Plastic Injection Research Articles

Mechanical and thermal properties of short banana fiber reinforced polyoxymethylene composite materials dependent on alkali treatment

Abstract The present study aimed to develop a recyclable composite material based on a natural product, namely banana fiber, instead of synthetic reinforcement elements such as glass fiber or carbon fiber in thermoplastic matrix composites employed in many aspects of daily life. Polyoxymethylene (POM) thermoplastic was used as the matrix material. 3 %, 6 %, and 9 % by weight short natural banana fiber was used as reinforcement. Furthermore, the study improved the mechanical and thermal properties of the composite material with chemical improvements based on alkali treatment of banana fiber. Banana fiber and POM was initially mixed mechanically and then extruded at temperatures between 170 and 190 °C to obtain a homogeneous mixture. The mixture obtained by extrusion was initially cooled in the cooling pool and then passed through the crusher to obtain granules. These granules were pressed in plastic injection molds to obtain standard test samples. The ratio of reinforcement material and the variations in mechanical and thermal properties of the composites induced by the alkali treatment are presented in figures and tables. Furthermore, the changes in cross-sectional material images were examined with electron microscopy scans. The tensile and flexural strength of the material improved with alkali treatment.

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector.

Analysis of Plastic Injection Mold Design and Cooling System

Plastik malzeme, otomotivden beyaz eşyaya, kırtasiye malzemelerinden oyuncaklara, mutfak eşyalarından ofis malzemelerine kadar her sektörde yerini almıştır. Plastik enjeksiyon yöntemi, plastik parça üretiminde en çok kullanılan imalat yöntemidir. Üretimi talep edilen plastik malzemeler için, plastik enjeksiyon kalıpları tasarlanmaktadır. Bu kalıplar tasarlanırken, ilk aşamada ürün tasarımı göz önüne alınır. Üretime uygun, kısa sürede ve kaliteli bir imalat yapmak için en önemli faktör tasarlanan ürünün, üretilebilir olmasıdır.
 Bu çalışmada plastik bir kırtasiye-oyuncak sektörü diyebileceğimiz bir sulu boya paletinin ürün tasarımı ve kalıp tasarımı aşama aşama incelenecektir. Ürün, kalıplamaya uygun hale getirilip, kalıp tasarımı gerçekleştirilmiştir. Tasarımı biten ürün, daha üretimi yapılmadan yolluk ve soğutma sistemlerinde karşımıza çıkabilecek olası hataları gözlemlenmiştir. Yapılan analize göre yolluk giriş yerinin en iyi ürün merkezinden olacağı ama maliyeti arttırdığı tespit edilip soğuk yolluk sistemi kullanılarak bir tasarım gerçekleştirilmiştir. Hava birikecek yerler tespit edilmiştir, bu bölgelere hava çıkışını sağlayacak cepler kullanılması önerilmiştir. Soğutma kalitesiyle ilgili yapılan analiz ile ürünün et kalınlığının homojen olduğu ve erken donma ihtimalinin düşük olduğu tespit edilmiştir. Bu yüzden et kalınlığına müdahale edilmesine gerek görülmemiştir.

A Global Approach to Modeling Injection Molding.

A problem of modeling plastic injection forming (molding) is presented, including both the plasticizing system of the injection-forming machine and the mold. When modeling the plastic flow in the mold, the input quantities are essentially unknown, e.g., the plastic melt temperature. Thus, a comprehensive (global) model of the injection-forming process is needed for the flow in the plasticizing system and in the mold. The process output quantities from the plasticizing system will be the input quantities for the mold. When modeling the plastic flow in the injection-forming machine, a comprehensive approach should be applied to consider the solid material conveying, material plasticizing, and the material melt flow. The model of material plasticizing is a basis for building such global models. In this research, the effect of the flow (including plasticizing) in the injection-forming machine on the flow in the mold is studied by simulation (using Moldex3D 2023R3OR 64-bit software) and experimentation. These studies are carried out for the injection forming of selected material using a specialized spiral mold. Simulations performed with the use of Moldex3D software for the plasticizing system significantly improved the accuracy of the simulation of the flow in the mold. However, the best results were obtained using experimental data (plastic melt temperature) as input quantities for mold filling simulations. The novel concepts of injection-forming process modeling based on our previous experimentations are also discussed.

Three-Dimensional Printing Conformal Cooling with Structural Lattices for Plastic Injection Molding

Three-Dimensional Printing Conformal Cooling with Structural Lattices for Plastic Injection Molding

Design of a Cavity Pressure Monitoring System for Plastic Injection Molding

This work aims to create an instrument that allows the measurement of cavity pressure during plastic injection molding. The designed device is intended to contribute to the equipment inventory of the Polymer Technology Laboratory at Sapientia EMTE, Faculty Târgu Mureș. The design criteria were the ease of mounting and use. The system was implemented with two load cells, two HX711 amplifiers, an ESP32-based microcontroller and a corresponding computer user interface. The user interface was developed using National Instruments LabVIEW program and finalized as a stand-alone application. During the design steps, attention was also paid to the practical applicability of the system.

