Injection Moulding Parameters Research Articles

Application of Taguchi Method in the Optimization of Injection Moulding Parameters for Manufacturing Products from Plastic Blend

Application of Taguchi Method in the Optimization of Injection Moulding Parameters for Manufacturing Products from Plastic Blend

Study on Injection Molding Parameters for Thin-Shell Plastic Parts Using a Neural Network–Based Approach

This study analyzed variation of warpage and tensile properties depending on injection molding parameters during production of thin-shell plastic components. A hybrid method integrating back-propagation neural network (BPNN), genetic algorithm (GA), and simulated annealing algorithm (SAA) are proposed to determine an optimal parameter setting of the injection-molding process. The results of 18 experimental runs were utilized to train the BPNN predicting warpage and tensile properties at various injection-molding conditions and then the GA and SAA approaches were applied to individual search for an optimal setting. The results show that the combinations of BPNN/GA and BPNN/SAA methods are effective tool for the optimization of injection molding parameter.

Feasibility study of use of recycled High Density Polyethylene and multi response optimization of injection moulding parameters using combined grey relational and principal component analyses

Usability of recycled High Density Polyethylene (HDPE) as substitute for virgin HDPE is investigated. Optimization design of the injection moulding parameters for recycled HDPE products is presented. Tensile, compressive and flexural strengths are selected to evaluate the process performance and the corresponding moulding parameters are melt temperature, holding pressure, injection time, and holding time. Optimal combination of injection moulding parameters is determined using Grey relational analysis. The principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics. Tensile, compressive and flexural strengths of the recycled HDPE are found close to that of virgin HDPE. Thus, recycled HDPE is a good substitute for virgin HDPE. Optimal combination of the process parameters for the multi-performance characteristics of the recycled HDPE is the set with melt temperature at 240 °C, holding pressure at 255 N/m 2, injection time at 0.6 s and holding time at 30 s.

Research on Microchannel of PMMA Microfluidic Chip under Various Injection Molding Parameters

Microfluidic chip has bright application prospect in the field of chemical and biological analysis. PMMA is the best material for microfluidic chip fabrication because of its low cost, prominent electrochemical properties and biological compatibility. In this work, a triple-channel microfluidic chip was designed; a nickel mold insert with microstructure was fabricated by UV-LIGA technology, and then the injection mold was designed and fabricated. The change in cross-section dimensions of microchannel was chosen as evaluating indicator, and micro-injection molding experiments were carried out. Then the influence of process parameters on cross-section dimensions of microchannel was observed with microscope. Experiment results demonstrated that substrate and cover plate could be manufactured in a single injection molding process by "1+1" type mold, and the manufacturing cycle was reduced to less than 20s, so production efficiency was greatly enhanced. Best combination of process parameters was obtained when mold temperature was 80°C, melt temperature was 250°C and injection rate was 297mm/s. Thereby, high quality substrate and cover plate could be manufactured efficiently.

Design and Manufacture of a Wax Injection Tool for Investment Casting Using Rapid Tooling

A rapid wax injection tool of a gearbox shift fork was designed, simulated, and manufactured using rapid prototyping and rapid tooling technology to save time and cost of producing wax models used for the investment casting process. CAE simulation softwares, in particular, MoldFlow, are used to get wax injection moulding parameters such as filling parameters, temperature profiles, freeze time, speed, and pressure. The results of this research were compared with conventional wax model production methods. The criteria of such comparison were based upon parameters such as time, cost, and other related characteristics, which resulted in saving of 50% in time and 60% in cost. In this research, design, assembly, and wax injection operation of the wax tool took 10 days. Considering the fact that wax melting temperature is as low as 70°C and injection pressure of 0.5 MPa, the tool suffers no damage due to the thermal and pressure stresses, leading to the mass production of wax models.

