Casting Machine Research Articles

Optimization of Plastic Speed Meter Housing for Automobiles: Injection Molding Simulation, Taguchi Method and Machine Learning

The plastic speed meter housing for automobiles requires accurate parts and assembly to inform the driver of their exact speed. For accurate assembly, the molded speed meter should have a minimize amount of deformation. In this study, to obtain injection molding conditions that minimize the deformation of the speed meter, the main molding conditions that cause the deformation of the speed meter were identified using the Taguchi method. By combining the confirmed molding conditions, 150 data sets were created, and machine learning was conducted using the data set. The model with the best accuracy learned through machine learning was the Linear Regression model. The results of this Linear Regression model were then validated with test data. The optimal injection molding conditions were derived by inputting 5000 molding conditions data into the learned Linear Regression model. Injection molding analysis was performed using the derived injection molding conditions, and the amount of deformation was reduced by about 6.4% compared to the case where current molding conditions were applied. The optimal molding conditions obtained by machine learning were applied to actcual molding. The amount of deformation of the mold amount of the molded speed meter housing was smaller than the amount of deformation predicted in the machine learning model.

PREPARATION OF BIODEGRADABLE PLATES FROM SUGARCANE BAGASSE

Saccharum officinarum is a natural result that remains as dry, stringy deposits. It is likewise being thought of as an option in contrast to wood in the creation of endlessly paper items. In numerous tropical & subtropical nations like India, China, Colombia, Iran, Thail&, & Argentina, Bagasse is normally utilized rather than wood in the development of mash, paper & board. This replacement produces mash with actual properties that are appropriate for printing & journal paper, tissue items, boxes, & newspapers. It can likewise be utilized for making sheets looking like compressed wood or molecule board, realized called bagasse sheets & Xanita sheets. These are broadly utilized in the creation of partitions & furniture. Waste sugarcane is dried in sunlight & Bagasse is extracted from it & purified using various chemical & mechanical techniques. Natural gum, that is all purpose flour has been used as binding agent. Shaping the biodegradable plate has been done from press moulding machine. Various properties such as moisture content, E. coli, Coliform, Salmonella. Yeast & Mould are tested from Analytical & environmental lab. So, from the synthesis, it concludes that the biodegradable plate manufactured from Saccharum officinarum plant are cheaper, as raw materials are easily available. Key Words: sugarcane bagasse, biodegradeable, lasers, templates, journals

Development of a Neural Network Model and Taguchi-Based Optimization for Dry Sliding Wear Performance of Al 6065 Alloy Reinforced with Nano SiC and Graphene Nanoplatelets

The Al6065 alloy is widely used in various applications, including tubing for furniture, railway and bus structures, pylons, platforms, and pipelines. However, due to the wear experienced in these applications, it is essential to enhance the wear confrontation of this alloy. To address this issue, this research focuses on reinforcing Al6065 with nanoparticles of silicon carbide and graphene by means of the stir casting method. The wear behaviour of the alloy is studied by varying the rotational speed, load, and composition of reinforcement in the stir casting machine using Taguchi design of experiment. The rate of wear and friction coefficient are measured as responses. The obtained results are then analysed and optimized for the minimum of the output responses using S/N ratio analysis. Further, ANOVA is carried out to determine the influence of each parameter, and a model of the neural network is developed to predict the response. The findings indicate that cumulative the percentage of reinforcement enhances the wear resistance of the alloy. The optimized values of the rotational speed, load, and composition of reinforcement lead to improved wear resistance, with a corresponding decrease in the coefficient of friction. The ANOVA results reveal that the rotational speed and load significantly affect the responses, while the reinforcement composition has a moderate effect. The developed neural network model accurately predicts the response with a high degree of accuracy. The model can be used to optimize the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene, as well as for prediction of the alloy's wear behaviour in various application environments. In conclusion, this research delivers a complete thoughtful of the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene. The findings can be used to optimize the wear resistance and improve the routine of this alloy in various applications.

