Injection Moulding Process Research Articles

Towards dynamic multiscale feedback during the injection moulding cycle of plastics

Injection moulding is a very popular technology for shaping plastics. Its history stretches back to the nineteenth century, and, as a consequence, it has developed outside the framework of digitisation. In order to fully implement the concepts of Industry 4.0, we need to update these legacy technologies so that they can fully benefit from the developments inherent in the “Internet of Things” and allow the process of injection moulding to take full advantage of digital optimisation so that it can fit effectively in the digital factory. In this work, we explore the quantitative use of X-ray scattering as a technology that can provide dynamic and multiscale feedback during the injection moulding cycle to be able to exploit digital twin technology as a means to optimise the operational parameters involved in injection moulding and to enable digital design of moulds in the fullest sense. This manuscript provides a way to mark future work and draw these possibilities to a wider audience.

Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.

Energy Consumption Prediction of Injection Molding Process Based on Rolling Learning Informer Model.

Accurate energy consumption prediction in the injection molding process is crucial for optimizing energy efficiency in polymer processing. Traditional parameter optimization methods face challenges in achieving optimal energy prediction due to complex energy transmission. In this study, a data-driven approach based on the Rolling Learning Informer model is proposed to enhance the accuracy and adaptability of energy consumption forecasting. The Informer model addresses the limitations of long-sequence prediction with sparse attention mechanisms, self-attention distillation, and generative decoder techniques. Rolling learning prediction is incorporated to enable continuous updating of the model to reflect new data trends. Experimental results demonstrate that the RL-Informer model achieves a normalized root mean square error of 0.1301, a root mean square error of 0.0758, a mean absolute error of 0.0562, and a coefficient of determination of 0.9831 in energy consumption forecasting, outperforming other counterpart models like Gated Recurrent Unit, Temporal Convolutional Networks, Long Short-Term Memory, and two variants of the pure Informer models without Rolling Learning. It is of great potential for practical engineering applications.

Improvements in Injection Moulds Cooling and Manufacturing Efficiency Achieved by Wire Arc Additive Manufacturing Using Conformal Cooling Concept.

The plastic injection moulding industry is a constantly developing industrial field. This industrial process requires the manufacturing of metal moulds using complex heating and cooling systems. The purpose of this research is to optimize both the plastic injection moulding process and the mould manufacturing process itself by combining practices in this industry with current additive manufacturing technologies, specifically Wire Arc Additive Manufacturing (WAAM) technology. A mould punch was manufactured by using both WAAM technology, whose internal cooling system has been designed under the concept of Conformal Cooling, and conventional cooling channel designs and manufacturing techniques in order to carry out a comparative analysis. Theoretical results obtained by CAE methods showed an improvement in heat extraction in the WAAM mould. In addition, the WAAM mould was able to achieve better temperature homogeneity in the final part, minimizing deformations in the final part after extraction. Finally, the WAAM manufacturing process was proven to be more efficient in terms of material consumption than the conventional mould, reducing the buy-to-fly ratio of the part by 5.11.

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Moulding

ABSTRACTThe development of machine learning (ML) technologies is driving changes across many sectors. In industrial settings, this is called the fourth industrial revolution and encompasses several technologies pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial injection moulding machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process, while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines K‐nearest neighbours classification and nearest neighbours optimization methods to optimize an injection moulding process effectively. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and ML methods in a real‐world setting to improve production quality, production time and energy cost.

ERRATUM: Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

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Dynamic output feedback robust MPC hybrid fault-tolerant control for discrete uncertain systems: a switching control strategy

To guarantee the control performance of the discrete system under unmeasurable state and partial actuator failure, a robust dynamic output feedback predictive fault-tolerant control method derived from switching strategy is developed. A new iterative error switching model is established, which both ensures tracking performance and lays the foundation for the development of the next switching controller. Based on this model, output feedback switching fault-tolerant controller is developed. Compared with conventional fault-tolerant control methods, the designed controller can be used under the case of unmeasurable states, and it is more fault tolerant and less conservative. According to the Lyapunov stability theory, the robust stability analysis is carried out under normal and fault conditions, respectively. Then, depending on the findings of the stability analysis, sufficient conditions to guarantee the stability of the closed-loop normal and faulty system are derived. Finally, the suggested method's efficacy is confirmed using the injection moulding process as an example.

Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization

This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.

Improving the joint strength of thermoplastic composites joined by press joining using laser-based surface treatment

Fibre-reinforced thermoplastic composites (TPC) provide an automated and cost-effective solution for their use in lightweight structures in series production. The combination of different material configurations allows the design of highly stress tolerant components. Previous studies demonstrated that the combination of TPC sheets, TPC hollow profiles and injection moulding compounds is even suitable for crash-relevant automotive parts. All three components are combined during the injection moulding process. To prevent collapse, the part must remain in a consolidated state and cannot be preheated. However, this results in poor adhesion between the hollow profile, the bulk material, and the TPC sheet. Previous studies have shown that the bonding strength between the hollow profile and the injection moulding compound can be increased by surface pre-treatment using laser structuring and plasma technology. In this work, laser structuring is employed to enhance the bonding strength between the hollow profile and the TPC sheet. Microscopy analysis is used to investigate the resulting surface morphology. Subsequently, single-lap-shear (SLS) specimens are produced by pressing the TPC sheets onto the flat part. The resulting bonding strengths are then evaluated by tensile shear tests. The study analyses the impact of various pre-treatment parameters on the bonding strength. Furthermore, it investigates the effect of sheet temperature on the bonding strength, including specimens without pre-treatment. Finally, the results of the surface treatment of hollow composite profiles are discussed.

Advances in Injection Molding and Extrusion Technology (2) 2. Technological Progress in Injection Molding Processes

Advances in Injection Molding and Extrusion Technology (2) 2. Technological Progress in Injection Molding Processes

Tensile, flexural and fatigue properties of different thermoplastic nanocomposites: experimental investigation and ANN prediction of the fatigue life

ABSTRACT Polymer-based nanocomposites have received immense attention in industrial applications as their mechanical properties are superior to traditional polymer composites. In this study, three different thermoplastic polymers, namely (a) polypropylene (PP), (b) acrylonitrile butadiene styrene (ABS), and (c) high-density polyethylene (HDPE) were used for the purpose of engineering different nanoclay-based nanocomposites. The blending of the polymer was performed by varying the nanoclay weight percentage (0.5, 1, 3, and 5 wt.%) using a twin-screw extruder. The different nanocomposites were fabricated through injection moulding process. The composites were fabricated by considering the 90 bars of injection pressure, 60 mm/s of injection speed, and 25 sec of cooling time. The effect of nanoclay inclusion on the mechanical properties of the nanocomposites was evaluated and compared with the neat polymer. The effect of nanoclay inclusion was studied through morphological analysis of the fractured tested specimens. The tensile strength and modulus of ABS-nanoclay (5%) exhibited an enhancement of 30.95% and 35%, respectively as compared to the pure ABS. Meanwhile, the fatigue life revealed an improvement of 66.01%. The tensile strength and modulus of the ABS/nanoclay (5%) were achieved as 38.6 MPa and 1.44 GPa.

Warpage evaluation and mechanical characterisation of modified polyamide-6 specimens produced by Arburg plastic freeformer

AbstractIn injection moulding and additive manufacturing processes, it is common for the final product to exhibit warpage induced by a non-uniform cooling rate after material deposition. Residual stresses can be generated in the built parts, with a volume shrinkage that leads to dimensional inaccuracy and reduced usability. This phenomenon is even amplified in the presence of semi-crystalline polymers, which still need to be more widespread among additive manufacturing processes despite their capacity to show better mechanical properties when compared to amorphous ones. This study evaluates the degree of deformation and subsequently characterises the mechanical properties of a novel modified Polyamide 6 formulation (i.e., RADILON® S X21351 NT) printed through the Arburg Plastic Freeforming process. This new proprietary formulation modified the crystallisation behaviour during cooling. The whole exothermic crystallisation peak was shifted to lower temperatures, allowing the processing envelope of the modified formulation to widen. More specifically, after preliminary polymer characterisation analyses and after evaluating the warpage, a mechanical characterisation was performed using tensile tests on specimens (dry-as-moulded and conditioned) printed with different filling strategies on the build platform (XY-0°; XY- ± 45°; XY-90°; ZX-0°). Measured mechanical properties were ultimately compared to those achieved by applying the injection moulding technique on the same non-modified material. The specimens produced through the Arburg Plastic Freeforming process showed brittle behaviour that was more marked than those obtained by injection moulding. Moreover, the infill direction and water content significantly influenced the mechanical properties of specimens.

