Mold Temperature Research Articles

Study on the Overmolding Process of Carbon-Fiber-Reinforced Poly (Aryl Ether Ketone) (PAEK)/Poly (Ether Ether Ketone) (PEEK) Thermoplastic Composites.

This paper used poly (aryl ether ketone) (PAEK) resin with a low melting temperature to prepare laminate via the compression-molding process for continuous-carbon-fiber-reinforced composites (CCF-PAEK). Then, poly (ether ether ketone) (PEEK), or a short-carbon-fiber-reinforced poly (ether ether ketone) (SCF-PEEK) with a high melting temperature, was injected to prepare the overmolding composites. The shear strength of short beams was used to characterize the interface bonding strength of composites. The results showed that the interface properties of the composite were affected by the interface temperature, which was adjusted by mold temperature. PAEK and PEEK formed a better interfacial bonding at higher interface temperatures. The shear strength of the SCF-PEEK/CCF-PAEK short beam was 77 MPa when the mold temperature was 220 °C and 85 MPa when the mold temperature was raised to 260 °C. The melting temperature did not significantly affect the shear strength of SCF-PEEK/CCF-PAEK short beams. For the melting temperature increasing from 380 °C to 420 °C, the shear strength of the SCF-PEEK/CCF-PAEK short beam ranged from 83 MPa to 87 MPa. The microstructure and failure morphology of the composite was observed using an optical microscope. A molecular dynamics model was established to simulate the adhesion of PAEK and PEEK at different mold temperatures. The interfacial bonding energy and diffusion coefficient agreed with the experimental results.

The effects of cooling rate (mould temperature) on HDPE gears produced through injection moulding

The effects of mould temperature (cooling temperature) on molten HDPE (Hostalen GC 7260) during manufacture, is evaluated in this paper. HDPE gears were produced at varying mould temperatures using Injection Moulding. Optimised injection values for melt temperature, injection volume, hold pressure, and hold time were obtained, and then held constant while the mould temperature was altered.Analysis on how the mould temperature affected peak melting points and crystallinity were then carried out using differential scanning calorimetry (DSC). These revealed that crystallinity improved as the mould temperature was increased from 22 °C to 65 °C. Gears produced at similar cooling temperatures were then meshed on a gear test rig and run at 1000 rpm, using different torque loadings. Their wear rates, and modes of failure were then analysed, and comparisons were made to ascertain how the differing mould temperatures employed during the injection moulding manufacturing process affected their wear characteristics. Topographical analysis of worn gear teeth was performed using scanning electron microscopy (SEM). It was noted that gear tooth wear and failure was dependent on the mould temperature employed during the manufacturing process. Gears produced at 65 °C showed improved tooth surface wear resistance at lower loads (0.5 Nm and 1 Nm) compared to those produced at 22 °C, but were more likely to fail through tooth fracture at the pitch line due to excessive material removal. Gears produced at lower mould temperatures, on the other hand, exhibited better wear resistance for higher loads (3 Nm and 4 Nm), compared to those produced at higher mould temperatures, and were more likely to fail due to material flow. The results show a correlation between mould temperature, crystallinity, and gear performance. Based on wear rate responses of gears produced at differing mould temperatures to the application of varying torque loadings, a Mould Temperature to Torque Reference Chart for HDPE is presented.

Mold filling behaviour of LPIM feedstocks using numerical simulations and real-scale injections

ABSTRACT This study aims to compare the flow patterns and in-cavity pressures obtained experimentally and numerically for different conditions. Four feedstocks based on 17-4PH stainless steel powder were fully characterised and implemented as material laws in an Autodesk Moldflow package before to obtain numerical simulations that were then validated using real-scale injections. The flow patterns obtained numerically for the different flat bar mold geometries were in good agreement with the experimental flow patterns, showing an almost perfect fit, whereas for the flow patterns of the complex mold geometry, there were some minor discrepancies. The simulated pressure profiles obtained for different mold geometries, feedstock temperatures, mold temperatures and solid loadings were in good agreement with the experimental pressure profiles in terms of trend and pressure values, with maximum relative differences varying from 30 to 64% depending on particular feedstocks and process parameters.

