Plastic Injection Moulding Research Articles

Parameter Selection to Ensure Multi-Criteria Optimization of the Taguchi Method Combined with the Data Envelopment Analysis-based Ranking Method when Milling SCM440 Steel

SCM440 steel is a commonly used material for making plastic injection molds and components such as gears, transmission shafts, rolling pins, etc. Surface roughness has a direct influence on the workability and durability of the parts and/or components, while the Material Removal Rate (MRR) is a parameter that is used to evaluate the productivity of the machining process. Furnished products with small surface roughness and large MRR is the desired result by all milling processes. In this paper, the determination of the values of input parameters is studied in order to ensure that during the process of milling SCM440 steel, it will have the smallest surface roughness and the largest MRR. There are five parameters that are required to be determined, namely the cutting insert material, the tool nose radius, the cutting speed, the feed rate, and the cutting depth. The Taguchi method was applied to design the experimental matrix with a total of 27 experiments. Result analysis determined the influence of the input parameters on surface roughness and MRR. The Data Envelopment Analysis-based Ranking (DEAR) method was applied to determine the optimal value of the input parameters, which were used to conduct the milling experiments to re-evaluate their suitability.

Analysis of the Manufacturing Process Using Polyproplene by Plastic Injection Molding Method

Polypropylene is the most widely used type of polymer material. It is widely used in many areas from food to textile. Plastic injection molding is one of the most common methods used in the production of plastic parts. Many products in different sizes and shapes can be produced with plastic injection molding. In our study, carved furniture was produced from polypropylene raw material by plastic injection method and the parameters suitable for production were specified. Factors such as pressure, temperature, time, and speed are factors that directly affect the production and end product quality. For this reason, it is very important that the parameters are optimized well. The total cycle time of the production is determined as 40 seconds, and the final product is a product with 170 grams and 0.9 mm wall thickness. At the end of the study, the cost expenses of the sample firm were specified.

Analysis and improvement in the Electrical Discharge Machining process used on the manufacturing of moulds for plastics injection moulding

Due to increasing competitiveness in the industry, companies will invariably adopt strategies to ensure that their employees continuously eliminate waste and create more value. This concept was applied at a company that designs, develops, and manufactures steel moulds for the injection of thermoplastics, with the purpose of implementing Lean thinking in the production sector, thus achieving the following objectives: organization of production; preparation of documents to support production, and standardization of graphite stock. Regarding the application of the 5S tool, an improvement in worker safety, and in cleaning was observed, which also led to a reduction in the time wasted when locating tools. Through the implementation of 5S auditing, enabling to verify an overall improvement, from a rating of 35% in the first audit to 85% in the subsequent audit. The tools implemented allowed for improvements in communication between sectors, leading to increased productivity and productive autonomy. Finally, graphite stock was standardized by means of Kanban.

Parametric optimization of plastic injection moulding for FMCG polymer moulding (PMMA) using hybrid Taguchi-WASPAS-Ant Lion optimization algorithm

In this fast growing world, the demand for fast-moving consumer goods (FMCG) and half of the goods are manufactured through the use of plastic. To cater to those, plastic goods are manufactured using different types of moulding process. Plastic injection moulding (PIM) is a technique of injecting molten plastic into a mould to form it. They are widely used to manufacture FMCG products like containers, remote cases, or other small, complex profiles. But sometimes these complex profile mouldings lack dimensional accuracy. The current study provides an understanding of the clear thermoplastic Polymethyl Methacrylate (PMMA) material moulding for FMCG items utilizing an injection moulding machine. The transparent top sheath used in male/female deodorants can be considered in this investigation. For the purpose of minimizing weld-line width and sink-marks depth during injection moulding of commercial grade transparent thermo-plastic (Polymethyl Methacrylate), eight plastic injection moulding criteria were considered, namely melting temperature, mould temperature, cooling time, injection pressure, back pressure, holding pressure, ambient temperature, and holding time. In accordance with Taguchi's Design, the 27 experiments were piloted, and the factors were optimized using the Taguchi-based WASPAS method (Weighted Aggregated Sum Product Assessment), the Ant Lion optimization algorithm, and analysis of variance to determine the most dominant parameter in the minimization of weld-line width and sink-mark depth of the plastic injection moulded part. It was discovered that the cooling time of the plastic injection moulded component is the most important element affecting the weld-line width as well as the depth of the sink-mark on the surface of the part.