Integrating Machine Learning with Machine Parameters to Predict Plastic Part Quality in Injection Moulding

The plastic injection moulding process is a critical manufacturing technique renowned for its high productivity, cost-effectiveness, and ability to produce intricate plastic components for various industries including medical and aerospace. The quality of the manufactured parts is influenced by several parameters, such as machine settings and mould characteristics, particularly thermal aspects. This paper specifically investigates the influence of primary machine parameters on part quality, excluding considerations of time, mould features, and cooling channel geometries. By focusing on the machine parameters and employing advanced machine learning methods, a comprehensive understanding is developed on how these factors can be utilised to predict the quality of the parts produced. The findings provide valuable insights into optimising the injection moulding process to enhance product quality and consistency.

In-line parameters optimization of plastic injection molding process in the context of disrupted supply chains

Recent social, healthcare, and geopolitical events have caused major disruptions to global supply chains, leading to significant negative consequences on the availability and cost of raw materials for various industries. The injection molding sector has been significantly impacted, with supply chain disruptions resulting in reduced availability of plastic granules and overall increases in raw material costs. To overcome these challenges, plastic component manufacturers need to optimize their material validation process to make the most efficient use of available materials. This article proposes a fully automatic solution for optimizing plastic component production in the context of disrupted supply chains, leveraging a Cyber-Physical Production Systems (CPPS) composed by a Cobot, an automatic guided vehicle, an injection molding machine, and a test bench for plastic components. The paper details the system architecture, implementation, and experimental results, demonstrating the effectiveness of the proposed approach. The results show that the CPS-enabled manufacturing system can successfully adapt to changes in material availability, increase production efficiency, and reduce waste. The proposed approach offers a promising solution for the challenges posed by disrupted supply chains in various manufacturing contexts and with different granulates and products.

Effects of Plastic Injection Moulding Process Parameters on Material Elastic Modulus on Complex Geometry and Parameter Optimization for Getting Maximum Stiffness

Plastic injection is widely used in white goods sector in case of highly production rates, low transformation costs and recyclability of products. There are many parameters affecting on the quality, dimensional stability and mechanical strength in plastic injection process. In washing machine tubs which are exposed to dynamic loads during spinning, it is necessary to optimize process parameters. In this research; packing pressure, packing transition point, packing time and injection time is optimized by using Box Behnken methodology via Moldlfow elastic modulus outputs and structural analyses results. Optimum process parameters are obtained to achieve best material orientation.

Injection mold improvement for a jar for cosmetic use through CNC machining

This project shows the improvement of a three-gram capacity jar for cosmetic use, manufactured by the company Ingeniería, Pailería Industrial y Mantenimiento S.A. de C.V. (IPIMSA). The jar for cosmetic use showed a leak between the jar and the lid, by means of the leak test, in a vacuum machine. To determine the root cause, an Ishikawa diagram is used, later, it was analyzed that the 110 T horizontal injection machine (Van Dorn) is in perfect condition, the problem was identified when disassembling the plastic injection mold, since the plate for the string of the jar was machined with the concentricity offset by one millimeter, causing the walls of the string of the jar to be uneven and thin, which is why the objective of this project is to improve the machining of the string generating plate of the jar. Jar for cosmetic use, to meet the leak test by the customer. For the machining, the Haas VF2 CNC machining center with a resolution of 0.001 mm, from the Universidad Tecnológica del Valle de Toluca, was used.

Machine learning-aided cooling profile prediction in plastic injection molding

Machine learning-aided cooling profile prediction in plastic injection molding

A Study on the Temperature Optimization of Mold and Melt Using Design of Experiments for Children's Chair

The quality of the finished product is affected by a number of factors during the plastic injection molding process. Two crucial process variables in the creation of products are melted and mold temperature. The study uses the Design of Experiments tool in Autodesk Moldflow to look at the impact of melt and mold temperatures on injection molding technology. Analytical items are specifically made of polypropylene (PP) using kids' chair mold. According to simulation analysis results, there is a remarkable effect of the melt temperature on both time at end of packing as well as deflection in the range of the analytical temperature at tmold of [40, 80]°C and tmelt of [180, 220]°C. Melt temperature also shows a notable influence not only on deflection but also on sink mark depth and volumetric shrinkage, along with the criteria to evaluate the expense of a product (time at end of packing, total part weight).

IMPLEMENTASI PENGGUNAAN METODE SHARED STORAGE DALAM PERBAIKAN TATA LETAK PENYIMPANAN MOLD DI PT DYNAPLAST CIBITUNG

In the development of industry and technology, warehouse layout issues have become crucial in manufacturing industries, including PT Dynaplast Cibitung, which specializes in plastic injection production using molds. The current placement of molds in the storage warehouse is suboptimal and hinders identification during the transfer to the machines. This research aims to improve the mold storage layout for better organization and easier identification during machine transfers. It adopts the shared storage method, prioritizing the storage of fast-moving items closest to the entry and exit points. Molds are classified into fast-moving, medium-moving, and slow-moving categories, with fast-moving molds placed nearest to the entry and exit points. Furthermore, mold placement also considers the distance to the machines to minimize transfer distances.