PHYSICO-CHEMICAL METHODS OF INCREASING THE CRACK RESISTANCE OF ALLOY AM4.5Cd (VAL10). NOTICE 1: EXPERIENCE OF USING AN RING SAMPLE

On the example of the AM4.5Kd (VAL10) alloy treated in the liquid state with hydrogen, the efficiency of using the well-known ring test for the study of physicochemical methods of influencing the crack resistance of wide-range aluminum alloys, which are prone to the formation of hot cracks during solidification, was studied. To achieve this goal, an annular mold mold was made according to the recommended dimensions, experiments were carried out on the effect of hydrogen treatment of the melt on the crack resistance of castings. Saturation with hydrogen was carried out by blowing the melt with water vapor, the degree of gas saturation was estimated from the porosity of the castings, calculated from the change in their density compared to the control sample obtained in a mold with sufficient power. Despite the measures taken to increase the sensitivity of the annular sample and obtain stable results, which consisted in adjusting the dimensions of the mold, in particular, the size of the central rod, during compression of which the hardening melt creates tensile stresses leading to the formation of a hot crack, as well as optimizing the thermal regime for obtaining a casting by increasing the initial mold temperature from 22 0С to 217-249 0С, the sensitivity of this sample under the experimental conditions remained too low. In 60 % of cases, gas saturation of the melt contributed to a reduction in the crack propagation process when using a mold at room temperature and up to 100 % when the mold was heated to a temperature of 228 0C to 260 0C. However, in both the first and second cases, 28 % of similar data were obtained on castings from the initial melt not saturated with hydrogen. This casts doubt on the reliability of the positive effect of hydrogen treatment and became the basis for the conclusion that it is inappropriate to use this type of sample in studies of physical and chemical effects on the melt in order to increase the crack resistance of the AM4.5Kd (VAL10) alloy due to a possible error in assessing the effectiveness of their impact, and also to decide on the need to develop a new sample, devoid of the identified shortcomings.

PENGARUH TEMPERATUR CETAKAN TERHADAP STRUKTUR MIKRO DAN KEKERASAN PISTON HASIL PENGECORAN MENGGUNAKAN 3D PRINTING

Motorcycle users in 2019 reached 133,617,012 units in Indonesia. The impact of a large number of motorcycle certainly attracts the interest of the industry to produce two-wheeled automotive vehicles. The piston is part of the engine that functions as a compression barrier in the combustion chamber, the piston material is aluminum alloy. Therefore, research was carried out on making pistons with investment casting methods, physical and mechanical patterns from 3D printers on pistons. This research starts from drawing the piston using CAD (computer-aided design), making a printed pattern from a 3D Printer machine with PLA (polylactide acid) material. The mold pattern is coated with a mixture of gypsum cement and silica powder. The casting process with a pouring temperature of 750ᵒC and a mold temperature of 200 ᵒC, 250 ᵒC, 300 ᵒC, 350 ᵒC, and 400 ᵒC. The results showed the largest grain size at a mold temperature of 400ᵒC. The highest hardness at 200 ᵒC mold temperature is 47.00 HRB and the lowest hardness at 400 ᵒC mold temperature is 40.56 HRB. The higher the temperature of the mold used, the level of porosity of a material will decrease. This is due to the temperature difference when pouring the molten metal and the lower mold temperature. This makes the metal solidification time longer. The higher the mold temperature, the slower the solidification time, the larger the grain structure, and the decreased hardness.

Enhancing the Fatigue Strength of the Weld Line in Advanced Polymer Injection Molding: Gas-Assisted Mold Temperature Control for Thermoplastic Polyurethane (TPU) Composites.

This study presents an innovative approach to enhancing weld line strength in advanced polymer injection molding through applying gas-assisted mold temperature control, significantly increasing mold temperature beyond typical values observed in conventional processes. We investigate the effects of various heating times and frequencies on the fatigue strength of Polypropylene (PP) samples and the tensile strength of Acrylonitrile Butadiene Styrene (ABS) composite samples at different Thermoplastic Polyurethane (TPU) percentages and heating times. Using gas-assisted mold heating, mold temperatures exceeding 210 °C are achieved, which represents a significant advancement compared to the standard mold temperatures of less than 100 °C. As a result, the fatigue strength of the PP sample with mold heating at 15 s shows a remarkable increase of up to 5.4 times at 5 Hz compared to the sample without mold temperature control. Moreover, ABS/TPU blends with 15 wt.% TPU exhibit the highest ultimate tensile strength (UTS) value of 36.8 MPa, while blends with 30 wt.% TPU have the lowest UTS value of 21.3 MPa. This advancement demonstrates the potential for improved welding line bonding and fatigue strength in manufacturing. Our findings reveal that increasing the mold temperature before injection results in higher fatigue strength in the weld line, with the TPU percentage having a more significant influence on the mechanical properties of ABS/TPU blends than heating time. The results of this study contribute to a deeper understanding of advanced polymer injection molding and offer valuable insights for process optimization.

