A Review on Machine Learning Models in Injection Molding Machines

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

Design and Automation of a vertical electrospinning system for manufacturing nanofibers

The objective of this research consists of the design, construction and automation of the electrospinning mechatronic system to obtain nanofibers. As a first stage, the structure of the electrospinning mechatronic system and the distribution, injection and manifold system were designed and built. In the second stage, the open-loop control system was outlined and implemented. It is made up of: control, isolation stage, and the plant. In the first element, the LabView interface and ATMega2560 microcontroller were used to manipulate the variables of the injection speed and distribution of the solution, the speed of the nanofiber collector and the height between the capillary tube and the collector, the magnitude of the temperature and humidity from the environment, also, the graphic interface was developed, the second element consists of isolating the control and power stage in addition to amplifying the command signals and enabling the correction elements, the third element receiving the signals from the power stage to perform the action and produce a change in the controlled variables in the process. With this prototype, it is intended to obtain nanofibers from different polymer solutions for use in the area of catalysis and biomaterials.

CFD Modeling of Multiphase Flow in an SKS Furnace with New Tuyere Arrangements

There has been a great deal of focus on the optimization of tuyere arrangements in SKS bottom blown copper smelting furnaces since the last decade, as the improved furnace operation efficiency of SKS technology has potential that cannot be ignored. New –x + 0 + x deg tuyere arrangements with 14 tuyeres are proposed in this research paper. Using a previously verified numerical model, CFD tests on the velocity distribution and wall shear stress for scaled-down SKS furnace models were conducted, with a constant total volumetric gas flow rate, and different operating parameters and furnace cross-section geometries. The results indicate that, at a relatively low gas injection speed compared with the previously optimized tuyere arrangement, although the –x +0 +x deg tuyere arrangements are unable to supply enhanced agitation in the typical round furnaces, they achieve better performance in elliptical furnaces. At a comparatively higher gas injection speed, the – x + 0 + x deg tuyere arrangements can improve the agitation performance in a round furnace while maintaining an acceptable wall shear stress on the bottom and side wall. The agitation enhancement with the − x +0 +x deg tuyere arrangements can essentially be attributed to stronger interactions between bubble plumes and furnace side walls. To further exploit the advantages of the new tuyere arrangements, an optimized tuyere angle was confirmed by a full-scale furnace model simulation.

Effects of Injection Molding Parameters on Properties of Insert-Injection Molded Polypropylene Single-Polymer Composites

The reinforcement and matrix of a polymer material can be composited into a single polymer composite (SPC), which is light weight, high strength, and has easy recyclability. The insert injection molding process can be used to realize the multiple production of SPC products with a short cycle time and wide processing temperature window. However, injection molding is a very complicated process; the influence of several important parameters should be determined to help in the future tailoring of SPCs to specific applications. The effects of varying barrel temperature, injection pressure, injection speed, and holding time on the properties of the insert-injection molded polypropylene (PP) SPC parts were investigated. It was found that the sample weight and tensile properties of the PP SPCs varied in different rules with the variations of these four parameters. The barrel temperature has a significant effect, followed by the holding time and injection pressure. Suitable parameter values should be determined for enhanced mechanical properties. Based on the tensile strength, a barrel temperature of 260 °C, an injection pressure of 127.6 MPa, an injection speed of 0.18 m/s, and a holding time of 60 s were determined as the optimum processing conditions. The best tensile strength and peel strength were up to 120 MPa and 19.44 N/cm, respectively.

Flush Flow Behaviour Affected by the Morphology of Intravascular Endoscope: A Numerical Simulation and Experimental Study

Background: Whilst intravascular endoscopy can be used to identify lesions and assess the deployment of endovascular devices, it requires temporary blockage of the local blood flow during observation, posing a serious risk of ischaemia.Objective: To aid the design of a novel flow-blockage-free intravascular endoscope, we explored changes in the haemodynamic behaviour of the flush flow with respect to the flow injection speed and the system design.Methods: We first constructed the computational models for three candidate endoscope designs (i.e., Model A, B, and C). Using each of the three endoscopes, flow patterns in the target vessels (straight, bent, and twisted) under three different sets of boundary conditions (i.e., injection speed of the flush flow and the background blood flowrate) were then resolved through use of computational fluid dynamics and in vitro flow experiments. The design of endoscope and its optimal operating condition were evaluated in terms of the volume fraction within the vascular segment of interest, as well as the percentage of high-volume-fraction area (PHVFA) corresponding to three cross-sectional planes distal to the microcatheter tip.Results: With a mild narrowing at the endoscope neck, Model B exhibited the highest PHVFA, irrespective of location of the cross-sectional plane, compared with Models A and C which, respectively, had no narrowing and a moderate narrowing. The greatest difference in the PHVFA between the three models was observed on the cross-sectional plane 2 mm distal to the tip of the microcatheter (Model B: 33% vs. Model A: 18%). The background blood flowrate was found to have a strong impact on the resulting volume fraction of the flush flow close to the vascular wall, with the greatest difference being 44% (Model A).Conclusion: We found that the haemodynamic performance of endoscope Model B outperformed that of Models A and C, as it generated a flush flow that occupied the largest volume within the vascular segment of interest, suggesting that the endoscope design with a diameter narrowing of 30% at the endoscope neck might yield images of a better quality.

Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the Z-axis, shrinkage along the X-axis, and shrinkage along the Y-axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.

Optimization of injection parameters for temperature time response gel plugging agent

Aiming at the temperature time response gel plugging agent developed by Daqing exploration and development institute. In this paper, the injection volume, injection speed and injection concentration are optimized. The experimental results show that: the temperature time response gel can achieve the best plugging effect under the injection volume 0.1PV, injection concentration 1000mg/L and injection speed 0.6ml/min. In the subsequent water injection stage, the conductivity of high permeability layer decreases from 72.7% to 0.7%, that of low permeability layer increases from 4.1% to 14.3%, and that of medium permeability layer increases from 23.2% to 85%.

Transfer and Optimisation of Injection Moulding Manufacture of Medical Devices Using Scientific Moulding Principles

Scientific moulding, also known as decoupled injection moulding, is a production methodology used to develop and determine robust moulding processes resilient to fluctuations caused by variation in temperature and viscosity. Scientific moulding relies on the meticulous collection of data from the manufacturing process, especially inputs of time (fill, pack/hold), temperature (melt, barrel, tool), and pressure (injection, hold, etc.). This publication presents a use case where scientific moulding was used to enable the transfer and optimisation of an injection moulding process from an Arburg 221M injection moulding machine to an Arburg 375 V model. The part was an endovascular aneurysm repair dilator device where a polypropylene hub was moulded over a high-density polyethylene dilator insert. Upon transfer, multiple studies were carried out, including material rheology study during injection, gate freeze study, cavity balance of the moulding tool, and pressure loss analysis. A design of experiments was developed and carried out on the process with a variety of effects and responses. The developed process cycle time was compared to that achieved theoretically using mathematical modelling and the original process cycle time. The studies resulted in the identification of optimum parameters for injection speed, holding time, holding pressure, cooling time, and mould temperature. The process was verified by completing a 32-shot study and recording part weights and dimensional measurements to confirm repeatability and consistency of the process. The output from the study was a reduction in cycle time by 12.05 s from the original process. A cycle time of 47.28 s was theoretically calculated for the process, which is within 6.6% of the practical experiment results (44.15 s).

EFFECTS OF INJECTION SPEED ON MECHANICAL PROPERTIES IN HIGH-PRESSURE DIE CASTING OF MG-RE ALLOY

In this study, in order to clarify the unknown physical properties of the Mg-Al-Th-RE alloy, the relationship between the injection conditions and the internal porosities and the mechanical properties exerted by the solidification microstructure were investigated. The obtained cast samples were investigated using X-ray CT internal measurements, tensile tests, Vickers hardness tests and solidification microstructure observations. The flow simulation and the X-ray CT analysis results showed that the porosity volume increased as the injection speed increases. The higher injection speed also affected the metal microstructure to become denser, which leads to a higher material strength and hardness. The eutectic phases quickly formed because of the shorter filled and cooled time. Therefore, the growth of the primary phase α-Mg was suppressed. On the other hand, it was considered that the material strength and hardness were greatly reduced by the coarse primary phase.

Fabrication of Poly(Vinylidene Fluoride)/Graphene Nano-Composite Micro-Parts with Increased β-Phase and Enhanced Toughness via Micro-Injection Molding

Based on poly(vinylidene fluoride)/graphene (PVDF/GP) nano-composite powder, with high β-phase content (>90%), prepared on our self-designed pan-mill mechanochemical reactor, the micro-injection molding of PVDF/GP composite was successfully realized and micro-parts with good replication and dimensional stability were achieved. The filling behaviors and the structure evolution of the composite during the extremely narrow channel of the micro-injection molding were systematically studied. In contrast to conventional injection molding, the extremely high injection speed and small cavity of micro-injection molding produced a high shear force and cooling rate, leading to the obvious “skin-core” structure of the micro-parts and the orientation of both PVDF and GP in the shear layer, thus, endowing the micro-parts with a higher melting point and crystallinity and also inducing the transformation of more α-phase PVDF to β-phase. At the injection speed of 500 mm/s, the β-phase PVDF in the micro-part was 78%, almost two times of that in the macro-part, which was beneficial to improve the dielectric properties. The micro-part had the higher tensile strength (57.6 MPa) and elongation at break (53.6%) than those of the macro-part, due to its increased crystallinity and β-phase content.