Ranking analysis of flyash – basalt fibre – polyamide 66 polymer composites based on the mechanical and sliding wear performance metrics using hybrid AHP-R method

This research work investigates the physical, mechanical, thermal, thermo-mechanical, and dry sliding wear characteristics of hybrid flyash particulates (F-class; 0 – 20 wt.% at the step of 5%)–basalt fibres (chopped; fixed 10 wt.%) reinforced polyamide 66 polymer composites fabricated using the twin screw extruder and injection moulding machine. Taguchi's design of experiment optimization approach is used for parameter optimization of the dry sliding wear process, followed by analysis of variance analysis. Further, the hybrid AHP-R method is used for ranking optimization based on the performance metrics. It is observed that the composition having 15 wt.% flyash particulates optimizes overall performance metrics, hence recommended for industrial parts fabrications. It has an experimental density of 1.23 g/cc, voids content of 5.93%, water absorption of 4.21%, tensile strength of 110.73 MPa, flexural strength of 146.72 MPa, Rockwell hardness of 62.44 HRM, fracture toughness of 4.17 MPa√m, impact strength of 2.06 J, the thermal conductivity of 1.24 W/mK, and specific wear rate of 7.55 × 10−4 mm3/Nm. The overall subjective ranking of the hybrid polymer composites attunes with the objective ranking by the hybrid AHP-R method.

Flow and Heat Transfer on the Surface of Molten Steel Slag Layer in Continuous Casting Mold

Protective slag is coated on the surface of molten steel during continuous casting, the flow and heat transfer state of the protective slag is a decisive factor affecting the inflow and consumption of liquid slag and is also an important prerequisite for stabilizing and improving the quality of continuous casting billets. Based on the Navier Stokes fluid momentum conservation equation and energy equation, a two-dimensional longitudinal numerical model describing the flow/heat transfer of liquid protective slag on the surface of steel is established. The data comes from the equipment parameters and casting process of an arc shaped slab continuous casting machine in a domestic steel plant. The flow field and temperature field distribution of protective slag are calculated and analyzed, and the effects of factors such as slag layer thickness and shear speed on the flow and heat transfer status of the liquid slag layer are discussed. When the bottom shear velocity increases from 0.005 m/s to 0.2 m/s, the maximum flow velocity of liquid slag from the nozzle to the narrow surface in the center area of the model increases from 0.0012 m/s to 0.0617 m/s, and the average longitudinal flow velocity of liquid slag near the nozzle increases from 0.0012 m/s to 0.0627 m/s. The research results provide reference for investigating the complex metallurgical behavior of protective slag.

A multitask encoder–decoder model for quality prediction in injection moulding

It is desirable in injection moulding that every yielded product is of high quality and precision. Several studies are conducted on modelling the relationship between the process parameters e.g., holding pressure, mould temperature, etc. that are either time series or non-time series, and the output product quality characteristics e.g., weight, dimensions, and warpage. To build a more comprehensive/generalized quality prediction model with online learning, this study for the first time takes time series process parameters from 2 pairs of in-mould pressure and temperature sensors simultaneously with 18 non-time series parameters and introduces a single deep learning architecture to perform multitask learning i.e., to leverage information from the related sources to simultaneously predict multiple product quality characteristics on a real injection moulding machine. This along with the discussed modern containerization practices develops a fully automated software part of the quality control process. The non-time series process parameters are learned by fully connected neural network layers of the model while the time series process parameters are related through an attention-based encoder–decoder architecture. This model is ablated as well as compared with a benchmark model to validate the performance advantage in terms of the reduced error asymptote. The proposed model offers an order of magnitude lower average mean squared error than the benchmark model, and 1.2 to 3 times smaller mean squared errors than its ablated versions.

