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Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the present study, electrostatic powder coating waste was recycled and employed to reinforce high‐density polyethylene. The hydrolysis method was preferred to modify the powder coating waste of various systems, and they were mixed with high‐density polyethylene at various weight ratios. The mixtures were prepared in a mechanical mixer and then in an extruder to obtain a homogeneous mixture. This homogeneous structure was pressed into molds with a plastic injection device to obtain standard tensile samples. Mechanical and thermal tests were conducted on the samples. It was determined that the tensile stress of the sample decreased with the increase in filler content. The changes in sample tensile strength were investigated after the samples were aged. Three‐point bending strength of the samples was also investigated and it was observed that they became more resistant to vertical loads as the filler content increased. The Izod impact strength of the samples differed based on the filler type and weight ratio. Furthermore, mass loss in the produced mixtures was determined with thermogravimetric analysis. The matrix and filler bonding mechanisms were observed under an electron microscope. [ABSTRACT FROM AUTHOR]

Optimizing the design of the conformal cooling channel can increase the cooling rate of injection mold. The aim of this study was the problem of low cooling efficiency of injection mold for deep-cavity plastic parts under the conventional cooling channel. Based on the analysis of the heat transfer principle of the injection mold, a mathematical description of the cooling time of the conformal cooling channel was made. By designing orthogonal experiments and using simulation methods in the Autodesk Moldflow 2019 software, with the minimum cooling time of the mold as the optimization goal, experimental optimization was carried out for the three design variables of the channel, thereby obtaining the optimal combination of design variables for the conformal cooling channel. The conformal cooling channel layout was innovatively designed, and through computer simulation experiments, it was concluded that the conformal cooling channel adopted a series flat-head layout, which has the shortest cooling time and the fastest cooling rate. Metal additive manufacturing technology was used to complete the manufacturing of the mold insert with the conformal cooling channel. After the trial production of the conformal cooling injection mold, the molding cycle was obviously shortened, and the injection molding production efficiency was significantly improved. [ABSTRACT FROM AUTHOR]

Clamping precision as a key factor of precision injection molding of high-quality plastic parts is restricted by low-precision clamping systems. Herein, a novel clamping method capable of sensing mold misalignment and self-adjusting mold position and attitude through floating structures is proposed to eliminate the restriction of clamping system, thereby achieving high clamping precision. The innovative clamping method possesses prestress steel wire-winding frames and rubber capsules filled with hydraulic oil, which ensures the floating structures work. Meanwhile, the hydraulic oil provides isostatic pressure to clamp the mold. The working space of the floating structures and the loading of the prestress steel wire-winding frames have been modeled to confirm their feasibility in the self-adaptive clamping method. Based on the modeling activities, a prototype injection molding machine was developed to evaluate the clamping precision. The experimental results indicate that the parallelism between the mold plates is 0.031 mm, far superior to that in China Machinery Industry Standards, and the uneven clamping force rate on mold cavity surface is 1.3 %. Moreover, the clamping force repeatability is only 0.14 %, superior to toggle and two-platen clamping systems. This method offers a new chance for the development of precision injection molding. • A novel clamping method capable of sensing mold misalignment and self-adjusting mold position and attitude. • The clamping method utilizes capsule with hydraulic oil to provide isostatic pressure and ensure floating structures work. • The prestress steel wire-winding frames in the clamping system cannot restrict degree of freedom of floating structures. • The developed clamping system is verified that the clamping precision is superior to traditional clamping systems. [ABSTRACT FROM AUTHOR]