Study of Injection Molding Process to Improve Geometrical Quality of Thick-Walled Polycarbonate Optical Lenses by Reducing Sink Marks.

This study investigates the challenges and potential of conventional injection molding for producing thick-walled optical components. The research primarily focuses on optimizing process parameters and mold design to enhance product quality. The methods include software simulations and experimental validation using polycarbonate test samples (optical lenses). Significant parameters such as melt temperature, mold temperature, injection pressure, and packing pressure were varied to assess their impact on geometric accuracy and visual properties. The results show that lower melt temperatures and higher mold temperatures significantly reduce the occurrence of dimensional defects. Additionally, the design of the gate system was found to be crucial in minimizing defects and ensuring uniform material flow. Effective packing pressure was essential in reducing volumetric shrinkage and sink marks. Furthermore, we monitored the deviation between the predicted and actual defects relative to the thickness of the sample wall. After optimization, the occurrence of obvious defects was eliminated across all sample thicknesses (lenses), and the impact of the critical defect, the sink mark on the planar side of the lens, was minimized. These findings demonstrate the substantial potential of conventional injection molding to produce high-quality thick-walled parts when these parameters are precisely controlled. This study provides valuable insights for the efficient design and manufacturing of optical components, addressing the growing demand for high-performance thick-walled plastic products.

Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

A learning-based model predictive control scheme for injection speed tracking in injection molding process

Injection molding is a pivotal industrial process renowned for its high production speed, efficiency, and automation. Controlling the motion speed of injection molding machines is a crucial factor that influences production processes, directly affecting product quality and efficiency. This paper aims to tackle the challenge of achieving optimal tracking control of injection speed in a standard class of injection molding machines (IMMs) characterized by nonlinear dynamics. To achieve this goal, we propose a learning-based model predictive control (LMPC) scheme that incorporates Gaussian process regression (GPR) to predict and model uncertainty in the injection molding process (IMP). Specifically, the scheme formulates a nonlinear tracking control problem for injection speed, utilizing a GPR-based learning residual model to capture uncertainty and provide accurate predictions. It learns the dynamics model and historical data of the IMM, automatically adjusting the injection speed according to target requirements for optimal production control. Additionally, the optimization problem is efficiently solved using a control-constrained differential dynamic programming approach. Finally, we conduct comprehensive numerical experiments to demonstrate the effectiveness and efficiency of the proposed LMPC scheme for controlling injection speed in IMP.

Innovative Approaches for Manufacturing Epoxy-Modified Wood and Cellulose Fiber Composites: A Comparison between Injection Molding and 3D Printing.

The current study focused on improving the properties of polylactic acid (PLA) for wider application in load-bearing scenarios. Various methods were explored to optimize the interaction between PLA and natural fibers, particularly wood fibers (WFs). Alkalized and epoxy-impregnated WFs were evaluated against untreated WFs and cellulose fibers in both injection molding (IM) and fused deposition modeling (FDM). FTIR analysis revealed the removal of hemicellulose and lignin in alkalized WFs and uniform epoxy curing. Addition of fibers reduced PLA's thermal stability while acting as nucleating agents. Additionally, fibers augmented the storage modulus of biocomposites, with alkalized fibers exhibiting the highest tensile modulus in IM. FDM samples with a 0° raster angle showed superior impact resistance compared to IM counterparts. Moreover, raster angle significantly influenced FDM biocomposite properties, enhancing the tensile strength and modulus of untreated WF and cellulose fibers at 0°. Although FDM did not produce alkalized WF samples, epoxy impregnation emerged as a promising method for enhancing PLA/WF composite mechanical properties in the IM process, offering valuable insights for composite material development.

