristic in determining the successful of micro MIM. Stainless steel SS 316L with D50 = 5.96μm was used with composite binder, which consists of PEG, PMMA and Stearic Acid. From rheological characteristic and highly significant parameter through screening experiment, feedstock with 61.5% with several injection parameters were optimized such as injection pressure(A), injection temperature(B), mold temperature(C), injection time(D) and holding time(E). Besides that, interaction effects between injection pressure, injection temperature and mold temperature were also considered to optimize in the Taguchi’s orthogonal array. Analysis of variance (ANOVA) in terms of signal-to-noise ratio (S/N-larger is better) for green strength was also presented in this paper. Result shows that interaction between injection temperature and mold temperature(BxC) give highest significant factor followed by interaction between injection pressure and injection temperature(AxB). Single factor that also contributes to significant optimization are mold temperature(C), injection time(D) and injection pressure(A). This study shows that Taguchi method would be among the best method to solve the problem with minimum number of trials.

This research studies the effect of injection moulding parameters on the density of green body of Cobalt-30Chromium-6Molybdenum (Co-30Cr-6Mo) for powder injection moulding (PIM) feedstock. In this paper 20 micron Co-Cr-Mo powder was mixed with a palm stearin and polyethylene binder system. L18 orthogonal array by Taguchi Method was used to optimize and predict the future performance. Several injection parameters were optimized such as injection temperature, holding pressure, injection temperature, and mould temperature and injection time. The result shows that the optimum combination of these parameters will produce higher density micro parts. The optimum parameters for 67% powder loading of 20µm Co-30Cr-6Mo powder is 180oC injection temperature, while injection pressure, mold temperature, packing time and injection time are 10 bar, 100oC, 5 s and 7 s respectively.

Micropowder injection molding (μPIM) is a potential low-cost process for the mass production of metal or ceramic microstructures. In order to obtain good molded microstructures and to avoid molding defects, it is important to select suitable injection molding parameters. In this paper, the selection of injection molding conditions for the production of 316L stainless steel microstructures by μPIM is presented. Silicon mold inserts with 24 × 24 microcavities were injection molded on a conventional injection molding machine. The dimensions of each microcavity were Φ 100 μ m × depth 200 μm, giving an aspect ratio of 2. The distance between each microcavity was 200 μm. Five sets of experiments were conducted by varying one injection molding parameter at a time. The parameters included injection pressure, holding pressure, holding time, mold temperature, and melt temperature. Higher injection pressure and holding pressure were required during the injection molding process due to the small dimensions of the microcavities and the large number of microcavities (576 microcavities). High mold temperature was required for complete filling of the microcavities. Molded microstructures without visual defects were obtained using appropriate injection molding parameters. Catalytic debinding and sintering of the 316L stainless steel microstructures were successfully conducted.

For many years, injection molding is adopted as the most suitable manufacturing process in the production of complex plastic components. It is very difficult to boost the product's quality stability to attain a higher forming speed and higher automation in the injection moulding process because the process often disrupted by numerous inevitable deviations. The settings for different process parameters and machine parameters have quite a major influence on the overall product quality and its performance of the injection moulding process, which shows the final product quality as output responses. Identifying the perfect process parameter settings so that numerous responses may be optimized at almost the same time is becoming a significant research challenge these days. Smart manufacturing is at the heart of the new industrial revolution, and it will continue to be the primary means of transforming and upgrading the manufacturing industry in the upcoming decades. Melt temperature, the temperature of mould, injection pressure, and velocity of molten polymer while injection, injection time, pressure of molten polymer while holding into the mold cavity, holding time of molten polymer, and cooling time are the variety of input process parameters.  While mechanical characteristics, flaws such as warpage, volumetric or dimensional shrinkage, sink mark on outer surface, residual stress, and surface finish/roughness are examples of responses. To study and optimize the molding process, researchers have used a wide range of methods. Mathematical modeling, simulation, design of experiments, Taguchi methodology, analysis of variance - ANOVA, response surface methodology - RSM, evolutionary algorithm (EA), artificial neural networks - ANN, genetic algorithms - GA, fuzzy logic, particle swarm optimization - PSO, are examples of these techniques. This paper presents a review of the numerous strategies that used till date for optimizing the key injection moulding parameters, as well as their benefits and challenges. In the review, it is discovered that a perfect injection molding process, which takes care of overall quality control is still being researched upon. The process parameters involved in injection molding, required response, and the methodologies used for optimization of overall injection molding process are discussed throughout this paper.