Micro texturing of injection molds for plastic part functionalization has been of growing interest in recent years. Especially for laser texturing it has been successfully demonstrated at lab scale that microscopic surface features can be replicated in plastics at highest structural precision. This paper focusses on high-rate laser surface texturing of injection mold steel 40CrMnMo7 for injection molding of plastic parts with hydrophobic surface behavior. In this study, an ultrashort pulse laser system was used providing an average maximum laser power of 70 W. At first, the most important process parameters like fluence (0.37 - 2.87 J/cm²) and pulse repetition frequency (PRF, 2 - 16 MHz) were varied in order to determine optimal parameter settings for high quality and high throughput laser machining using a polygon scan system with a scanning speed of 100 m/s. In addition, the influencing effect of the microstructure topography on hydrophobicity was examined. Particularly, the laser texturing of the mold sidewalls was challenging as the processing angle was tilted to 45° that was helpful for defect-free demolding of the injection molded 3D-part. Finally, a whole 3D-injection mold was laser textured for injection molding of a small plastic box with a processed surface area > 40 cm² to prove the feasibility of the process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Based on the tuna swarm optimization-based support vector machine (TSO-SVM) and the multi-objective sparrow search algorithm (MOSSA), this paper proposes a multi-objective optimization approach for injection molding of thin-walled plastic components, addressing the issues of warpage deformation and volume shrinkage that compromise molding quality. Firstly, data samples are obtained based on the Box–Behnken experimental design and computer-aided engineering (CAE) simulation. Subsequently, SVM is employed to build a predictive model between the experimental factors and quality objectives. Additionally, the TSO is applied to optimize the hyperparameters of SVM, aiming to enhance its regression performance and prediction accuracy. Finally, the MOSSA is employed for multi-objective optimization, combined with the CRITIC scoring method for decision-making, to obtain the optimal combination of process parameters. The obtained parameters are then validated through simulation in Moldflow software. After optimization, the warpage deformation is reduced to 0.5085 mm, and the volume shrinkage rate is decreased to 7.573 %, representing a significant reduction of 40.9 % and 18.1 %, respectively, compared to the pre-optimized results. The remarkable improvement demonstrates the effectiveness of the method based on TSO-SVM and MOSSA for the efficient monitoring of the injection molding process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

While organic photovoltaics are accessing specific application sectors taking advantage of their unique properties, it is important to identify as many differentiators as possible to expand the market penetration and consolidation of this technology. In this work, for the first time, the large‐scale fabrication of organic photovoltaic modules embedded into structural plastic parts through industrial injection molding is demonstrated. Thermoplastic polyurethane is chosen as the injected material to show that this additional processing step can yield flexible, lightweight photovoltaic modules with enhanced device robustness and virtually unchanged performance. The critical optomechanical and physico‐chemical material properties, as well as the plastic processing parameters to enable in‐mold plastic solar cells with improved performance and stability, are discussed and provided with perspective. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Quality management is a continuous and dynamic approach to business that seeks to increase the quality, competitiveness and productivity of the organization through the process of continuous improvement of the quality of products, services, processes, people and the environment. The production of plastic injection molding tools is characterized by single production and high product prices. Mistakes in the process of development, design and production of tools most often lead to high costs, non–fulfillment of agreed delivery dates and loss of image with the customer. Because of this, the manufacturer should establish an effective quality management system in its processes, and especially the quality control subsystem. In modern production, product quality control is indispensable and is imposed as a mandatory function of the organization. Controlling and testing of the product during the process of its creation and delivery to the customer is carried out according to pre–defined control points and characteristics for control. These elements are defined in the process of design and technological preparation and are an integral part of the appropriate control documentation. Adequate application of control documentation improves product quality, identifying sources of variation in individual processes and establishing controls for their monitoring. This has the effect of increasing customer satisfaction, reducing waste and improving the financial results of the business. The aim of this paper is to analyze the processes of making plastic injection molding tools and to define and implement the control documentation required for their production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the automotive industry are currently getting more attention because of a significant rise in demand for plastic products, particularly in the automotive, aerospace, sports, and household markets. To produce elaborate shapes and complex geometries with good dimensional precision, manufacturers placed increased focus on plastic injection molding. Adopting various cutting-edge technologies, such as CAD/CAM/CAE, is crucial to meeting demand since they help us understand the injection molding process and manufacture high-quality products. This article focuses on the analysis of the rear fender for automotive products used in the injection molding process using the software Moldex3D. One of the various types of software used to simulate a component's model is Moldex3D. Software called Moldex3D is commonly referred to as computer-aided engineering (CAE). According to the material and technological factors utilized, determines the melt front time, and sprue pressure. Polypropylene (PP) material has been taken into consideration in current research. The qualities of plastic products are influenced by a variety of factors. According to the investigation, the most suitable gate position for a consistent flow of mold material has been suggested. The injection speed and injection pressure are factors that affect the melt front time, according to the multilevel factorial design. To generate the best quality product, the operational factors must be accurate. With a change in parameters, there are changes in the product. The quality of manufacturing products without defects such improper location of runner, flow marks, short shots, hesitation, over packing, and others are referred to as the optimal parameter. [ABSTRACT FROM AUTHOR]