Determination of Optimal Process Parameters for Plastic Injection Molding of Polymer Materials Using Multi-Objective Optimization

Determination of Optimal Process Parameters for Plastic Injection Molding of Polymer Materials Using Multi-Objective Optimization

Rule-based explanations based on ensemble machine learning for detecting sink mark defects in the injection moulding process

Manufacturing quality control (QC) in plastic injection moulding is of the upmost importance since almost one third of plastic products are manufactured via the injection moulding process. Moreover, smart manufacturing technologies are enabling the generation of huge amounts of data in production lines. This data can be used for predicting the quality of manufactured plastic products using machine learning methods, allowing companies to save costs and improve their production efficiency. However, high-performance machine learning models are usually too complicated to be understood by human intuition. Therefore, we have introduced a rule-based explanations (RBE) framework that combines several machine learning interpretation methods to help to understand the decision mechanisms of accurate and complex predictive models – specifically tree ensemble models. These generated rules can be used to visually and easily understand the main factors that affect the quality in the manufacturing process. To demonstrate the applicability of RBE, we present two experiments with real industrial data gathered from a plastic injection moulding machine in a Singapore model factory. The collected datasets contain condition data for several manufacturing processes as well as the QC results for sink mark defects in the production of small plastic products. The experiments revealed that it is possible to extract meaningful explanations in the form of simple decision rules that are enhanced with partial dependence plots and feature importance rankings for a better understanding of the underlying mechanisms and data relationships of accurate tree ensembles.

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears.

This paper was aimed at finding out the solution to the problem of insufficient dimensional accuracy caused by non-linear shrinkage deformation during injection molding of small module plastic gears. A practical numerical approach was proposed to characterize the non-linear shrinkage and optimize the dimensional deviation of the small module plastic gears. Specifically, Moldflow analysis was applied to visually simulate the shrinkage process of small module plastic gears during injection molding. A 3D shrinkage gear model was obtained and exported to compare with the designed gear model. After analyzing the non-linear shrinkage characteristics, the dimensional deviation of the addendum circle diameter and root circle diameter was investigated by orthogonal experiments. In the end, a high-speed cooling concept for the mold plate and the gear cavity was proposed to optimize the dimensional deviation. It was confirmed that the cooling rate is the most influential factor on the non-linear shrinkage of the injection-molded small module plastic gears. The dimensional deviation of the addendum circle diameter and the root circle diameter can be reduced by 22.79% and 22.99% with the proposed high-speed cooling concept, respectively.

Comparison of the Surface Quality of the Products Manufactured by the Plastic Injection Molding and SLA and FDM Method

Plastic injection molding is a widely used manufacturing method in the automotive and machinery sectors. The design and manufacture of the appropriate mold needed to manufacture a plastic product with this method are a difficult, time-consuming, and costly process. Due to the developing technology and increasing quality requirements, this method remains insufficient, especially for sensitive and intricate parts that will be manufactured in low numbers. In additive manufacturing, the 3D printing technique is developing rapidly, and high-quality products can now be obtained with this method. In this study, sample pieces manufactured by fused deposition modeling (FDM), stereolithography (SLA) 3D printing techniques were compared with pieces manufactured by the plastic injection molding method. Prototype products were examined in terms of manufacturing techniques, appearance, and surface qualities. As a result of the study, it was determined that the parts manufactured by the SLA 3D printing technique were better in terms of the ease of manufacturing, appearance, and surface roughness and that this was followed by plastic injection molding and the 3D printing FDM method, respectively.