Characterization and analysis of weld lines on micro-injection moulded parts using atomic force microscopy (AFM)

In recent years plastic moulding techniques, such as injection moulding, have been developed to fulfil the needs of micro-components fabrication. Micro-injection moulding (μIM) is the process which enables the mass production of polymer micro-systems such as micro-mechanical parts, micro-fluidic systems, micro-optics, micro-components for medical devices, etc. A key factor in micro-polymer based systems is the possibility of designing and therefore replicating very complex geometries, allowing an increasing number of applications and micro-products. Complex geometries bring challenges during the filling stage of the process. Many features cause melt front separation: through holes, presence of pins and cores, changes of thickness, multi-gating systems, existence of micro-inserts, two component injection moulding, etc. When two or more melt flows join each other after separation, a layer between the two original flows will generate and a weld line is formed on the surface of the micro-moulded part. This phenomenon has to be avoided or at least reduced, since in the weld line area the mechanical properties are poorer than in the bulk part, creating strength problems on the final part. Although weld lines are unavoidable, the micro-injection moulding process can indeed be optimized in order to on one hand obtain a good filling of the cavity and on the other hand decrease the size of the weld lines. In this paper, an investigation devoted to the determination of the influence of typical injection moulding parameters on the weld lines’ dimensions is presented, using an atomic force microscope (AFM). Depth and width of weld lines were chosen as parameters to be optimized.

An in-line near-infrared process control tool for monitoring the effects of speed, temperature, and polymer colour in injection moulding

In recent years, near-infrared spectroscopy has become a versatile analytical tool and has gained acceptance in a large range of production processes and industrial sectors, such as the petrochemical, pharmaceutical, environmental, biomedical and automotive sectors. This study demonstrates the potential offered by infrared spectroscopy for the injection moulding industry. It was found to be a useful tool, not only for in-line monitoring of polymeric materials, but also for in-line monitoring of the injection moulding parameters such as temperature and injection speed.

Influence of feedstock characteristics and process parameters on properties of MIM parts made of 316L

Metal injection moulding (MIM) is already a well established and promising technology for the mass production of small, complex, near net shape products. The dimensions and mechanical properties of MIM products are influenced by the feedstock characteristics, the process parameters of injection moulding, as well as the debinding and the sintering. The mechanical properties primarily depend on the sintering density and the microstructure. However, the process parameters of the injection moulding must not be neglected. The present investigations were focused on the influence of the main injection moulding parameters and the sintering conditions. The investigations showed that appropriate mechanical properties can be obtained for MIM products. However, they are drastically deteriorated if inappropriate injection moulding and sintering conditions are selected. In this contribution the influence of the injection moulding and sintering parameters on the mechanical properties of sintered material is presented and discussed. For the optimisation of the whole process a feedstock characterisation was also made.

Correlation between Injection Molding Parameters, Morphology and Mechanical Properties of PPS/SEBS Blend Using Artificial Neural Networks

Abstract The objectives of this work were to identify the injection molding processing variables with the greatest effect on the morphology and mechanical properties of an injection molded blend made of poly (p-phenylene sulphide), PPS and block copolymer styrene-ethylene-butadiene-styrene ‘SEBS’. Artificial Neural Networks, ANNs, are used as an alternative method to constitutive and empirical models, to predict morphological features and mechanical properties from the injection molding conditions, and to predict mechanical properties from the morphological features. The quantification of SEBS dispersion in the PPS matrix was done using a dispersion function. Mold temperature and flow rate were the processing variables with the highest influence on the entire morphology, while the holding pressure influenced mainly the inner layers. Impact strength and toughness were most influenced by mold temperature, holding pressure and the outer layers. The flexural modulus was influenced by all processing variables and the intermediate layers. Three different ANNs were evaluated: one (ANN-1) to predict morphology from processing conditions and another two to predict mechanical properties from morphology and from processing conditions (ANN-2 and ANN-3, respectively). These latter ANN models had similar results, indicating that both inputs could be successfully used to predict mechanical properties, as the mean residuals were close to experimental errors. On the other hand, ANN-1 showed a lower performance, with a mean error smaller than the experimental error, suggesting that ANNs could overtake some inherent uncertainties. In this case, it was concluded that the distribution of data along output domain was more important than a high number of training data in the ANN's performance.