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The mold is the mechanical subassembly that has the role of giving a certain shape to a plastic material with well-determined dimensions. The design and correct execution of injection molds conditions the achievement of high yields of injection processing. Molds for injection of thermoplastic materials are basically composed of two main parts: half mold from the injection nozzle side and half mold from the throwing side. The mold is fixed on the clamping plates of an injection machine. The majority of molds work by injecting the plastic material through a hole with the axis perpendicular to the plane to be separated. In the case of special injections (two-color or three-color injection), the injection is made both perpendicular to the plane to be separated and in the plane to be separated. In the construction of molds, thanks to the infusion of new technologies, optimization is constantly used. An example of this is NC milling after 5 axes. In addition to the reduced processing time of a mold, this also results in higher precision. Compared to NC milling after 3 axes, the processing after 5 axes of irregularly shaped surfaces is done with a smaller number of milling passes, with a better quality of the surfaces. The NC programs for 5 axes claim, thanks to the complex device, a large volume of calculation, at the corresponding processing speed. This can only be solved for the masses with the help of large computers. Complicated and long milling programs can be run with the help of these computers even during the night. [ABSTRACT FROM AUTHOR]

In the realm of injection-molded parts, small length scale deformation defects such as sink marks often pose a major challenge to the aesthetics or functionality of the parts. To address this problem, we present a comprehensive thermoelastomechanical approach that calculates the deformation of injection molded plastic by solving the elastic problem at each time step. In our methodology, two treatments of the molten core are considered: one as a liquid and the other as a rubbery state. Our results suggest that the rubbery state treatment provides higher accuracy in predicting the deformation results, as it maintains the displacement of the localized thermal shrinkage in its vicinity. The validity of our method is supported by empirical measurements on produced parts from the existing literature as well as on samples that we molded independently. [ABSTRACT FROM AUTHOR]

The article presents the use of the OEE tool in a production plant. The aim of the article was to verify whether the use of the OEE indicator will indicate where time is wasted in production. Does the integration of individual programs: classes suchas; APS, MES, ERP, CMMS contribute to the production department achieving better production efficiency? Theoretical considerations were supplemented with an analysis of the production process of an injection molding machine in a plastics processing plant. [ABSTRACT FROM AUTHOR]

Introduction: A hydraulic power injection machine is designed to use a driving screw to inject melted plastic into a specified mold. This machine can be found at an automotive spare parts factory. The cantilever-style heavy-duty screw injector is supported by one roller and secured at the end. An obvious need for vibration analysis on the roller support is essential. A mass spring damper model is proposed for deeply investigating the friction induced vibration mechanism for this injection system to well understand and analyze its vibration behavior. Purpose: A mechanical mode of two degrees-of-freedom (DOF) is designed to improve research on the dynamic features of the Plastic Hydraulic Injection System (PHIS) mechanism. Materials and methods: Experimental investigation and analysis of this mechanism are explored to obtain the instability speed and critical stick slip (SS) speed. The numerical imitation results of this work will help with the design and development of the PHIS mechanism. Conclusion: The stability of the system and SS behavior are next examined by determining the critical variability speediness and critical SS speed. A simulation study is carried out to evaluate the effect of various parameters of the system on its stability and on the behavior of the SS motion. [ABSTRACT FROM AUTHOR]

The paper deals with the dilatometric study of high-alloy martensitic tool steel with the designation M398 (BÖHLER), which is produced by the powder metallurgy process. These materials are used to produce screws for injection molding machines in the plastic industry. Increasing the life cycle of these screws leads to significant economic savings. This contribution focuses on creating the CCT diagram of the investigated powder steel in the range of cooling rates from 100 to 0.01 °C/s. JMatPro® API v7.0 simulation software was used to compare the experimentally measured CCT diagram. The measured dilatation curves were confronted with a microstructural analysis, which was evaluated using a scanning electron microscope (SEM). The M398 material contains a large number of carbide particles that occur in the form of M7C3 and MC and are based on Cr and V. EDS analysis was used to evaluate the distribution of selected chemical elements. A comparison of the surface hardness of all samples in relation to the given cooling rates was also carried out. Subsequently, the nanoindentation properties of the formed individual phases as well as the carbides, where the nanohardness and reduced modulus of elasticity (carbides and matrix) were evaluated. [ABSTRACT FROM AUTHOR]