Optimization of residual stress in low-pressure casting of ZL205A alloys

ZL205A alloys are prone to generate serious residual stress due to the long solidification intervals and multi-phase composition. In this study, the low-pressure casting process for ZL205A alloy hemisphere shell casting was investigated and continuously optimized. In the initial process, the knockout time, pouring temperature, preheating temperature of the sand mould, dwelling pressure, and filling rate were 3 h, 710 °C, 30 °C, 65 kPa, and 0.33 kPa·s−1, respectively. Macroscopic properties and microstructural features were simulated using a combination of ProCAST and Thermal-Calc software. Additionally, a series of experimental procedures, including EBSD, SEM, EDS, and XRD tests, were conducted to investigate the formation, morphology, and distribution of the microstructure. Current findings demonstrate that during the solidification process, the maximum temperature difference exceeds 220 °C, the maximum undercooling reaches 49.56 °C, and the maximum effective stress peaks at 301.7 MPa. The residual stress ranges from 20 to 200 MPa. Furthermore, there is no significant texture and preferred grain orientation during nucleation and solidification, and the structure field exhibits an equiaxed crystal but inhomogeneous pattern. Moreover, the finer and more homogeneous the grains and the smaller the deviation in grain orientation, the lower the residual stress. Specifically, two analytical models are developed: one between the casting parameters and the residual stress, and another between the shrinkage defects and the casting parameters. The lowest residual stress is achieved when the knockout time, pouring temperature, preheating temperature of the sand mould, dwelling pressure, and filling rate are 6 h, 725 °C, 250 °C, 95 kPa, and 1.67 kPa·s−1 respectively.

Effects of Mold Temperature on the Microstructures and Tensile Properties of the Thixoforged Graphite/AZ91D Composite

The effects of mold temperatures on the microstructures and mechanical properties of thixoforged Grp (graphite particles)/AZ91D composites have been investigated, followed by partial remelting and thixoforging technology. The results indicate that the best semi-solid microstructure could be obtained after being partially remelted at 600 °C and held for 60 min. Correspondingly, under a mold temperature of 300 °C, the best tensile properties were obtained by thixoforging. The UTS (ultimate tensile strength) and elongation of the thixoforged Grp/AZ91D were up to 304.1 MPa and 13.9%, respectively, which increased 11.3% and 43.1% in comparison with the thixoforged AZ91D, respectively. The variation of the tensile properties responded to the influences of mold temperatures on the amount of eutectic phase, the distribution of Grp, and the grain size of α-Mg. Meanwhile, HRTEM (High Resolution Transmission Electron Microscope) showed good bonding between Grp and AZ91D, and many edge dislocations were found in the inverse FFT (Fast Fourier Transform) image. And the result showed that the increase in tensile properties is attributed to the synergistic effect of load transfer, dislocation strengthening, and Orowan looping mechanisms from the Grp strengthening the matrix.

Effect of injection molding conditions on the properties of polyamide 6/calcium carbonate nanocomposite

AbstractThe application range of polyamide 6 in lightweight part applications can be significantly increased by using appropriate processing parameters and by incorporating additives. Therefore, specimens were manufactured at varying mold temperatures and injection velocities to study the effect of injection molding parameters on the properties of polyamide 6 and its nanocomposite with nano‐calcium carbonate. Mechanical properties, degree of crystallinity, density, heat deflection temperature, and melt flow index were measured to assess the effect of injection molding conditions on material properties. Mold temperature was the process factor that most affected polyamide 6 and polyamide 6/nano‐calcium carbonate properties. Increasing this parameter increased density, impact strength, flexural strength, flexural modulus, and heat deflection temperature. The nanocomposite's degree of crystallinity, tensile modulus, and melt flow index also increased. High injection velocity reduced only the impact strength of the nanocomposite. The findings indicate that by modifying injection parameters, it is possible to improve mechanical properties and processing efficiency of polyamide 6 and its nanocomposites, bringing them closer to their maximum potential. Incorporating the nanofiller increased almost all properties except for impact strength and elongation at break. Nano‐calcium carbonate provided considerable advantages to polyamide 6 mechanical performance and processability while increasing sample weight by only 1.5%.