Examination of injection moulded thermoplastic maize starch

This paper focuses on the effect of the different injection moulding parameters and storing methods on injection moulded thermoplastic maize starch (TPS). The glycerol and water plasticized starch was processed in a twin screw extruder and then with an injection moulding machine to produce TPS dumbbell specimens. Different injection moulding set-ups and storing conditions were used to analyse the effects on the properties of thermoplastic starch. Investigated parameters were injection moulding pressure, holding pressure, and for the storage: storage at 50% relative humidity, and under ambient conditions. After processing the mechanical and shrinkage properties of the manufactured TPS were deter- mined as a function of the ageing time. While conditioning, the characteristics of the TPS changed from a soft material to a rigid material. Although this main behaviour remained, the different injection moulding parameters changed the character- istics of TPS. Scanning electron microscope observations revealed the changes in the material on ageing.

Manufacturing of multi-functional micro parts by two-component metal injection moulding

Several metals and alloys can be used to enhance the mechanical and physical properties of micro parts and components for micromechanical, micro-chemical or sensor applications. Such parts can be produced in series by the powder metallurgical process of micro metal injection moulding (μ-MIM). This paper describes a novel manufacturing route for metallic multi-material micro components, two-component micro metal injection moulding (2C-μ-MIM). Similar to “two-colour” injection moulding of plastics, the process allows the integration of multiple functions in a micro part by simultaneously injecting and joining two materials in one mould. Net-shape parts with solid material interfaces are obtained. In this paper, the 2C-μ-MIM process is exemplified for the combination of a non-magnetic and a ferromagnetic stainless steel (316 L and 17-4PH). It is shown that intact material interfaces of less than 500×500 μm2 can be achieved by careful selection and tailoring of metal powders, injection moulding and co-sintering parameters.

Correlations between Injection Molding Parameters, Morphology and Mechanical Properties of PPS Using Artificial Neural Networks

Abstract The processing conditions of injection molding have a complex influence on the morphology and on the mechanical properties of a semicrystalline polymer. Therefore, to establish correlations between processing conditions, morphology and mechanical properties constitutes a difficult task; finding these correlations is one of the goals of a materials engineer. The purpose of this work was to study the influence of the injection molding conditions on the morphology and mechanical properties of poly(p-phenylene sulphide), PPS, using artificial neural networks, ANNs. First, a statistical analysis was done to find the more influential processing parameters that affect the morphology and mechanical properties of the PPS. Second, ANNs were applied to establish correlations between processing conditions, morphology and properties. It was found that the variables with the highest influence on the morphology and the mechanical properties were the injection and mold temperatures (Tinj and Tmold, respectively), as they showed a straight relationship with the crystallinity index of the injection molded part. Three different ANNs were built to predict the correlations. The ANN-1 predicted the crystallinity gradient along the thickness of the injection molded part from Tmold, Tinj, and flow rate, Q; the ANN-2 predicted the elastic and flexural modulus, E, and the yield stress from the crystallinity gradient, while the ANN-3 predicted the mechanical properties directly from the processing conditions. All ANNs were built with only fifteen experimental data and were trained with the group cross-validation method, GCV and with a training-test set method. Both methods showed similar and excellent performance. Thus, it can be concluded that ANNs can be used as a powerful tool in the learning of these complex correlations.

Effects of Injection Molding Parameters on the Production of Microstructures by Micropowder Injection Molding

ABSTRACT 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.

Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis

This paper optimizes the injection-molding process for friction properties of fiber-reinforced polybutylene terephthalate using Taguchi method and principal component analysis. Four controllable factors of the manufacturing process were studied at three levels each. The single-response optimization of friction property was conducted by Taguchi method. Principal component analysis was employed to correspond to multi-response cases, for transforming the correlated friction properties to a set of uncorrelated components and evaluating the principal components. The optimum combination of process factors and levels for multiple qualities, based on the first principal component, was determined first. The appropriate number of the principle components, and the influence of the number on the optimum process condition, were subsequently studied by extracting more than one principal component and integrating into a comprehensive index. Finally, the analysis of variance was used to find out the most influential injection-molding parameter for single and multiple responses problems.