The incorporation of tannin in bio‐composite biodegradable thermoset materials (BTM) based on poly (lactic acid), starch, sugarcane fiber, and nutrient compounds, to achieve an alternative material for conventional plastics processed by injection molding aiming environmental applications was investigated. The biodegradation of injection molded BTM dog‐bone shape specimens prepared with and without incorporation of 1% tannin extract was evaluated up to 60 days with overall characterization by scanning electron microscopy, Fourier transform–infrared spectroscopy, thermogravimetric analyzer, and X‐ray diffraction. The BTM produced with 1% tannin showed reduced molding defects with an increased productivity of up to 82%, improved mechanical properties and reduced biodegradation of samples buried in an organic substrate. Based on physicochemical characterization, the observed delay in biodegradation of BTM induced by tannin is suggested to result from its tight interaction with the starch and lignocellulosic fibers, acting as antimicrobial agent and reducing the hydrophilicity of blends. Natural polyphenols such as tannins are suitable additives to improve the processability of injection molded bio‐composites and to extend the lifetime of biodegradable materials subjected to contact with high microbial load substrates, such as pots and buckets used to grow plants. [ABSTRACT FROM AUTHOR]

The simulation solutions of different plastic injection molding processes are as multifaceted as the field of injection molding itself. In this study, the simulation of a special injection molding process, which generates partially foamed integral components, was parameterized and performed. This partial and physical foaming is realized by a defined volume expansion of the mold cavity. Using the injection molding simulation software Moldex 3D, this so-called Pull and Foam process was digitally reconstructed and simulated. Since the Pull and Foam process is a special injection molding technique for producing foamed components, the validity of the simulation results was analyzed and evaluated. With the use of Moldex 3D, varied settings such as different bubble growth models and mesh topologies were set, parameterized, and then analyzed, to provide differentiated numerical calculation solutions. Actual manufactured components represent the experimental part of this study and are produced for reference. Different evaluation methods were used to quantify morphological quantities such as porosities, local densities, and cell distributions. These methods are based on two-dimensional and three-dimensional imaging techniques such as optical microscopy and X-ray microtomography (µ-CT). Thus, this structural characterization of the manufactured samples serves as the validation basis for the calculated results of the simulations. According to the illustrated results, the adequate selection of bubble growth models and especially mesh topologies must be considered for valid simulation of specific core-back techniques, such as the Pull and Foam injection molding process. [ABSTRACT FROM AUTHOR]

Thailand has a huge variability of bast fiber plants, some of which have been little researched regarding their applicability in composites. Bast fiber(bundle)s from different species were investigated and incorporated into a polylactide (PLA) matrix by injection molding. Hemp and kenaf were used as well-studied fibers, while roselle, Fryxell and paper mulberry are less extensively characterized. Tensile strength, tensile modulus and interfacial shear strength (IFSS) of single fiber(bundle)s were highest for hemp, followed by kenaf, roselle, Fryxell and paper mulberry. Despite the lower tensile strength and IFSS of paper mulberry, the highest tensile strength was achieved for the paper mulberry/PLA composite followed by hemp/PLA. Scanning electron microscope (SEM) analyses showed that the single cells in paper mulberry fiber bundles, in contrast to the other fiber types investigated, were only partially bonded to each other, which explains the lower strength of the fiber bundles. The higher aspect ratio of fiber(bundle)s of paper mulberry in the PLA composite can explain the good composite characteristics. Apart from hemp, paper mulberry shows the best reinforcing effect in the PLA matrix and offers interesting potential for composite applications. Compared to neat PLA, the tensile strength could be increased by 24% and the tensile modulus by 54%. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to achieve an optimum wafer spool among wafer conveyor spools made of three different food-grade thermoplastic materials for plastic injection molding production. For that purpose, an existing spool design created for machining, and a new spool design created for plastic injection molding production are analyzed by using the finite element method. These two designs are optimized by utilizing the response surface methodology, based on the criteria of minimizing the equivalent stress, deformation and mass. At the end of the detail comparison of new alternative spool designs having different materials according to their ranking scores obtained for both static structural and plastic molding analysis, the new spool design having the material of polypropylene is selected as a best design for plastic injection molding production. [ABSTRACT FROM AUTHOR]

This paper proposes a supervised learning with a class-balancing loss function (SL-CBL) approach for fault detection and feature- similarity-based recipe optimization (FSRO) for a plastic injection molding process. SL-CBL is a novel method that can accurately classify an input sample as a normal or fault condition, even when the training data are severely class-imbalanced. The proposed class-balancing loss function consists of the weighted focal loss and the loss of the F1 score; together, these are used to correctly classify even a small number of faulty samples. SL-CBL is investigated with four classifiers of different structures; the classifiers consist of several fully connected and batch normalization layers. FSRO is an optimization scheme that finds the optimal recipe whose feature is similar to the features of normal samples. The optimal solution is obtained by minimizing the Euclidean distance to the centroid of the normal features. In this research, the proposed SL-CBL and FSRO methods are validated by applying them to an industrial plastic injection molding dataset. The validation results show that the proposed SL-CBL approach achieves the highest F1 score with the lowest misclassification rate, as compared to the alternative methods. When visualizing the feature space, the optimal recipe found by the FSRO scheme was found to be close to the centroid of the normal features, even if the initial recipe is classified as a fault. Furthermore, each variable of the optimized recipe lies within the confidence interval of 3a for the normal condition. This indicates that the optimal recipe is statistically similar to the normal samples. [ABSTRACT FROM AUTHOR]