Microstructural evolution of Mg–14Gd–0.4Zr alloy during compressive creep

The present work reports the creep behavior and microstructural evolution of the sand-cast Mg–14Gd–0.4Zr alloy (wt.%) prepared by the differential pressure casting machine. Their compressive creep tests at 250 °C were performed under various applied stresses (i.e., 60, 80 and 100 MPa). Among them, the sand-cast Mg–14Gd–0.4Zr samples examined under 250 °C/80 MPa for 39 and 95 h, respectively, were chosen to systemically analyze their creep mechanisms using high-angle annular dark field-scanning transmission electron microscopy (HAADF-STEM). The obtained results showed that the enhancement of creep resistance can be mainly attributed to the coherent β' and β'F phases with an alternate distribution, effectively impeding the basal <a> dislocations movement. However, with the creep time increasing, the fine β'+β'F precipitate chains coarsened and transformed to semi-coherent β1 phase and even to large incoherent β phase (surrounded by precipitate-free areas) in grain interiors. The precipitate-free zones (PFZs) at grain boundaries (GBs) were formed, and they could expand during creep deformation. Apart from the main cross-slip of basal and prismatic <a> dislocations, <c + a> type dislocations were activated and tended to distribute near the GBs. The aforementioned phenomena induced the stress concentrations, consequently leading to the increment of the creep strain.

Review of Design and Production of the Injection Moulding Machine

Plastics are being employed for both light and heavy items in industrial settings more and more often. Plastic daily objects like bottle caps, remote control casings, needles, and more are created using injection molding equipment, along with large goods like automotive body panels. During the molding process, a liquid or flexible raw material is shaped using a rigid structure known as a mold. This technique originally became popular during the Second World War as the need for mass-produced items increased. It is now extensively utilized across various industries. This study examined the role of plastic molding machines in the design and production of these devices. To correctly examine the corpus of knowledge about the design and production of plastic molding machines, this was done by comparing and assessing already published, widely available works, beginning with early innovations and continuing with more recent advancements. The study's objective was met. Despite rising efforts to design and construct more modern machines that are economical and compact, it was noticed that the bulk of early injection molding machines were large. The injector molding machine has the potential for further improvement and local manufacture, according to the study's conclusions.

Performance analysis of “Injection Moulding Machine” under fuzzy environment through contemporary arithmetic operations on right triangular generalized fuzzy numbers (RTrGFN)

The reliability of an industrial system plays a significant role in new technological era where everyone is concerned about the performance of associated industry. With the frequent demands of the customers, the job of the production industries becomes more tedious to produce the required products at a high rate. To fulfill the customer’s demand, the initial focus of the industries is to work well without any interruption/failure in the entire production process. In this paper, Reliability, Availability, MTTF, MTTR, MTBF, ENOF is proposed for an industrial system namely “Injection moulding machine”. For this, the membership function for right triangular generalized fuzzy numbers (RTrGFN) is proposed with the certain level of confidence. The real data of the Injection moulding machine is taken to validate the proposed methodology. The input data is extracted from the records/maintenance sheets of several years and found uncertainty due to the loss of any information or human mistakes. The parameters of the system like failure rate and repair time is retrieved. Based on data, AND-OR combination for the system is constructed. The lambda-tau methodology along with RTrGFN and its corresponding arithmetic operations is used for the performance analysis of the considered “Injection moulding machine”. For better understanding the results are discussed with the aid of graphs. Also after seeing the result one can conclude that this methodology is one of the best methodology for the performance analysis of a conventional system. Also authors have added the highlights and future scope of the research work at the end of the manuscript.

The influence of surface imperfections on phosphate coating performance of nodular cast iron substrates