A hybrid optimization approach for intelligent manufacturing in plastic injection molding by using artificial neural network and genetic algorithm

This study presents a novel hybrid optimization approach for intelligent manufacturing in plastic injection molding (PIM). It focuses on globally optimizing process parameters to ensure high-quality products while reducing cycle time, material waste, and energy consumption. The method combines a backpropagation neural network (BPNN) with a genetic algorithm (GA) and employs a multi-objective optimization model based on design of experiments (DoE). A BP artificial neural network captures the relationship between optimization goals and process parameters. Leveraging the genetic algorithm, it effectively optimizes process parameters for achieving global optimization goals. The case study involves a polypropylene product, considering dimensional deviation, weight, cycle time, and energy consumption during the PIM cycle. Design variables include melt temperature, injection velocity, injection pressure, commutation position, holding pressure, holding time, and cooling time. The results demonstrate that this approach efficiently adjusts process parameters to meet quality standards, significantly reducing raw material consumption (2%), cycle time (12%), and energy consumption (16%). This offers substantial benefits for companies in highly competitive markets demanding swift adoption of smart production methods.

A Numerical Simulation and Multi-objective Optimization for the Plastic Injection Molding of a Centrifugal Pump Casing

Plastic pumps are used to efficiently move liquids within aquaculture systems and seafood processing facilities. Commonly, the centrifugal pump casing is fabricated by plastic injection molding technology. In the plastic injection molding process, the control parameters are essential to the success of plastic molding product fabrication. This study aims to optimize process parameters for the quality of centrifugal pump casing during plastic injection molding using a combination of response surface methodology (RSM) and the NSGA-II method. Firstly, the L27 orthogonal array design of the experiment method is used, which has four input variables, including plastic temperature, mold temperature, cooling time, and injection time (filling time and packing time). The output responses of the injection molding process to be evaluated are the warpage and shrinkage deformation of the molded product. After obtaining data, the RSM model is applied to describe the relationship between the input and output variables. Finally, multi-objective optimization uses the NSGA-II method for the best product quality. The results show that the optimized parameters for injection molding of the centrifugal pump casing are a plastic temperature of 200°C, mold temperature of 65°C, cooling time of 25s, and injection time of 10s with warpage of 0.868 mm, and shrinkage of 10.104mm.

Comparative analysis of artificial neural network model and analysis of variance for predicting defect formation in plastic injection moulding processes

This study investigates the impact of plastic injection moulding process parameters on overflow defect formation. Experiments were conducted using a Taguchi L27 orthogonal array design. Multilayer Perceptron (MLP) artificial neural networks is explored and compared with ANOVA predictions. To assess model performance, the Root Mean Squared Error (RMSE) and the coefficient of determination (R2) is applied. The study considered temperature, speed, pressure, and packing force when constructing the MLP model using the back-propagation algorithm in Python. Results show that among the configured MLP neural networks, the 3-layer MLP architecture with sigmoid activation functions in hidden layers and a linear function in the output layer exhibited the lowest prediction error and the highest coefficient of determination. Comparative analysis reveals that the MLP neural network outperforms the ANOVA model, indicating superior prediction accuracy. The predicted outcomes from the ANN align well with experimental values, demonstrating the effectiveness of the ANN model in forecasting defect formation under specific process conditions. This research sheds light on the significance of process parameters and showcases the potential of MLP neural networks as a valuable tool in predicting and mitigating overflow defects in plastic injection moulding.

Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process

Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant.

Design and analysis of a 4-axis cartesian robot for unloading plastic injection machines in industrial applications

In both industrial and educational settings, efficient handling of products from Plastic Injection Machines is crucial for precise and stable system operation. Enhancements in the design and production processes, achieved through the implementation of a Four Axis (4D) BOM type Cartesian System, lead to significant improvements in cost-effectiveness and product quality. In this study focuses on optimizing system operations, including rotational movements and the operation of the vertical cylinder through forward-backward movements. Finite element analysis is employed to investigate potential issues arising from shape changes in the mechanical structure due to dynamic loads on the plate joint connections. By addressing these concerns during the design phase through simulation, mechanical structure errors are eliminated, resulting in improved system performance.

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human–system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact.

Strength and manufacturability enhancement of a composite automotive component via an integrated finite element/artificial neural network multi-objective optimization approach

This study addresses the enhancement of an injection-molded fiber-reinforced plastic / metal hybrid automotive structure and its plastic injection molding process through the integration of the finite element method, artificial intelligence and evolutionary search methods. Experiments are conducted to validate the finite element models. The orthogonal array and Latin hypercube methods are employed to generate a database via finite element analysis. The database is then used to train artificial neural networks that accurately evaluate component distortion, manufacturing time, and structural strength. A genetic optimization algorithm is applied to identify optimal process parameters. The procedure was demonstrated to simultaneously reduce product warpage and manufacturing time by 10 and 62 %, respectively, when compared with the reference manufacturing process while strength is kept above the required levels with a reduced number of required data points. A more in-depth investigation into the causes of strength variation and deformation is also provided. The results contribute to the advance of robust composite automotive structures with superior quality, manufactured through efficient processes.