In this paper, the design of novel slotless permanent magnet synchronous motor (PMSM) for a collaborative robot was studied considering the manufacture process of winding. The winding manufacture process of novel slotless PMSM was proposed in three steps. First, the two types of coil units were manufactured based on the winding jig to assemble the coil units. Second, the coil unit was manufactured using the injection molding based on the plastic material such as polyphenylene sulfide (PPS). Third, the units of the coil were assembled to form a stator winding. Considering this manufacture process of winding, the slotless motor design was studied for the collaborative robot. For the design and analysis of slotless motor, finite element analysis (FEA) was performed through ANSYS Maxwell. The electromagnetic performance was analyzed according to the pole-slot combination. Considering the space of the collaborative robot, the basic model was designed. Based on the basic model, the electromagnetic performance was analyzed according to the design parameters such as the thickness of magnet and yoke and turns per slot. Considering the torque and current density, the final model was designed. To verify the FEA results, the slotless motor was manufactured and the experiment and FEA results were compared. [ABSTRACT FROM AUTHOR]

Injection molding has become an increasingly widely used method in the production of plastic parts. The injection process can be separated into five steps: mold closure, filling, packing, cooling, and product ejection. Before the melted plastic is loaded into the mold, the mold needs to be raised to a specified temperature, in order to increase the mold's filling capacity and improve the resultant product quality. One of the easy methods used to control a mold's temperature is to provide hot water through a cooling channel in the mold, to raise the temperature. In addition, this channel can be used for cooling the mold with cool fluid. This is simple, effective, and cost efficient, involving uncomplicated products. To improve the heating effectiveness of the hot water, a conformal cooling-channel design is considered in this paper. Through heat-transfer simulation using the CFX module in the Ansys software, an optimal cooling channel was defined according to the simulation result, using the Taguchi method integrated with principal component analysis. The comparison of traditional vs. conformal cooling channels revealed higher temperature rises in the first 100 s in both molds. During heating, conformal cooling produced higher temperatures compared with traditional cooling. Conformal cooling demonstrated better performance, with average temperature peaking at 58.78 °C and a range of 63.4 °C (max) to 54.66 °C (min). Traditional cooling resulted in an average steady-state temperature of 56.63 °C and a range of 61.74 °C (max) to 53.18 °C (min). Finally, the simulation results were verified experimentally. [ABSTRACT FROM AUTHOR]

Efficient hybrid joining methods are required for joining metals and plastics in the automobile and airplane industries. Injection molded direct joining (IMDJ) is a direct joining technique that uses metal pretreatment and injection molding of plastic to form joints without using any additional parts. This joining technique has attracted attention from industries for its advantages of high efficiency and low cost in mass production. Blast-assisted IMDJ, an IMDJ technique that employs blasting as the metal pretreatment, has become suitable for the industry because metal pretreatment can be performed during the formation of the metal surface structure without chemicals. To satisfy industry standards, the blast-assisted IMDJ technique needs to be optimized under blasting conditions to improve joining performance. The number of parameters and their interactions make this problem difficult to solve using conventional control variable methods. We propose applying statistical and artificial intelligence analyses to address this problem. We used multiple linear regression and back propagation neural networks to analyze the experimental data. The results elucidated the relationship between the blasting conditions and joining strength. According to the machine learning predicted results, the best joining strength in blast-assisted IMDJ reached 22.3 MPa under optimized blasting conditions. This study provides new insights into similar engineering problems. [ABSTRACT FROM AUTHOR]

Plastic injection molding industries demand high precision, accuracy, and advanced manufacturing processes, that are efficient and effective to minimize costs and comply with industrial standards and customer requirements. This article provides a descriptive and comprehensive approach aimed at identifying process improvements in the manufacturing of two product parts of a plastic injection mold. To this end, data has been collected to assess the relation between schedule times and actual manufacturing times, and devise possible actions to improve the processes' efficiency. The data was also used to understand the performance of the current production process, to detail the times per manufacturing process, and to identify possible problems in the production process of the two product parts. The information gathered, directed the advancement of improvement proposals to mitigate and minimize the issues leading to variations between schedule and actual manufacturing times. The approach was descriptive, based on empirical data, to characterize the operational processes, that the parts must go through, highlighting the variances between the expected and actual manufacturing times. [ABSTRACT FROM AUTHOR]