Effects of calcium-treatment of a plastic injection mold steel on the tool wear and power consumption in slot milling

Dies and molds steels are essential materials in the manufacturing industry of engineering products. These materials are usually machined in the hardened condition and therefore, can be problematic to transform them in chips. Calcium treatment can be a viable alternative to increase machinability without compromising the main properties of the steel. The present work investigates the machinability of the calcium treated mold steel, AISI P20 UF, and compares it to the non-treated version of the same material, AISI P20, in slot milling tests with triple coated (TiN, TiCN and Al2O3) cemented carbide tools. A consolidated method that minimizes the number of tests needed for the determination of the extended Taylor's equation coefficients was used and the power consumption was measured during the machining experiments. SEM was used for the exploration of the wear of the used tool and its mechanism. The results showed that the calcium treated steel presented a considerably higher tool life, and although the treatment did not affect the power consumption directly, indirectly it reduced it because of the positive reduction of the tool wear rate allowing the power to be kept at lower levels for more extended periods. Attrition (adhesion) and abrasion were the primary tool wear mechanisms observed when machining the non-treated steel and attrition for the calcium treated material. In this latter case, because of the longer tool lives, chippings of the cutting edge were also present.

Evaluation of Local Flow during Injection Molding of Fiber-reinforced Plastics via Moving Particle Semi-implicit Particle Simulation

Evaluation of Local Flow during Injection Molding of Fiber-reinforced Plastics via Moving Particle Semi-implicit Particle Simulation

Design and construction of a sensor analytic system for the monitoring of the parameters of a plastic injection mould

PurposeValues of parameters such as temperature, humidity, number of plastic products and the location of plastic injection moulds are required to determine the efficiency of plastic injection moulds with a view to improving the quality of the outputs. This article determined the appropriate sensors for the measurement of these essential parameters in the most suitable form of representation of the data to aid a proficient analysis of the data.Design/methodology/approachThe outputs of these sensors were obtained by connecting the sensors to the general-purpose input/output (GPIO) pins of a Raspberry Pi and writing a Python programme for the connected GPIO pins. The values of the outputs of these sensors were represented in a graphical form. The connection of the Raspberry Pi and the sensors were done with a full-sized breadboard and jumper wires. A computer-aided design (CAD) of the connections was produced using Fritzing software.FindingsThe appropriate sensors determined are MLX90614 infrared thermometer sensor, DHT11 humidity sensor, pixy2 vision sensor and Neo-6m GPS sensor. This study proposed that the sensors analytic system be applied on an industrial plastic injection mould to measure and display the various parameters of the injection moulds for the purpose of understanding and improving the performance of the injection mouldOriginality/valueAn electronic system that provides the continuous values of essential parameters of a plastic injection mould in operation.

Design of an Arc-Core Moving Mechanism for Injection Molding Using a Link and Cam Mechanism

A novel plastic injection mold design for a product with a deep arc-hole based on the kinematic analysis is proposed. To move the wide angle arc-core, a hybrid mechanism combining a slider-crank and a swing cam is designed. The force transmission coefficient of the slider-crank and the pressure angle of the swing cam are used to evaluate the dimensions of the mechanism. The motion of the designed mechanism was confirmed by using a prototype made with a 3D printer.

Problems and Constraints Involved in the Injection Moulding Process of Biocomposites: A Review

Biocomposite materials have revolutionized the engineering field, predominantly in manufacturing applications, where these materials were widely used as an alternative to the metal-based structural body or component. Since conventional materials have caused various pollution problems to the ecosystem, innovative efforts have been taken to develop the based product on natural materials composites but presently are not fully explored yet. Most issues raised are due to the lack of expertise in addressing some of the problems and constraints related to preparing feedstock materials involved in the injection moulding technique. The injectability process of the biocomposite material significantly depends on the early stage of feedstock preparation. The preparation steps should be considering the detailed characterization of the composite material, the effectiveness of the mixing method, the identification of the feedstock's flowability properties, and the appropriate processing parameters used. This review manuscript has a significant contribution to researchers interested in furthering the application of biocomposite-based materials, particularly in plastics injection moulding technology for manufacturing and processing engineering.

