Development and Validation of a 1D Dynamic Model of an Injection Moulding Process and Design of a Model-Based Nozzle Pressure Controller

A 1D model describing the dynamics of an injection moulding machine and the injection process is presented. The model describes an injection cylinder actuated by a dual-pump electro-hydraulic speed-variable drive and the filling, holding and cooling phases of the injection moulding process utilising amorphous polymers. The model is suggested as the foundation for the design of model-based pressure controllers of, e.g., the nozzle pressure. The focus is on using material, mould and machine properties to construct the model, making it possible to analyse and design the dynamic system prior to manufacturing hardware or conducting experiments. Both the presented model and the developed controller show good agreement with experimental results. The proposed method is general in nature and enables the design, analysis and evaluation of the machine, material and mould dynamics for controller design based solely on the physical properties of the system.

Embedding a surface acoustic wave sensor and venting into a metal additively manufactured injection mould tool for targeted temperature monitoring

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) "stop-and-go" approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III

This study aimed to improve the injection molding quality of LSR material lenses by optimizing the process parameters. To achieve this goal, we employed the population-based optimization algorithm NSGA-III, which can simultaneously optimize multiple objective functions and identify an equilibrium point among them, thereby reducing the time required to find the optimal process parameters. We utilized analysis software to simulate the injection molding process of LSR material lenses, with a specific focus on examining the relationship between tie bar elongation and the optimized process parameters. During the study, we intentionally varied key process parameters, including the melt temperature, holding pressure, and holding time, to analyze their impact on the residual stress of the final product. In order to investigate the intricate relationship between the tie bar yield, injection molding process parameters, and lens residual stress, we installed strain sensors on the tie bar to continuously monitor changes in clamping force throughout the injection molding process. The experimental results showed that both the tie bar force and mold cavity pressure exerted significant influence on residual stresses. By applying the NSGA-III algorithm for optimization, we successfully determined the optimal process parameters, which included a melt temperature of 34.92 °C, a holding pressure of 33.97 MPa, and a holding time of 9.96 s. In comparison to the initially recommended process parameters during the design phase, the optimized parameters led to reductions of 12.98% in clamping force and 47.14% in residual stress. Furthermore, the average transmittance of the actual product remained within the range of 95-98%. In summary, this approach not only enables the prediction of the lens's residual stress trends based on the tie bar elongation, but also leads to a substantial enhancement of lens quality, characterized by reduced residual stress and improved transmittance through the optimization of process parameters. This methodology can serve as a valuable guide for optimizing real-world injection molding processes.

Feasibility assessment of injection molding online monitoring based on oil pressure/nozzle pressure/cavity pressure

This study produced a box-like product through injection molding, and pressure sensors were installed at the nozzle in the injection molding machine and mold cavity. Injection molding experiments were conducted with various process parameters to understand the correlations between the pressure of the cavity, nozzle, and oil pump. A high correlation (R > 0.95) between oil pressure and nozzle pressure could be found in the study. The oil pressure in the injection molding machine could fully describe the pressure variation of the molten plastic at the nozzle. However, the correlation between nozzle pressure and cavity pressure was slightly reduced due to changes in injection speed, melting temperature, and packing time. Thus, a suitable molding window can improve the correlation between oil pressure, nozzle pressure, and cavity pressure. This study provides a correlation between the three pressures and a reference for the future selection of process variables, an essential pretreatment for online monitoring.

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines

Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. In this paper, an optimal tracking injection velocity control problem arising in a typical IMM is studied. An effective hybrid intelligent control approach with less computing resources for real-time implementation based on the deep learning (DL) method to mimic the classical model predictive control rule is developed to deal with the tracking control of the injection speed. The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. The benefits of this approach over the conventional optimization method are illustrated through simulation results, which show that the convergent DL-based controller can effectively avoid the complex calculation in the control process of IMMs and meet the requirements of more robustness and resist environmental uncertainty to a certain level and can be potentially implemented in embedded hardware much more efficiently and conveniently with a smaller memory footprint and faster computation time.

Online detection of residual stress near the gate using cavity pressure for injection molding

Residual stress negatively affects the quality of optical products and is a difficult problem to solve. Regulating process parameters is a critical element in quality control. Therefore, the online monitoring of critical process variables is necessary for stabilizing product quality and reducing manufacturing cost. This study proposes a method for the online monitoring of residual stress near the gate of a mold using multiple pressure sensors to measure process variables and verifies the feasibility of online monitoring. The study found that residual stress near the gate could not be accurately measured with only a single sensor because measurements were susceptible to interference with process parameters. When a single sensor was used to measure process variables, correlation coefficients of between 0.48 and 0.59 were obtained, and when multiple sensors were used, correlation coefficients of between 0.80 and 0.92 were obtained, which indicated strong correlation. Therefore, the online monitoring of residual stress near the gate is feasible. This monitoring method can provide a new concept of online monitoring technology for the injection molding of optical products.

Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

Quality prediction and control of thin-walled shell injection molding based on GWO-PSO, ACO-BP, and NSGA-II

ECG recorders are precision medical devices, but their thin-walled shells are susceptible to warpage and shrinkage during injection molding production due to the injection molding process, which greatly shortens their service life. To address this problem, a multiobjective optimization method for injection molding process parameters based on a combination of a BP neural network model optimized by an ant colony algorithm (ACO-BP) and an improved non-dominated sorting genetic algorithm (NSGA-II) is proposed. The study takes the warpage deformation amount and volume shrinkage rate of the plastic part as the optimization objectives, and the melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time as the design variables. However, for BP neural networks, it is crucial to choose an appropriate number of hidden layer neurons, so the particle swarm algorithm combined with the grey wolf algorithm (GWO-PSO) is used to solve for the optimal number of hidden layer neurons. Firstly, the number of hidden layer neurons of the BP network model was solved based on the samples obtained from the Box–Behnken experimental design and the GWO-PSO algorithm, and the ACO-BP algorithm was used to build the prediction models for warpage and volume shrinkage, respectively, and then combined with NSGA-II for global optimisation. The pareto optimal solution set was subjected to CRITIC analysis and the optimal process parameters were finally obtained, with a minimum warpage of 0.3293 mm and minimum volume shrinkage of 4.993%, a reduction of 8.93 and 6.95% respectively compared to the pre-optimisation period. At the same time, injection molding tests were carried out on the optimum process parameters, and it was found that the molding quality of the plastic parts was better and met the actual production requirements through measurement. The research in this paper provides a theoretical basis for further improving the quality defects of the thin-walled injection molded parts.

Analysis of the Warpage Phenomenon of Micro-Sized Parts with Precision Injection Molding by Experiment, Numerical Simulation, and Grey Theory

In this study, we determined the effects of design and processing parameters of precision injection molding (PIM) to minimize warpage phenomena of micro-sized parts using various plastics (polyoxymethylene (POM), acrylonitrile-butadiene-styrene (ABS), polypropylene (PP), polyamide (PA), and ABS+ polycarbonate (PC)). We applied a numerical simulation (Moldflow) to determine the runner’s balance in multi-cavities of the micro-sized part and simulate the warpage phenomenon of micro-parts with PIM. We used simulation data to fabricate a steel mold by computer numerical control (CNC) machining. In this, we study manufactured a micro-sized part and measured its warpage value using various PIM process parameters (melt temperature, mold temperature, injection pressure, and filling time). In order to obtain optimal results (i.e., minimum warpage), we employed the Taguchi method and grey theory to discern the influence of each process parameter on PIM. Finally, we determined that the most significant PIM process parameter influencing the warpage phenomenon of micro-sized parts was the mold temperature, regardless of whether in terms of the experimental results, numerical simulations, or grey theory. The PA material had the most suitable properties for application for micro-sized parts, regardless of whether in terms of experimental results, numerical simulations, or grey theory for PIM. This study also illustrates that micro-sized parts can be fabricated by PIM without the use of micro-injection molding, and we determined that the mold temperature required for molding does not need to be higher than the glass-transition temperature of the material.

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed.

Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model

The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

Application of Physical Methods for the Detection of a Thermally Degraded Recycled Material in Plastic Parts Made of Polypropylene Copolymer

The paper deals with the possibility of applying physical methods to detect a thermally degraded recycled material in plastic parts made of polypropylene. Standard methods of evaluating the mechanical properties of the material under static tensile and bending stress, as well as under dynamic impact stress using the Charpy method, were used for the experimental measurements. The rheological properties of materials were monitored using a method involving measuring the melt flow index, while their thermal properties and oxidative stability were monitored using differential scanning calorimetry. Based on the methods used, it can be clearly stated that the most suitable technique for detecting thermally degraded recycled material in polypropylene is the method involving establishing the melt flow index. The bending test seems to be the most suitable method for detecting recycled material by measuring the material’s mechanical properties. Similarly to the melt volume flow rate (MVR) method, it was possible to unambiguously detect the presence of even a small amount of recycled material in the whole from measuring the material’s bending properties. It is clear from the results that in the short term, there may be no change in the useful properties of the parts, but in the long term the presence of degraded recycled material will have adverse consequences on their lifespan.

Robust parameter search for IC tray injection molding using regrind resin

Quality consistency is essential in maximizing the productivity rate of the injection molding process and minimizing the production cost. The quality consistency problem is particularly acute in the case of injection molding processes performed using regrind resin, for which the rheological properties are less uniform and more unpredictable than those of virgin material. Accordingly, the present study proposes a two-stage approach for optimizing the injection molding process parameters in such a way as to achieve a consistent molding quality over repeated injection molding cycles. In the first stage, the values of the injection speed/pressure, velocity-to-pressure (V/P) switchover point, and packing pressure are individually determined based on an inspection of the cavity pressure profile and machine parameters provided by the injection molding machine controller. In the second stage, a robust parametric search method based on a first-order regression model is employed to determine the optimal combination of the process parameter settings. Using an Integrated Circuit (IC) tray fabricated from regrind resin for illustration purposes, the results confirm that the proposed method overcomes the problem of small variations in the melt quality and therefore provides an effective technique for improving the yield rate and quality of the continuous mass production.

External Gas-Assisted Mold Temperature Control Improves Weld Line Quality in the Injection Molding Process

Simulations and experiments were conducted with gas temperatures of 200–400 °C to investigate the impact of external gas-assisted mold temperature control (Ex-GMTC) on the quality of weld line of molding products. In the heating step, the heating rate was 19.6 °C/s from 30 to 128.5 °C in the first 5 s in a 400 °C gas environment. When applied to heating the weld line area of an injection mold, Ex-GMTC improved the appearance of the weld line when the cavity temperature was preheated to 150 °C. For the tensile strength test, a melt flow simulation comparing the packing pressure of different mesh thicknesses revealed that Ex-GMTC helped maintain a high pressure in the weld line area in different packing periods. This was verified by an experiment where Ex-GMTC was applied with 400 °C gas to change the mesh area temperature. The result indicated that an increase in the weld line area temperature from 60 to 180 °C improves the tensile strength of all mesh thicknesses, which was more pronounced with thinner parts, especially at 0.4 mm. The simulations revealed that high temperature is concentrated in the weld line area of the cavity surface, thus reducing the energy wasted during heating.