&lt;p&gt;The service performance of the nodular cast iron wheel hub was modelled by investigating the effect of surface morphology and characteristics on the phosphate coating size, morphology, and corrosion resistance properties of the zinc phosphate conversion coated cast iron substrates. The machined and unmachined surfaces as well as the coatings were examined by a scanning electron microscope coupled with an energy-dispersive X-ray spectrometer (SEM-EXD). The influence of the substrates’ surface imperfections on the phosphate coating and the subsequent corrosion resistance was assessed and rated according to the standard procedures. Surface analysis of the machined and unmachined cast iron hubs reveals the existence of graphite and foreign material inclusions on the substrate surface that impact the phosphate coating properties and resistance to corrosion. The average phosphate coating crystal size is 1.74 µm and 2.58 µm for the unmachined and machined cast iron substrates, respectively. The corrosion resistance of the coated unmachined wheel hub surfaces was rated poor and disapproved based on the application requirements. The poor corrosion resistance was ascribed to the influence of the substrates’ surface characteristics on the coating adhesion to the substrate. However, the cast surfaces should be properly shot-blasted to remove any adhere foreign materials on the as-cast skin to enhance the coating adhesion.&lt;/p&gt;

The influence of surface imperfections on phosphate coating performance of nodular cast iron substrates

&lt;p&gt;The service performance of the nodular cast iron wheel hub was modelled by investigating the effect of surface morphology and characteristics on the phosphate coating size, morphology, and corrosion resistance properties of the zinc phosphate conversion coated cast iron substrates. The machined and unmachined surfaces as well as the coatings were examined by a scanning electron microscope coupled with an energy-dispersive X-ray spectrometer (SEM-EXD). The influence of the substrates’ surface imperfections on the phosphate coating and the subsequent corrosion resistance was assessed and rated according to the standard procedures. Surface analysis of the machined and unmachined cast iron hubs reveals the existence of graphite and foreign material inclusions on the substrate surface that impact the phosphate coating properties and resistance to corrosion. The average phosphate coating crystal size is 1.74 µm and 2.58 µm for the unmachined and machined cast iron substrates, respectively. The corrosion resistance of the coated unmachined wheel hub surfaces was rated poor and disapproved based on the application requirements. The poor corrosion resistance was ascribed to the influence of the substrates’ surface characteristics on the coating adhesion to the substrate. However, the cast surfaces should be properly shot-blasted to remove any adhere foreign materials on the as-cast skin to enhance the coating adhesion.&lt;/p&gt;

Comparative analysis of the thermal behavior of blooms and beam-blanks during continuous casting: Development and validation of a mathematical model for heat transfer

The present work explores the feasibility of producing blooms and beam blanks in a continuous casting machine using a single support rollers setup, aiming to eliminate the need for specialized setups for each geometry. The current practice requires frequent roll replacements during production switches, leading to reduced machine availability and safety risks.To assess the viability of the new approach, a 2D mathematical model for heat transfer and solidification was developed for both blooms and beam blanks. Simulations with the current and the new setup were performed, and their results analyzed. The model was validated with experiments during production campaigns at the steel melting shop. The model accurately predicts the casting process, with solid fraction deviation of 6% for blooms and 16% for beam blanks, and temperature difference between the model and thermal imaging measures values are between 20 °C and 70 °C.The heat transfer simulation results show that a single rollers setup can yield minimal variations in the temperature related operational parameters, supporting the feasibility of this approach. In addition to the thermal analysis performed in this work, mechanical stress and strain assessments need to be performed to further investigate the suggested solution and they will be the topic of future works.The findings provide valuable insights for the steel industry, as this type of feasibility study is a relatively unexplored area. Furthermore, the models developed can also be used to optimize process parameters, study defects formation during continuous casting and in the development of new products.

Optimization of Macro Segregation and Equiaxed Zone in High-Carbon Steel Use in Prestressed Concrete Wire and Cord Wire Application