Regulation of Entanglement Networks under Different Shear Fields and Its Effect on the Properties of Poly(l-lactide)

Entanglement will lead to a sharp increase in viscosity and a decrease in flowability of polymers and affect their crystallization behavior adversely. Thus, it seems that disentanglement becomes a feasible way to improve the flowability and crystallization ability of polymers without adding other substances. Poly(l-lactide) (PLLA), as a renewable polymer material, has attracted increasing attention in the past decades. Accelerating crystallization of PLLA is extremely important to improve its performance. The present study utilizes a self-designed polymer melt disentanglement machine (PMDM) with PLLA as the experimental material, which can effectively promote polymer disentanglement by applying a unique complex shear field (superposition of rotational and vibrational shear fields) to a polymer melt. The experimental results show that compared with the untreated material, the disentangled ones have a lower viscosity and a higher melt flow rate and can fill the mold cavity at a lower pressure and a lower melt or mold temperature during the injection molding process. Simultaneously, the crystallization kinetics of the disentangled materials is accelerated, the crystallinity is higher under the same secondary processing conditions, and the heat resistance and mechanical properties of prepared products are better. This study provides new ideas for resolving the conflict between mechanical properties and processing flowability, improving the heat resistance of PLLA, and expanding its application range.

A mathematical examination of the impact of mould transparency to infrared radiation on solidification during the investment casting process

Investment casting is a highly dynamic process during which multiple competing physical phenomena are at work. Those seeking to understand and simulate such processes computationally are confronted with a considerable task, balancing accuracy with efficiency. Approximations and models based on well-understood and documented fundamental physics are powerful tools in a modeller’s arsenal. Driven by observed discrepancies between experimental thermocouple measurements and simulation predictions of casting temperatures, this work explores the additional alloy cooling mechanism of mould transparency to infrared radiation, targeting a new mathematical approximation applicable in such situations. Direct attenuation, scattering from coarse sand, sand distribution in the mould and material temperatures play a role in the extent of radiation transparency that must be considered. From this model, estimation of the additional cooling rate resulting from expected mould transparency can be determined and applied as a corrective measure to computation fluid dynamics (CFD) simulation results that do not capture this phenomenon.

Physical and Mechanical Properties of Starch Foam Reinforced with Pineapple (Ananas comosus) Leaves Powder

Investigation of capability of high potential pineapple (Ananas comosus) leaves powder (PLP) as reinforcing material on the starch-based foam production has not been reported. This study was aimed to characterize the physical and mechanical properties of starch foam formed from the mixture of cassava starch and PLP. The foams were made using a baking process on a thermo-pressing machine at temperature of 170°C and 180°C of the lower and upper mold temperature during 120 seconds. The starch foams were formulated with four different concentrations (0, 5, 10, and 15%) of PLP as reinforcement and tested for their characteristics of mechanical (compressibility) strength, water adsorption, morphological structure, density, color appearance, and biodegradability. Addition of PLP up to 15% improved the physical and mechanical properties of the foams.

Numerical simulation of squeeze casting of aluminum alloy flywheel housing with large wall thickness difference and complex shape

The squeeze casting process of ZL104 aluminum alloy flywheel housing with large wall thickness difference and complex shape was simulated by ProCAST software. The results show that the filling process was stable and could be divided into four stages: connection of channels, filling of horizontal zone, vertical direction, and difficult-filling zone. There were six characteristic zones with solidification lag, where shrinkage cavity and shrinkage porosity were obvious. The location prediction of defects was accurate. Through range analysis of defects volume, the optimal combination of process parameters was determined as pouring temperature of 650 °C, specific pressure of 48 MPa, mold temperature of 220 °C, local specific pressure of 800 MPa, and pressure delay time of 10 s (Side A) and 12 s (Side B). The maximum stress occurred in the thin-walled structure with fast solidification and large curvature. The simulation results were verified by the actual process.

Effects of Processing Method and Parameters on the Wall Thickness of Gas-Projectile-Assisted Injection Molding Pipes.

