This paper proposes the use Taguchi method for parameter optimization in the numerical simulation stage for the injection molding process to reduce the total displacement of the product. The ...variables of melting temperature, cooling time, filling time, and holding time were identified. The use of Taguchi's design of experiments of three levels and five factors is proposed, which adds up to a total of 27 iterations of the experiment. The signal-to-noise analysis determined that the two most influential parameters in the decrease of displacement were melting temperature and pressure maintenance time. After the analysis of the variance and the interpretation of signal graphs, two experiments were proposed whose values demonstrated an improvement of 27 % (5.0349 mm) and 31.43 % (4.7485 mm), respectively, compared to the control values (6.9252 mm). Using Taguchi and SolidWorks plastic, it was possible to reduce the variation of deformation and the detection of the main variables that affect the filling process of the part by applying the proposed method.
El trabajo plantea el uso de Método Taguchi para la optimización de parámetros en la simulación numérica del proceso de inyección de plástico para reducir el desplazamiento total en el producto. Se identificaron las variables de temperatura de derretimiento, tiempo de enfriamiento, tiempo de llenado, y tiempo de mantención. Se plantea la utilización de diseño de experimentos de Taguchi de tres niveles y cinco factores, que suman un total de 27 iteraciones del experimento. El análisis de señal a ruido determinó que los dos parámetros más influyentes en la disminución de desplazamiento fueron temperatura de derretimiento y tiempo de mantención de presión. Tras el análisis de la varianza y la interpretación de gráficas de señal se plantearon dos experimentos cuyos valores demostraron una mejora de 27 % (5.0349 mm) y 31.43% (4.7485 mm), respectivamente, en comparación a los valores de control (6.9252 mm). Mediante el uso de las herramientas permite. Mediante el uso de Taguchi y SolidWorks plastic se logró disminuir la variación de la deformación y la detección de las principales variables que afectan en el proceso de llenado de la pieza aplicando el método propuesto.
Since its beginning, lean manufacturing has built a worldwide reputation based on results related to production improvement and cost reduction in several companies. This management philosophy focuses ...on customer value creation through the elimination of production wastes. Lean methods and techniques have spread their scope from the automotive industry to a wide range of industries and services. This article presents a case study that describes the use of the lean tool value stream mapping in the production process of automotive parts for a major automotive company. At the beginning of the project, relevant data from the process were collected and analysed. Subsequently, the initial process was mapped, the related wastes were identified, and then future processes were mapped and financial results were estimated. The proposals were presented on kaizen meetings, the action plan was discussed and the decision regarding which option to choose was taken. Consequently, the Cycle Time and the level of the workforce were reduced, the process was improved and savings were obtained.
The customization of plastic injection molding dies is technologically and economically limited by conventional manufacturing processes. Recent advances in hybrid additive manufacturing (HAM) have ...provided more geometrical freedom for the manufacturing of parts with desired properties. In this paper, we report manufacturing of a hybrid 420/Corrax stainless steel with a reliable interface that can be applied in the manufacturing of next-generation geometrically complex plastic injection molding dies with enhanced strength and corrosion resistance. AISI 420 martensitic stainless steel is used as a cost-effective substrate, and a maraging stainless steel grade, known as Corrax, is printed on top of it using laser powder bed fusion (LPBF). A hybrid heat treatment cycle is applied to improve metallurgical properties and to enhance mechanical compatibility between the martensitic and the maraging stainless steels. Tensile tests coupled with scanning electron microscopy are carried out for analysis of failure, which show the development of shear bands in the microstructure of the 420 stainless steel substrate while a limited amount of deformation occurs in the interface region and Corrax microstructure. Void nucleation, growth, and coalescence are found at the 420/Corrax interface due to mechanical incompatibility and decohesion; however, microstructural instability mainly occurs along the shear bands on the 420 side and leads to fracture, which is quantified using high-resolution X-ray computed tomography. Nanoindentation tests show that the maximum level of hardness occurs at the interface due to the existence of sub-micron grains and the formation of AlN nanoparticles. Also, the formation of β-NiAl precipitates enhances the Corrax strength after heat treatment. In addition to a high strength, elevated corrosion resistance of the cooling channels is essential to extend the service life of plastic injection molding dies. Potentiodynamic corrosion testing at the interface shows that Corrax has remarkable corrosion resistance compared to 420. Therefore, additive manufacturing of the critical die areas such as the cooling channels using Corrax increases the service life of the mold.