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area. [ABSTRACT FROM AUTHOR]

Eliminating warpage in injection molded polymeric parts is one of the most important problems in the injection molding industry today. This situation is critical in geometries that are particularly susceptible to warping due to their geometric features, and this occurs with topologies of great length and slenderness with high changes in thickness. These features are, in these special geometries, impossible to manufacture with traditional technologies to meet the dimensional and sustainable requirements of the industry. This paper presents an innovative green conformal cooling system that is specifically designed for parts with slender geometric shapes that are highly susceptible to warping. Additionally, the work presented by the authors investigates the importance of using highly conductive inserts made of steel alloys in combination with the use of additively manufactured conformal channels for reducing influential parameters, such as warpage, cooling time, and residual stresses in the complex manufacturing of long and slender parts. The results of this real industrial case study indicated that the use of conformal cooling layouts decreased the cycle time by 175.1 s—66% below the current cooling time; the temperature gradient by 78.5%—specifically, 18.16 °C; the residual stress by 39.78 MPa—or 81.88%; and the warpage by 6.9 mm—or 90.5%. In this way, it was possible to achieve a final warping in the complex geometry studied of 0.72 mm, which was under the maximum value required at the industrial level of 1 mm. The resulting values obtained by the researchers present a turning point from which the manufacturing and sustainability in the injection molding of said plastic geometries is possible, and they take into account that the geometric manufacturing features analyzed will present a great demand in the coming years in the auto parts manufacturing industry. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? In this study, we apply the Latin hypercube sampling method for sampling and combine the CGIDN and response surface modeling methods, which can effectively optimize the injection process. The CGIDN method allows a small number of initial data points to be considered and uses a method of continuously updating the sampling points to guide the search for the optimal process parameters that minimize the residual stress values and volume shrinkage. What is the implication of the main finding? The Latin hypercube sampling method allows uniform, random and orthogonal sampling within the planned spatial area of the experimental factor design and allows artificial control of the number of trials. The method yields sample data with high spatial coverage of the ex-perimental design can improve the accuracy of modeling. The method proposed in this study can effectively optimize the process parameters in the injection molding process, thus improving the reliability and quality output of the injection molded products and providing guidance to the plant engineers in adjusting the machines. Injection molding process parameters have a great impact on plastic production quality, manufacturing cost, and molding efficiency. This study proposes to apply the method of Latin hypercube sampling, and to combine the response surface model and "Constraint Generation Inverse Design Network (CGIDN)" to achieve multi-objective optimization of the injection process, shorten the time to find the optimal process parameters, and improve the production efficiency of plastic parts. Taking the LSR lens array of automotive LED lights as the research object, the residual stress and volume shrinkage were taken as the optimization objectives, and the filling time, melt temperature, maturation time, and maturation pressure were taken as the influencing factors to obtain the optimization target values, and the response surface models between the volume shrinkage rate and the influencing factors were established. Based on the "Constraint-Generated Inverse Design Network", the optimization was independently sought within the set parameters to obtain the optimal combination of process parameters to meet the injection molding quality of plastic parts. The results showed that the optimal residual stress value and volume shrinkage rate were 11.96 MPa and 4.88%, respectively, in the data set of 20 Latin test samples obtained based on Latin hypercube sampling, and the optimal residual stress value and volume shrinkage rate were 8.47 MPa and 2.83%, respectively, after optimization by the CGIDN method. The optimal process parameters obtained by CGIDN optimization were a melt temperature of 30 °C, filling time of 2.5 s, maturation pressure of 40 MPa, and maturation time of 15 s. The optimization results were obvious and showed the feasibility of the data-driven injection molding process optimization method based on the combination of Latin hypercube sampling and CGIDN. [ABSTRACT FROM AUTHOR]