In this study, the relationship between macro segregation and the equiaxed zone in high-carbon grades with continuous casting parameters was investigated and optimized at the İsdemir iron and steel plant. The work was conducted for the 1080 quality of the SAE J403 standard. In this study, some parameters, such as casting speed, secondary cooling, EMS current value and EMS frequency value, were examined. When the results of the experiments are examined, it can be observed that the equiaxed zone in the macrostructure decreases significantly with the reduction of the EMS frequency value. The decrease in casting speed and increase in EMS current value caused an increase in the equiaxed zone. The increment in secondary cooling led to a decline in the equiaxed zone. Once the macro segregation results are examined, it can be seen that it is very important to optimize the continuous casting parameters in order to reduce the macro segregation results of—especially—carbon, sulfur and phosphorus elements. It has also been determined that the macro segregation values of carbon, sulfur and phosphorus elements are low in casting conditions where casting speed is low, and the EMS current value and EMS frequency value are high. In addition, macro segregation measurements of manganese, silicon, chromium and vanadium elements are found to be low under similar casting conditions. It is critical to optimize the continuous casting parameters before production, especially in high-carbon grades to be used for prestressed concrete wire and cord wire applications. As a result of the work conducted using the İsdemir billet continuous casting machine for the 1080-grade SAE J403 standard, aiming to optimize macro segregation and the equiaxed zone, the effective results have been achieved by using process parameters of 2.8 m/min casting speed, 360 A EMS current, 5 Hz EMS frequency and low secondary cooling intensity.

Study of mechanical properties of sustainable biocomposite panels using Jute-PLA and Sisal-PLA

In recent years, bio composites have experienced significant growth and have found applications in various sectors, like domestic use, aerospace, circuit boards, and automotive industries. Extensive research has been conducted on natural fibres to develop it as composite materials so that can compete with synthetic fibre composites. Jute and sisal fibres with Polylactic acid (PLA) means fully biodegradable and recyclable, offer an environmentally friendly solution. The fabrication of the bio-composite specimen involved the utilization of a hot press moulding machine. The maximum strength and bending properties, including ductility, were assessed through flexural and tensile tests following ASTM standards. Free vibration analysis was conducted to determine important characteristics like damping frequency, damping ratio, and natural frequency of the bio composites. The resonant set obtained for Sisal/PLA is inferior to Jute/PLA composites, which clearly demonstrates the influence of fibre in resonant behaviour of the composites. Further Sisal/PLA composites show 20% rise in damping performance at first two resonant sets and 30% in the third resonant. This free vibration characterization of most widely used to composites provides valuable insights into the practical usage under suitable environment conditions.

Dry sliding wear and mechanical investigations on flyash particulates - Lapinus fibre - polyamide 66 polymer composites: Ranking analysis using hybrid Analytic Hierarchy Process-R method

This research work examines the physical, mechanical, thermal, thermo-mechanical, and dry sliding wear performance of hybrid waste flyash particulates (F-class; 0-20 wt% @ step of 5%) – Lapinus fibres (fixed 10 wt%) reinforced Polyamide 66 polymer composites fabricated using the twin screw extruder and injection moulding machine. This follows worn surface morphology to understand the prevailing wear mechanisms responsible for surface damage during sliding. Optimization of control parameters and identification of their order of significance in the dry sliding wear process is performed using Taguchi’s design of experiments and analysis of variance (ANOVA). Further, ranking optimization of the hybrid composite specimens based on their performance metrics is analysed using the hybrid AHP-R method. It has been observed that the hybrid composite specimens having 10 wt% flyash particulates optimize the overall performance metrics; therefore, it may be recommended to fabricate parts or components for industrial usage. It tends to have an experimental density of 1.18 g/cc, voids content of 7.11%, water absorption of 3.87%, tensile strength of 105.95 MPa, flexural strength of 144.86 MPa, Rockwell hardness of 58.12 HRM, fracture toughness of 4.11 MPa√m, Impact strength of 1.86 J, thermal conductivity of 1.08 W/mK, and specific wear rate of 1.12 × 10−3 mm3/Nm. This observation was attuned to the ranking analysis using the hybrid AHP-R method.