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•A HAM 420/Corrax martensitic-maraging stainless steel is fabricated using the LPBF method.•Strength and mechanical compatibility are enhanced by applying a hybrid heat treatment.•Development of shear bands followed by void formation on the 420 side lead to fracture.•β-NiAl precipitation hardening enhanced the Corrax strength after heat treatment.•Significant grain boundary and AlN nanoparticle strengthening occur at the interface.
This article presents a novel plastic injection molding method for industrial manufacturing of polyvinyl chloride/carbon fiber/graphene (PVC/CF/Gr) nanocomposite pipes. In this method, the ...reprocessing cycles in the screw cylinder barrel and the fiber (embedded as the material) passes through the extruder lead to enhance Gr dispersion and orientation within the matrix. The mechanical properties of the manufactured nanocomposite pipes are evaluated through three standard tests, namely axial tension, axial compression, and transverse compression. From the experimental results, it is concluded that the second reprocessed PVC/CF/Gr composite demonstrates superior mechanical properties in comparison with the virgin composites. The results indicate an increase of 179% and 154% for Young’s modulus, along with an ultimate tensile strength at 2-wt% Gr with two reprocessing iterations in the cylinder barrel, respectively. Improvement in the mechanical properties of the PVC/CF/Gr composite produced by melt reprocessing could be due to strong interfacial interactions between the polymer matrix and the fibers.
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•Ensemble learning models can predict the quality in the injection moulding process.•Rule-based explanations are developed for interpreting the ensemble models.•The method generates ...decision rules and visualizes them with PDP and ICE plots.•The method is illustrated with a dataset in the injection moulding process.•The explanations can aid the machine operators in manufacturing.
Manufacturing quality control (QC) in plastic injection moulding is of the upmost importance since almost one third of plastic products are manufactured via the injection moulding process. Moreover, smart manufacturing technologies are enabling the generation of huge amounts of data in production lines. This data can be used for predicting the quality of manufactured plastic products using machine learning methods, allowing companies to save costs and improve their production efficiency. However, high-performance machine learning models are usually too complicated to be understood by human intuition. Therefore, we have introduced a rule-based explanations (RBE) framework that combines several machine learning interpretation methods to help to understand the decision mechanisms of accurate and complex predictive models – specifically tree ensemble models. These generated rules can be used to visually and easily understand the main factors that affect the quality in the manufacturing process. To demonstrate the applicability of RBE, we present two experiments with real industrial data gathered from a plastic injection moulding machine in a Singapore model factory. The collected datasets contain condition data for several manufacturing processes as well as the QC results for sink mark defects in the production of small plastic products. The experiments revealed that it is possible to extract meaningful explanations in the form of simple decision rules that are enhanced with partial dependence plots and feature importance rankings for a better understanding of the underlying mechanisms and data relationships of accurate tree ensembles.
The cooling process is an essential aspect while designing for uniform heat transfer between the mold and the molded part. Improper design and placement of cooling channels result in non-uniform ...cooling and thus results in differential shrinkage and warpage on the final product. The installation of the channels yet plays a crucial role in the cooling of the part. Conforming channels that are placed at an optimum distance from the part to enhance the cooling process. In this paper, the performance parameters of straight drilled channels are compared with the conformal cooling channels for an electric alarm box. The analysis indicates that the conformal cooling method improved and enhanced the cooling process and reduced the defects like warpage and differential shrinkage by 25.5% and 28.0% respectively.
In this study, the cell thin shell phone cover produced with polycarbonate/acrylonitrile butadiene styrene (PC/ABS) thermoplastic were decided as a model. First, the effects of the injection ...parameters on warpage for different thickness values were examined using Taguchi method. The warpage values were found by analyses which were done by moldflow plastic insight (MPI) 4.0 software. The most influential parameter on the warpage of PC/ABS material was found as packing pressure. Second, to determine the forces that cause the plastic part to fail at the points determined over the top surface of the cell phone cover, CATIA V5R12 (general structural analysis) was used. The structural analysis of ABS, PC, reinforced ABS, reinforced PC/ABS thermoplastic materials in addition to PC/ABS material used in telephone manufacture were done in order to determine the performance. When we look at the structural analysis, the strongest materials are 15% carbon fiber reinforced PC/ABS, 15% carbon fiber reinforced ABS, PC, PC/ABS and ABS, respectively. The most critical point on the top surface of the cell phone is point 2
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