The L27 (313) Taguchi orthogonal array is used in the experiment while the injection pressure, injection temperature, powder loading, mold temperature, holding pressure and injection speed are optimized in this study. A grey relational grade (GRG) obtained from the grey relational analysis (GRA) is used to solve the titanium alloy powder injection molding operations with the multiple performance characteristics such as strength and density. From the analysis of variance (ANOVA), the injection temperature has the highest contribution at confidence level 90% to the quality of green part followed by powder loading. The others factor such as mold temperature, holding pressure, injection pressure and injection rate and all the interactions are not significant even though it give contribution during injection molding but the confidence level are less than 90%.

This study optimized the influence of process parameters on the mechanical properties during injection molding (IM) of PC/ABS blend. The Taguchi method of design of experiments (DOE) was employed to optimize the process parameters and to increase the tensile strength and the elasticity module. Taguchi’s L9 (34) orthogonal array design was employed for the experimental plan. Process parameters of the injection molding such as material temperature, injection pressure, holding time, and mold temperature were studied with three levels. The Signal to noise (S/N) ratio for mechanical properties of PC/ABS blend using the Taguchi method was calculated. Taguchi’s results proposed two sets of optimal injection parameters conditions to achieve the best mechanical characteristics (σ, E). The (S/N) ratio results proved that the injection pressure was the more prominent than the other IM process parameters for the tensile strength, and the material temperature was the more prominent for the elasticity module.

Metal Injection Moulding (MIM) is an effective near-net-shape process for producing small, intricate parts in high volumes. Many optimizations of the MIM process parameter have been published without using any of the statistical based Design of Experiment (DOE) methodology and ends with unsatisfactory results in a wide range of experimental settings. This paper focuses on analysing the optimization of injection moulding process parameters for titanium alloy Ti-6Al-4V green parts using response surface methodology (RSM) to determine significant parameters. The feedstock is prepared by mixing Ti-6Al-4V powder with palm stearin (PS) and polyethylene (PE) binders in three different powder loadings: 63 vol%, 65 vol%, and 67 vol%. RSM is then used to create a Design of Experiment (DOE) to optimize parameters, including A: injection pressure, B: injection temperature, C: powder loading, D: mould temperature, and E: holding pressure. Analysis of variance (ANOVA) was performed to identify the significant parameters influencing defects in Ti-6Al-4V green parts. The study successfully identified the optimal conditions for producing Ti-6Al-4V components using PS and PE binders: A: 443.139 bars, B: 142.11 ºC, C: 65.38 vol.%, D: 44.694 ºC, and E: 629.561 bar. Scanning electron microscopy (SEM) from optimised of green part shows that the primary backbone polymer PE holds the green part and generates wetting for the feedstock and will ease moulding and shaping while the secondary backbone PS will act as a soft polymer that can provide the moisture to the feedstock.

Purpose This paper studies the feasibility of additive manufacturing (AM) processes and their potential materials to produce mold inserts for injection molding (IM) of plastic parts. This study aims to describe the technological advancements and practical implications of integrating AM with IM to produce plastic parts, reducing the gap and determining the feasibility of AM for insert production. Design/methodology/approach A literature search of different databases was done. Applying the PRISMA methodology, the 67 most relevant articles between 2013 and 2024 were selected. From these, a bibliometric analysis was performed, and the main results regarding the mechanical properties and the number of injection cycles achieved by inserts were extracted. Findings Material jetting (MJT), vat photopolymerization via ultraviolet light laser (VPP-UVL) and laser powder bed fusion using metal powders (PBF-LB/M) are the most useful AM processes reported in the literature to produce inserts for IM. Studies show that the maximum number of successful injection cycles achieved with these AM inserts has been 116, 85 and more than 500 cycles, respectively. The molded geometry, the injected material and the IM parameters influence the number of injection cycles, being the injection pressure, the mold temperature and the injection temperature the most critical parameters to consider in extending the life of the inserts. Originality/value To the best of the authors’ knowledge, this study provides the first systematic review with a comprehensive overview of this innovative approach to evaluate the emerging directions, current barriers and future potential of using AM with IM to manufacture plastic parts. As such, this study highlights the primary findings in the literature concerning AM processes and the materials commonly used to manufacture inserts for IM.