Accelerometers are sensitive devices that capture vibrational fault signatures from industrial machines. However, noise often contaminates these fault signatures and must be eliminated before analysis. A data-driven (DD) denoising algorithm capable of filtering useful vibrational fault signatures from background noises was derived in this study. The algorithm was first validated by comparing its denoised result with a numerically generated ideal signal with a known exact solution. The DD denoising approach reduced the Mean Squared Error (MSE) from 0.459, when no denoising was performed, to 0.068, indicating an 85.2% decrease in noise. This novel approach outperformed the Discrete Wavelet (DW) denoising approach, which had an MSE of 0.115. The proposed DD denoising algorithm was also applied to preprocess vibration data used for the real-time lubrication condition monitoring of the plastic injection molding machine's toggle clamping system, thereby reducing false positive relubrication alarms. The false positive rates, when analysis was performed on the raw vibration and the DW denoised vibration, were 10.7% and 7.6%, respectively, whereas the DD denoised vibration yielded the lowest false positive rate at 1%. This low false positive rate of the DD denoised vibration indicates that it is a more reliable condition monitoring system, thereby making this technique suitable for the smart manufacturing industry. [ABSTRACT FROM AUTHOR]

During root canal treatment, whole or a part of the tooth crown may be lost. Post and core is a method of repairing a damaged crown, in which a pinjet dipped in acrylic resin is used to mold the tooth canal and make a cast post and core. The main goal of this research is to produce an inexpensive pinjet with required strength and proper adhesion to acrylic resin. Therefore, to determine a right material for pinjet, lap shear and tensile tests were performed on eight polymer materials to check the adhesion of pinjet to acrylic resin and its yield strength, respectively. To produce some pinjets, a plastic injection mold was built and Moldflow Insight software was used to determine the process parameters of the injection molding. The results showed that HIPS 7240 with the lowest price among the examined materials and sufficient adhesion to acrylic resin is a suitable material for the pinjet. Also, this material with a yield strength of 10.91 MPa had an appropriate strength to prevent plastic deformation during the post and core molding process. While the injection time, melt temperature, and mold temperature were considered to 1 s, 230 ˚C, and 25 ˚C, respectively in both simulation and experimental methods, the injection pressure was 19 and 18 MPa and the injection speed was obtained 0.3 and 0.27 cm/s respectively in the simulation and experiments. Producing a perfect pinjet shows the ability and accuracy of the simulation in proposing the process parameters of plastic injection molding. [ABSTRACT FROM AUTHOR]

Quality control is considered a critical aspect of plastic materials in the injection molding process. Two types of deformations occur during the injection molding process, namely, volumetric shrinkage and warpage. This study aims to optimize the warpage of the polyethylene terephthalate preform (PET) used for the packing of carbonated drinks. PET warpage results in an uneven distribution of material over the wall surface of the preform and causes variation in wall thickness. During the filling operation of carbonated drinks, the preforms are subjected to high pressure at the points where the wall thickness is at a minimum, which induces a high-stress concentration. Under high pressure, the preforms are ruptured at the points where the warpage is at a maximum (stress concentration area), causing wastage of the beverage as well as the preform. In this study, the Taguchi method and analysis of variance (ANOVA) are used to determine the most significant parameters to induce warpage during the molding process. Then, we optimize the process parameters in order to reduce warpage through a numerical approach using SolidWorks Plastics. The result shows that the ambient temperature and melting temperature are the most critical parameters that contribute to the warpage, yielding 42.115% and 41.278%, respectively. Among the 6 parameters considered for this study, the pressure holding time contributes a minimum of 0.5961% to the yielding of the warpage. Overall, by optimizing the process parameters, warpage of the PET preform is minimized by 7.7202%, which helps to reduce wastage of the carbonated drink as well as the rejection rate of the preform during the filling operation. In a nutshell, the quality of the preform is improved. [ABSTRACT FROM AUTHOR]

The mechanism of laser-inducing hierarchical metal surface structures was investigated both experimentally and numerically, providing insights into improving bonding strength of metal-plastic hybrids (MPHs). For this purpose, metal sheets were first treated with the nanosecond pulsed laser to induce surface microstructures, followed by being put into the mold cavity, and jointed with plastic via the injection molding process. Both aluminum and titanium alloys were adopted as the metals and the glass fiber reinforced polyphenylene sulfide was chosen as the plastic material for MPHs. Specifically, laser parameters including the scanning speed, laser frequency, scan repetition, and modulation switch pulse-width were varied to investigate their impact on metal surface microstructure morphology and thus the MPH bonding strength. It is found that although having no clear trend while changing the laser parameters, the MPH bonding strength is strongly dependent on both surface morphology and material type of the metals. Finally, the process was optimized based on observations concerning the laser-inducing mechanism. The titanium alloy surfaces with bumpy morphology facilitated forming microscale mechanical interlocking between the metal and the plastic, leading to enhanced MPH bonding strength. [ABSTRACT FROM AUTHOR]