Fibre orientation of fibre-reinforced PBT composites in injection moulding

The present study investigates the fibre orientation and distribution of 15 wt-% short fibrereinforced polybutylene terephthalate using both experimentation and computer-aided analysis. Filling time, melt temperature, mould temperature and packing pressure are chosen to be the controllable parameters that govern the injection-moulding process. The influence of injectionmoulding process parameters on the frozen–intermediate–core morphology is observed using scanning electron microscopy. The variations of frozen layer thickness are strongly affected by the injection-moulding parameters. The minimum is at a filling time of 3 s, a melt temperature of 240°C, a mould temperature of 90°C, and a packing pressure of 100% of the constant 42 MPa injection pressure for the mould with dimension 45 mm x 45 mm x 4.0 mm. The experimental results are used to validate the accuracy of the computer simulation in fibre orientation and distribution. Simulated and experimental results closely correspond to each other.

Mechanical properties of a cryogenically mechanically alloyed polycarbonate-poly(aryl ether ether ketone) system

The processing–property relationship of a model cryogenically mechanically alloyed polymer–polymer system [polycarbonate (PC) and poly(aryl ether ether ketone) (PEEK)] was investigated. PC and PEEK powders were cryogenically mechanically alloyed for 10 h, and the resulting two-phase powder particles were processed into testable coupons with a miniature ram-injection molder. The bulk mechanical properties of the coupons made from the mechanically alloyed powders and nonmechanically alloyed powders were investigated as a function of mechanical alloying and injection-molding parameters. The injection-molded coupons were mechanically tested in the three-point-bending mode. The results demonstrated that no measurable improvement was achieved in the energy to break, strain at failure, or failure strength in the coupons made from the mechanically alloyed materials in comparison with those of the coupons made from the nonmechanically alloyed powders. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 1196–1202, 2003

Improved mould design in metal injection moulding by combination of numerical simulations and experiments

Metal injection moulding combines thermoplastic injection moulding and powder metallurgy technologies to produce small and intricate metallic parts in a near net shape. The injection stage is of primary importance and the injection moulding of a metallic powder and plastic binder permits the desired shape to be obtained. A procedure is proposed for mould cavity design and optimal choice of injection parameters. This procedure is based on both numerical simulation by the finite element method and experiments allowing a primer choice and an update of injection moulding parameters. The powder segregation phenomena have been also investigated by experiments, moulding and simulation and an approach based on a biphasic flow approach has been proposed that permits powder segregation to occur during injection. Based on that approach, a three-dimensional finite element software has been developed and used for simulation. Experiments and numerical investigations on powder segregation have been carried out and results are presented that are in good agreement with experiments.

Development of an expert system for the determination of injection moulding parameters of thermoplastic materials: EX-PIMM

Injection moulding is a process in which a hot polymer melt is forced to flow into an empty, cold cavity of desired shape and then allowed to solidify under a high holding pressure. There are several parameters of the injection moulding process. It is not easy to handle the relationships between these parameters in order to obtain an effective and rapid moulding. The goal of this study is to develop a frame-based, modular and interactive expert system (called “EX-PIMM”) for the determination of the injection moulding parameters of thermoplastic materials. The selection of the most suitable plastic injection moulding machine (PIMM) and thermoplastic material for the given job are the main objectives of this study. In addition, the optimum number of cavities can be determined according to the selection strategy used in this study.

Analyses of powder segregation in MIM

The effects of main process parameters on final components made by metal injection moulding have been studied through experiments correlated with accurate modelling and numerical simulations. A mould specially designed for experiments was made in our laboratory for components with different processing conditions, then the components were debound and sintered. Mechanical properties were measured by tensile and bending tests and correlated with processing conditions. The prediction of segregation during injection moulding was based on a bi-phasic model with a newly developed algorithm. The results from simulation were compared with experimental results and the agreement demonstrates that proposed methods are well adapted for mould design and determination of injection moulding parameters.

Simulation of injection moulding effects of thermal and inertial effects on flow stability

The effects of main process parameters on final components made by metal injection moulding have been studied through experiments correlated with accurate modelling and numerical simulations. A mould specially designed for experiments was made in our laboratory for components with different processing conditions, then the components were debound and sintered. Mechanical properties were measured by tensile and bending tests and correlated with processing conditions. The prediction of segregation during injection moulding was based on a bi-phasic model with a newly developed algorithm. The results from simulation were compared with experimental results and the agreement demonstrates that proposed methods are well adapted for mould design and determination of injection moulding parameters.