Modelling of yield length in the mould of commercial plastics using artificial neural networks

This paper presents an application of the Continuous Sensitivity Equation Method (CSEM) for the optimization of the injection molding process and its three-dimensional (3D) simulation by the finite element method. Finding the proper combination of process parameters such as injection speed, and melt and mold temperatures is critical to achieving a part that minimizes warpage and has the desired mechanical properties. Very often a successful design in injection molding comes at the end of a long trial and error process. Design Sensitivity Analysis (DSA) can help manufacturers improve their designs and can produce substantial savings in terms of both time and money. This work explores the ability of sensitivity analysis to predict the effects of design parameters on the performance of an injection molding process. The paper presents results of a 3D finite element solution of the filling stage of the injection molding process. Sensitivities of the solution with respect to different process parameters are computed using the continuous sensitivity equation method. Solutions are shown for the non-isothermal filling of a rectangular plate with a polymer melt behaving as a non-Newtonian fluid. The paper presents the equations for the sensitivity of the velocity, pressure and temperature and their solution by a finite element method. Sensitivities of the solution with respect to the injection speed, the melt and mold temperatures and to the heat transfer coefficient at the cavity/mold interface are shown.

For the installation of a fuel cell stack to convert chemical energy into electricity it is common to apply bipolar plates to separate and distribute reaction gases and cooling agents. For reducing manufacturing costs of bipolar plates a fully automated injection molding process is examined. The high performance thermoplastic matrix material, polyphenylene sulfide (PPS), defies against the chemical setting and the operation temperature up to 200 °C. To adjust also high electrical and thermal conductivity, PPS is highly filled with various carbon fillers up to an amount of 65 percentage by volume. In the first step two different structural plates (one-sided) with three different gate heights and molds are designed according to the characteristics of a bipolar plate. To cope with the approach that this plate should be producible on standard injection molding machines with variothermal mold temperature control, injection molding simulation is used. Additionally, the simulation should allow to formulate a quality prediction model, which is transferrable to bipolar plates. Obviously, the basis for a precise simulation output is an accurate description of the material properties and behavior of the highly filled compound. This, the design of the structural plate and mold and the optimization via simulation is presented, as well. The influence of the injection molding process parameters, e.g. injection time, cycle times, packing pressure, mold temperature, and melt temperature on the form filling have been simulated to determine optimal process conditions. With the aid of the simulation and the variothermal mold temperature control it was possible to reduce the required melt temperature below the decomposition temperature of PPS. Thereby, hazardous decomposition products as hydrogen sulfide are obviated. Thus, the health of the processor, the longevity of the injection molding machine as well as the material and product properties can be protected.

The injection molded housing part with thin shell feature could be produced to increase the internal space for packing more components. In this study, injection velocity, packing pressure, mold temperature, and melt temperature were selected as effective parameters for injection molding process. For the purpose of reducing dimension shrinkage variation of thin shell molded part, the response surface methodology was utilized to determine the relationship between input parameters and responses. Then the optimization condition was obtained according to the desirability function. Results show that melt temperature is the most significant factor on dimension shrinkage variation in transverse direction, followed by packing pressure, mold temperature, and injection velocity. However, in the longitudinal direction, packing pressure has the greatest influence on the dimension shrinkage variation, followed by injection velocity, melt temperature, and mold temperature. In accordance with verification experiments, the difference between the experimental data and predicted values ranges from −9.8% to 1.8%. To obtain the optimal condition, the overall desirability must be larger than 0.9. Based on analysis of variance, the proposed models look reasonably accurate.

The application of Taguchi method to reduce warpage in an injection moulding process is studied. The objective of this paper is to analyze the effect of injection moulding parameters, i.e., injection time, packing time, melt temperature and mould temperature, on the warpage defect in dumbbell plastics part. Optical comparator horizontal type was used to measure the difference of warpage value on each part. L9 orthogonal array with 3 replications was done with 27 totals of specimens. The result collected was optimized using Taguchi method and percentage of contribution was calculated using analysis of variance (ANOVA). According to the analysis, it is found that the significant factors affected warpage are injection time (32.01%), packing time (29.73%), mould temperature (24.39%) and melt temperature (13.87%). The optimum parameters for minimizing the warpage were injection time (1s), packing time (5s), melt temperature (270 °C) and the mould temperature (21 °C). By using Taguchi method and ANOVA analysis, optimum parameters and the percentage of contribution of parameters can be obtained. Thus, it shows that design of experiment method is the good quality tools to get the best quality for production.