Tribological Properties of PEEK and Its Composite Material under Oil Lubrication

PEEK (Poly Ether Ether Ketone) is a high-performance thermoplastic polymer with excellent mechanical, thermal and chemical stability. PEEK has good performance, and is widely used in hydraulic motors. However, there are few studies on the friction and wear properties of materials under the condition of oil lubrication with wide application. The modification of PEEK and the expansion of its application have become a hot research topic in the industry. This study focuses on the modification of the design of PEEK and explores the friction and wear characteristics of self-lubricating materials under different modification schemes. Friction and wear samples were prepared using PEEK-modification pelletizing and injection-molding processes, followed by fixed-condition friction and wear tests. The tribological mechanisms and wear properties of the materials under different modification schemes were analyzed, leading to the identification of several sets of improved reinforced materials. Experimental results demonstrate that modified materials can enhance surface tribological performance, with the best modification effect observed at an SCF filling rate of 15%. The modified PEEK material can better meet the requirements of specific applications, such as high-temperature environments, chemically aggressive environments, or applications requiring high strength and wear resistance.

A Method of Simulating the Dissolution Behavior of Physical Blowing Agents. Dissolution Behavior of Physical Blowing Agent in Low-pressure Foam Injection Molding Process (Part 2)

A Method of Simulating the Dissolution Behavior of Physical Blowing Agents. Dissolution Behavior of Physical Blowing Agent in Low-pressure Foam Injection Molding Process (Part 2)

Experimental Investigation of Processing Temperature Effect on Adhesive Bond Strength Between Engineering Thermoplastics in the Plastic Injection Molding Process

Abstract Multicomponent injection molding industry is experiencing a growth due to its ability to reduce production costs and streamline processes. However, compared to single injection, multicomponent injection molding introduces interface regions where multiple engineering polymers meet. Consequently, it is essential to comprehend and enhance the adhesive bonding strength properties of these polymers. This study investigates the adhesive bond strength of polymer–polymer multimaterial molding using two-shot bi-injection and overmolding techniques. The research also emphasizes the influence of injection molding process parameters of mold temperature and melt temperature on the adhesive bond strength of polycarbonate (PC), polycarbonate–acrylonitrile butadiene styrene (PC–ABS), acrylonitrile butadiene styrene (ABS), and styrene ethylene butadiene styrene (SEBS). Tensile strength results revealed that the bi-injection method yields the highest interface strength, approximately 10 MPa lower than the reference value for single-material hard–hard plastics. Results from overmolded samples for both injection sequences are presented, indicating that material with low melting temperature was found to be the first injected part for better adhesion strength. Empirical equations for estimating adhesion strength were derived as a function of interface temperature obtained from CAE numerical simulations and polymer glass transition temperatures. The proposed equation achieved R2 values greater than 0.96. This empirically derived equation will serve as a guide for multi-injection manufacturing processes.

Injection Moulded Part Analysis by Alignment of Simulated and Referent Part Geometry

Warpage of the injection moulded parts is one of the most common defects after demoulding, which is intensively present in thin-walled moulded parts or parts with non-uniform wall thickness. The level of the warpage can be reduced by optimizing the mould tempering system in the phase of mould design, by optimizing the injection moulding parameters and recently by optimizing the shape of the mould cavity elements geometry – so-called inverse contouring. Computer simulation of injection moulding process can show the level of moulded part warpage after ejection from the mould, but it will not allow detailed measuring of critical moulded part measures deviations. Detailed analysis and prediction of the critical moulded part dimensions can be performed by aligning of the deformed moulded part design obtained with computer simulation, with moulded part reference CAD geometry. This paper shows the main steps and an example of the geometry aligning process for a specific thermoplastic part.