Fiber-Reinforced Thermoplastic (FRT) is widely used in plastic injection molding products. In this paper, the effect of fiber concentration on the quality of the plastic injection molding product is considered numerically. PA6 plastic materials, Schulamid 6, with different Glass Fiber (GF) contents, i.e., 15wt%, 30wt%, and 50wt% are selected for the same part and mold designs and process conditions. The warpage of the plastic product, the pressure loss inside the cavity, the plastic density, the packing pressure, and the volumetric shrinkage were investigated. The governing equations and the boundary conditions are numerically solved by the CAE software for injection molding products with the control volume method in a 3D transient system. The results indicate that the higher the content of glass fiber in Schulamid 6, the lower the fluidity of the melted plastic in the Schulamid 6 between different glass fiber contents of 15wt%, 30wt%, and 50wt%. Consequently, the pressure loss in the cavity is highest for Schulamid 6 GF50. Moreover, high GF concentration enhances the plastic density and leads to low volumetric shrinkage and also small warpage inside the product. Therefore, the quality of the part could be improved with the high GF concentration in plastic, i.e., Schulamid 6 GF 50. [ABSTRACT FROM AUTHOR]

In this study, the effect of mold release agent on the Environmental Stress Cracking (ESC), of plastics under injection molding application has been investigated for crack and craze initiation. Recently, polycarbonate which is used as a polymer in this study has attracted great attention due to its advantages such as high durability, impact resistance, and is more economical. During the plastic injection production process, plastics parts experience failure due to severe causes. ESC, which occurs when metallurgical, environmental, and mechanical conditions are combined. Furthermore, a plastic resin is degraded by a chemical agent in the presence of stress. So, the effects of the chemical agent and mechanical stresses cause cracking after some time. It has been determined how chemical agents' consequences critical plastic component failure. Moreover, contamination of chemical agents is an accelerating agent to plastic parts considering ESC mechanism. Molding release agents are selected for questioning as a chemical agent because they are used widely in the industries. Plastic parts that have different mechanical properties are manufactured while using different molding release agents, and craze initiation and propagation stages have been observed experimentally. Therefore, considering ESC, some chemical agents is a factor of increasing crazes to grow more rapidly. [ABSTRACT FROM AUTHOR]

Wire electrical discharge machining (WEDM) is an unconventional machining technology that can be used to machine materials with minimum electrical conductivity. The technology is often employed in the automotive industry, as it makes it possible to produce mold parts of complex shapes. Copper alloys are commonly used as electrodes for their high thermal conductivity. The subject of this study was creating mathematical models for the machining optimization of Ampcoloy 35 with different thicknesses (ranging from 5 to 160 mm with a step of 5 mm) using WEDM to improve the surface properties of the mold parts. The Box–Behnken type experiment was used with a total of 448 samples produced. The following machining parameters were altered over the course of the experiment: the pulse on and off time, discharge current, and material thickness. The cutting speed was measured, and the topography of the machined surfaces in the center and at the margins of the samples was analyzed. The morphology and subsurface layer were also studied. What makes this study unique is the large number of the tested thicknesses, ranging from 5 to 160 mm with a step of 5 mm. The contribution of this study to the automotive industry and plastic injection mold production is, therefore, significant. The regression models for the cutting speed and surface topography allow for efficient defect-free machining of Ampcoloy 35 of 5–160 mm thicknesses, both on the surface and in the subsurface layer. [ABSTRACT FROM AUTHOR]

Fiber reinforced plastic materials are composite materials used in product lightening studies. In the injection process of these anisotropic materials, the fibers may disperse in different directions depending on the flow. In the finite element modeling of products manufactured from these materials, the identification of fiber directions by transferring them from the process environment increases the accuracy of the analysis. In this study, a finite element model of the vehicle pedal obtained from short fiber reinforced plastic produced by injection molding and structural analysis was carried out by including fiber orientation data were created. Fiber orientation data were obtained as a result of injection simulation used in the multi-scale material model including anisotropy. Pedal results produced using 30% glass fiber reinforced polyamide material were compared with pedal data produced using non-reinforced plastic material and steel material. Compared to the steel pedal, the displacement results of the pedal made of reinforced plastic material increase by 2.5% and the stress values increase by 7%. When the yield stress of reinforced plastic, which is 294 MPa, is taken as reference, it is concluded that this increase is below the constraint values. In addition, 20% lighter pedal is obtained compared to steel material. [ABSTRACT FROM AUTHOR]