Gas-Projectile-Assisted Injection Molding (G-PAIM) is a new injection molding process derived from the Gas-Assisted Injection Molding (GAIM) process by introducing a projectile to it. In this study, the short-shot method and the overflow method of both the G-PAIM and GAIM processes were experimentally compared and investigated in terms of the wall thickness of the pipes and its uniformity. The results showed that the wall thickness of the G-PAIM molded pipe was thinner and more uniform than that of the GAIM molded pipe, and the wall thickness of the pipe molded by the Gas-Projectile-Assisted Injection Molding Overflow (G-PAIM-O) process was the most uniform. For the G-PAIM-O process, the influence of processing parameters, including melt temperature, gas injection delay time, gas injection pressure, melt injection pressure and mold temperature, on the wall thickness and uniformity of the G-PAIM-O pipes were studied via the single-factor experimental method. It was found that the effects of gas injection delay time and gas injection pressure on the wall thickness of the G-PAIM-O pipes were relatively significant. The wall thickness of the pipes increased with the increase in gas injection delay time and decreased with the increase in gas injection pressure. The melt temperature, melt injection pressure and mold temperature had little effect on the wall thickness of the G-PAIM-O pipes. In general, the wall thickness uniformity of the G-PAIM-O pipes was slightly affected by these processing parameters.

Evaluation of the injection molding process behavior during start-up and after parameter changes using dynamic time warping correspondences

Injection molding is one of the most widely used plastic processes due to its mass production capability and low production costs. Both simple and complex part geometries can be produced in short cycle times which, with other manufacturing processes, would require long production times. To control the process, it is adjusted by a variety of parameters. In addition to the machine configurations, disturbances, the thermal behavior of the machine components and the process control influence the process behavior and the resulting part quality.Due to the large number of machine parameters and the complexity of the process, it is not apparent to the machine operator how long the machine needs after switching on until the process runs stable. Up to now, the quality characteristics of the injection molded parts are determined by extensive tests in order to evaluate the process behavior. Thereby mostly the weight is used representatively as quality characteristic for process and quality control because it can be determined non-destructively, and it is closely related to other quality characteristics (such as dimensional properties).The aim of this research was to analyze the time series of the machine process data and to assess an automated method for evaluating the start-up behavior and the machine behavior after machine parameter changes. For this purpose, dynamic time warping correspondences (DTW) was used to investigate the process behavior based on high-resolution process data (here: injection pressure, flow rate, and screw volume).The method presented in this paper makes a significant contribution to automating the start-up process and reducing the testing effort. In addition, the investigations provide insightful findings for analyzing the process behavior after parameter changes. To improve the outcome, the mold temperature and cavity pressure should also be evaluated with the presented method in future investigations.

Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA

Plastic injection molding is one of the most used methods for producing plastic products because it can be produced at a high production rate, low cost, and ease in manufacturing. However, one defect that affects product quality is namely warpage. To reduce plastic product warpage, the injection molding process is required optimal process control to increase plastic product quality. The objective of this paper is to optimize injection molding process parameters for minimizing the warpage of plastic glass. The optimization process is divided into two phases. The Finite Element Method (FEM) was employed in the first phase to simulate 32 experiments under various parameters. The parameters of this process consist of melt temperature ranging from 180 to 230 °C, mold temperature in the range of 20 – 45 °C, filling time from 0.82 to 0.92 s, packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA) was developed to predict the warpage and solve the optimization process to find optimal parameters. Combining the intelligent method shows that ANN and GA effectively find the optimal process parameters that can reduce the warpage of the product by 35.73% from the maximum value.

A Novel Sensing Feature Extraction Based on Mold Temperature and Melt Pressure for Plastic Injection Molding Quality Assessment