A Density-based Clustering Approach for Monitoring of Injection Moulding Machine

Fault Detection and Isolation (FDI) is crucial and challenging problem in many industrial applications and continues to be an on-going research issue in the control community. In the literature, model-based techniques are mostly employed to generate residuals for diagnosis and decision-making. In this paper, we focus on FDI problem using a novel based-clustering approach. The key idea is to restrict each fault to be a data cluster with high-density gathering the most similar objects. In this way, the algorithm does not require prior analytic models to start. It uses rather a density measurement to detect and isolate cluster’s regions. The overall algorithm is expanded around two fundamental steps: cluster domain description and density-based clustering. To address properly the requirements of system control and monitoring, the algorithm is designed to work in real-time as observations are acquired and it is endowed with specific tools for data mining and feature extraction. A study case is proposed consisting of plastic Injection Molding Machine (IMM) to prove the effectiveness of the method.

Novel Structural Design of a Bicycle Saddle to Fulfill Cyclist Physiological Requirements

Background: The perineum area that interacts with the saddle is vulnerable; compressing neurovascular tissues has been asserted as the link to pathologies. Existing saddle designs are mainly based on the hole blocks, which are composed of two materials. These composite combined structures increase manufacturing difficulty. Objective: The injection molding technology is suitable for mass production. We proposed a saddle design to meet the ergonomic requirements of cyclists. Saddle frames present a continuous curvature geometry to ensure improved injection modeling. Methods: Static numerical calculations and measurements were employed for vertical load evaluations with different stiffness regions acquired using perforated chain patterns. Two-step plastic injection molding was utilized with proper bonding and processing compatibility. In prototyping, the first and second shots are PC and TPE, respectively. Results: The frame sustains vertical loadings from bending and provides adequate stiffness and proper flexibility. The computer-aided design mold increases the contact area, and annular grooves increase the adhesion between two materials. The outer frame exhibits high rigidity; the middle area exhibits flexibility and high deformation. Conclusion: This question is widely noticed and various answers have been proposed. By investigating the patent database and searching journal papers on saddle designs, the authors confirmed the novelty of the proposed structure.

Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials.

Achieving good quality of products from plastic injection moulding processes is very challenging, since the process comprises many affecting parameters. Common defects such as warpage are hard to avoid, and the defective parts will eventually go to waste, leading to unnecessary costs to the manufacturer. The use of recycled material from postindustrial waste has been studied by a few researchers. However, the application of an optimisation method by which to optimise processing parameters to mould parts using recycled materials remains lacking. In this study, Response Surface Methodology (RSM) and Particle Swarm Optimisation (PSO) methods were conducted on thick plate parts moulded using virgin and recycled low-density polyethylene (LDPE) materials (100:0, 70:30, 60:40 and 50:50; virgin to recycle material ratios) to find the optimal input parameters for each of the material ratios. Shrinkage in the x and y directions increased in correlation with the recycled ratio, compared to virgin material. Meanwhile, the tensile strength of the thick plate part continued to decrease when the recycled ratio increased. R30 (70:30) had the optimum shrinkage in the x direction with respect to R0 (100:0) material where the shrinkage increased by 24.49% (RSM) and 33.20% (PSO). On the other hand, the shrinkage in the y direction for R30 material increased by 4.48% (RSM) and decreased by 2.67% (PSO), while the tensile strength of R30 (70:30) material decreased by 0.51% (RSM) and 2.68% (PSO) as compared to R0 (100:0) material. Validation tests indicated that the optimal setting of processing parameter suggested by PSO and RSM for R0 (100:0), R30 (70:30), R40 (60:40) and R50 (50:50) was less than 10%.

Impact of support material deformation in MEMS bulk micromachined diaphragm pressure sensors

In this work, experimental data and finite element analysis reveals deflection in diaphragm supporting material leading to non-linear pressure-reflection response. These results are contrary to the standard assumptions presented in literature, where modelling of deflection response in diaphragm pressure sensors is primarily carried out assuming a rigid supporting structure. An extrinsic fiber-optic Fabry–Perot pressure sensor, based on micro-electromechanical system, is developed and used to investigate optical deflection response. While the sensor is not novel, a series of experiments to validate support deflection phenomena are designed and carried out using a silicon membrane at gauge pressures from 0 to 1000 PSI in ambient temperature. The device is packaged with an industry standard stainless-steel housing typically used in plastic injection moulding. Sensor performance is compared to analytical and finite element modelling. Results suggest that the device is experiencing greater deflection than analytically predicted at pressures above 200 PSI, where a rigid support is assumed in existing literature. Based on these results, a modified analytical model is proposed to correct for this behaviour. The modified model is created through addition of a nonlinear component to an existing model, which is then fitted to the experimental data using least-square methods. Prediction of the experimental deflection results is improved from 81% error using a fixed-support analytical model to 4% error using the presented adjusted model. It is demonstrated that nonlinear effects are present and optically measurable in cases where deflection is lower than 1% of the membrane thickness. This work will aid in the implementation of high-resolution pressure sensors operating in harsh environment conditions.

Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method

The product quality, productivity, and cost are mainly considered to make the manufacturing plan in plastic injection molding (PIM). The process parameters in PIM play a crucial role in determining the product quality, productivity, and cost. There are actually contradictions between above three properties. Therefore, it is difficult to quickly and accurately obtain the process parameters setting that meet the product quality requirement under the premise of acceptable productivity and cost. In this paper, a differential sensitivity fusion method (DSFM) is proposed to perform the multi-objective optimization of process parameters in PIM for the product quality and productivity improvement and the cost-saving, which integrates sampling strategy, numerical simulation, metamodeling method, and multi-objective optimization algorithm. The sampling strategy is utilized to generate sampling points from the design space at different parameter levels. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, the metamodeling method is applied to construct the response predictors to calculate the objective responses for any sampling point in the global design space. The multi-objective optimization algorithm is executed to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. The automobile front bumper is taken as the case study to verify the proposed method. The numerical results demonstrate that the proposed metamodeling method has better prediction accuracy and performance compared to some classical methods (e.g., response surface model, Kriging) and the multiple objectives cannot reach the optimal simultaneously. Moreover, the trade-off analysis identifies the better solution for decision-making, which helps to quickly and effectively select the optimal process parameters setting.

Warpage Optimisation on the Moulded Part with Straight Drilled and Conformal Cooling Channels Using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches.

It is quite challenging to control both quality and productivity of products produced using injection molding process. Although many previous researchers have used different types of optimisation approaches to obtain the best configuration of parameters setting to control the quality of the molded part, optimisation approaches in maximising the performance of cooling channels to enhance the process productivity by decreasing the mould cycle time remain lacking. In this study, optimisation approaches namely Response Surface Methodology (RSM), Genetic Algorithm (GA) and Glowworm Swarm Optimisation (GSO) were employed on front panel housing moulded using Acrylonitrile Butadiene Styrene (ABS). Each optimisation method was analysed for both straight drilled and Milled Groove Square Shape (MGSS) conformal cooling channel moulds. Results from experimental works showed that, the performance of MGSS conformal cooling channels could be enhanced by employing the optimisation approach. Therefore, this research provides useful scientific knowledge and an alternative solution for the plastic injection moulding industry to improve the quality of moulded parts in terms of deformation using the proposed optimisation approaches in the used of conformal cooling channels mould.

Thermal buckling of thin injection-molded FRP plates

The occurrence of buckling during cooling of an injection-molded fiber-reinforced plastic (FRP) object is a complex phenomenon which can result in unacceptably large deformations. The present work focuses on the thermal buckling of injection-molded thin disks, as they are representative for the local behavior in parts of a more complex object. An analytical approach based on the Föppl-von Kármán theory for thin elastic plates is presented and used to analyze its buckling stability and initial post-buckling behavior. The system of equations is formulated in a coordinate-free approach for a thin anisotropic linear thermo-elastic body with residual thermal stresses. Large out-of-plane displacements are assumed. The particular case of a free disk with cylindrical orthotropy and uniform thermo-elastic properties through its thickness is considered. A general technique to study the bifurcation in the solutions (i.e., buckling) based on a perturbation approach and the Fourier-Galerkin method to determine post-buckling deflections is proposed. Experimental and numerical results available in the literature are used as validation cases. The results of the present model show qualitative agreement with the experimental data. It is found that the periodicity of the first buckling mode for orthotropic disks is fully determined by the ratio between the elastic moduli in the tangential and radial directions. The predictions are also in excellent quantitative agreement with the numerical results of FEM models for the same cases. The effect of the disk thickness on the buckling temperature and warpage magnitude is correctly captured.