MECHANICAL BEHAVIOUR OF CARBON AND JUTE FIBERS REINFORCED ALUMINIUM LAMINATES FABRICATED IN DIFFERENT STACKING SEQUENCES

In this research work, the mechanical properties of aluminium laminates having carbon fiber reinforcement and carbon-jute fibers reinforced aluminium laminates fabricated in different stacking sequences are studied. Here the fiber metal laminates are manufactured through the hand layup technique. A compression moulding machine was used to press the laminates during the production process. In this study, carbon and jute fibers along with thin aluminium sheets are used as reinforcement. Four different stacking sequences were selected. Each arrangement is symmetric and consists of ten layers of reinforcements. The fabricated laminates were subjected to tensile, compressive, flexural, hardness and impact tests. The test results show that the laminate L2 with outermost layers of carbon fibers revealed the highest tensile strength of 268 MPa, followed by L4 laminate with jute fibers outer layers having a tensile strength of 226 MPa. Whereas, the laminates L1 and L3 with outer layers of aluminium sheets show improved flexural and impact strength along with high hardness value. The flexural strengths are 246 MPa and 204 MPa respectively. Impact strength of 36 MPa, 32 MPa and hardness value of 64 HVN and 62 HVN respectively.

Energy-saving Technology Focusing on the Power Supply of Injection Molding Machine

Energy-saving Technology Focusing on the Power Supply of Injection Molding Machine

Design of a Samier Cracker Dough Molding Machine Semi-Automatic with Roll Cutting Method

The main obstacle that UMKM SA has is limited production technology which is still manual so that workers take a long time and production results are not good because the thickness of the crackers is uneven and the sizes cannot match. So it is necessary to design a cracker-making machine by making a printing roll tool. The design method uses the manufacturing process method which is based on the cutting process and has a different perspective from the previous thing. Where the researcher analyzes the advantages and disadvantages of the product to be made, has an overview of the product to be made by taking samples of material sources and data from the analysis that has been carried out directly to the resource persons as well as by analyzing the processes that have been carried out. The results of the study are from data analysis of the cracker dough cutting machine with the selected concept, it can be said to be effective with a faster time record in each test, good efficiency and effectiveness of the first tool requires an average time of 03:80 to 01:12 with cracker dough size 46x46x1 totaling 121 pieces.

THE STUDY OF USING NATURAL FILLERS ON THE BIODEGRADATION PROPERTIES OF VIRGIN/WASTE LOW DENSITY POLYETHYLENE AND VIRGIN/WASTE HIGH DENSITY POLYETHYLENE COMPOSITES.

A study was carried out to check the effect of natural filler on the degradation properties of virgin and waste LDPE and virgin and waste HDPE. Samples were collected and compounded with Maleic Anhydride as a compatibilizer between the polymer matrix and PLA, starch and charcoal. Using the two-roll mix and the compression moulding machine for compounding, samples were moulded and cut according to specifications for the test. Tests carried out were done according to ASTM for water absorption, soil burial and QUV. Results showed as the amount of natural filler increased, it improved water absorption up to 42 days for both virgin LDPE and HDPE, while it took 180 days for samples with more percentage of natural fillers to record mass loss due to degradation. The SEM result confirmed a lot of cracks and holes as a result of immiscibility from the poly blends and which aided with high water absorption and degradation. The QUV results also showed degradation of all samples after exposure time in the accelerated weathering test.&#x0D;

A multi-stage fault prediction method of continuous casting machine based on Weibull distribution and deep learning

When predicting the failure of large complete equipment such as continuous casting machines, it is usually difficult to obtain full life cycle failure data of core equipment such as continuous casting rollers. The strategy based solely on reliability distribution cannot guarantee the accuracy of fault prediction, while the strategy based solely on deep learning cannot predict medium and long-term fault trends. Aiming at the problem, a multi-stage fault prediction model TCN-BiGRU-WD for continuous casting rollers is built. The fault data of continuous casting roll is firstly input into TCN for feature extraction, and then the extracted features are input into BiGRU. Not only the short-term fault rate with high accuracy is output, but also the shape parameter(k) and scale parameter(λ) of Weibull distribution (WD) are output, so as to obtain the medium and long-term fault rate function (f(t;λ,k)) of continuous casting roll in the future. An example shows that the proposed multi-stage fault prediction model can not only improve the prediction accuracy of the short-term fault rate of continuous casting roll, but also expand the ability to predict the long-term fault trend in the future.