Injection molding is one of the polymer molding methods. Product quality mainly can be affected by temperature and pressure. To observe the process of the melt forming in the mold, it is required to monitor the signal with the sensor. To achieve the purpose of robust molding, this research provides a new design-process window architecture based on machine learning of the injection product quality assessment. The process window is also known as the forming area map. The monitored value for defective products will be above or below the variable limit. The process is set to the center of this window so that any variation within the window will still result in an acceptable product. There are some potential problems in the current process. First, the conventional process window uses a small number of process variables as monitoring indicators, and it cannot provide stronger monitoring indicators. Second, most of the conventional process windows are 2-D and there are many monitoring windows, so the information that can be monitored is limited. Therefore, product quality cannot be effectively monitored without proper selection of important factors. Finally, the current process window can only provide information in the process and cannot make product quality prediction. In order to solve the above problems, a machine learning method is proposed to design the process window. The random forest classifier and regressor are used to predict the product quality. In order to improve the usability of the factor, the design of the proposed new variable is derived from the classical formula of thermodynamics (heat conduction). Using existing factors and proposing time-based feature changes can better represent the molding process as a whole. Based on the experimental results, the proposed variables can effectively increase the predictive performance of random forest model for quality assessment. The research results make a satisfactory result to the quality assessment of industrial injection molding. The accuracy of the prediction results is 100%. Moreover, it can be found from the 3-D process window that the data are very concentrated and can be clearly classified by the hyperplane, which is defined as the shortest distance between two clusters, and this space is the boundary of the process window. Therefore, compared with the past, the judgment boundary based on the random forest process window is no longer a simple boundary. Random forests can provide more accurate visualization boundaries.

Possibility of infrared (IR) thermography camera to investigate properties of injected plastic product and optimisation injection process parameter using a design of experiment

ABSTRACT The injection processing parameters have an important role in product quality. The purpose of this research is to estimate the usage of infrared (IR) thermal camera in automatic quality control of polymer parts in the production lines compared to simulation. The evaluation of injected part properties monitored from an IR camera is studied in terms of the defect and mechanical properties. The optimisation of the processing parameters to increase mechanical properties and decrease injection defects is examined. There are four processing factors with four levels namely, melt temperature, mould temperature, injection pressure, and flow rate designed by using Taguchi’s experimental design. The effect of average temperature of injection moulded parts monitored by IR camera on defect and mechanical properties is determined. The variance (ANOVA) method is used to evaluate the influence of each factors. The parameter factors of all experiments are definitely different because of P-values less than 0.05. The optimum processing parameters of PP were a melting temperature of 193°C, mould temperature of 30°C, injection pressure of 50 bar, and flow rate of 40 cm3/s. The suitable processing conditions for ABS are melting temperature, mould temperature, injection pressure, and flow rate of 216°C, 50°C, 90 bar, and 20 cm3/s respectively. IR thermograph accurately monitors short shot, sink mark, and large flash in moulding part; moreover, IR camera can estimate the mechanical properties of ABS by average temperature but cannot accurately measure the mechanical properties of high glossy PP. It is meant that IR thermal imaging technique can automatically control the product quality in the injection moulding process.

Modeling Analysis of Melting and Crystallization Process of Mold Flux Based on the Image Processing Technology

The aim of this paper is to obtain the image information based on a given image of mold flux and to obtain the features that can describe the dynamical difference. The melting and crystallization dynamics of the slag were analyzed using the autoregressive moving average (ARIMA) time series model and data fitting method. Firstly, the binary image of the digital region of the original image was obtained by image information processing and segmentation methods, the original image number was determined by comparing the similarity of the information matrices of the given and standard images. The standard number with the highest similarity was considered as the number of the original image, and MATLAB was used to solve the problem, the digital information in all the images was successfully extracted. Secondly, ten eigenvalues were extracted from the given image after removing the background, and three principal components were obtained by principal component analysis. Then, a scoring model was constructed based on the percentage of variance, and the comprehensive scores of the three principal components to analyze the melting and crystallization process of the mold flux. Finally, based on the above work, the dynamic relationship between temperature, time and the melting and crystallization process of the mold flux was investigated. Since the temperature is approximately linearly correlated with time, the problem was transformed into finding the relationship between the melting and crystallization process of the mold flux and time. The least squares method, polynomial fitting and other methods were used to derive the relationship function, the relationship between the melting and crystallization process of mold flux and temperature and time was quantitatively analyzed.

A Study on the Impact of Silicon and Manganese on Peritectic Behavior in Low Alloy Steels Assisted by Mold Thermal Mapping Technology and Shell Growth Measurements

Non-uniform shell growth commonly caused by the peritectic transformation in low carbon and low alloy steels has been directly correlated with mold thermal maps using a mold immersion test into a molten steel alloy. Mold thermal maps were obtained by performing real-time temperature measurements with optical fibers embedded 1 mm from the mold working surface. Shell growth measurements were obtained by 3D optical scanning of the recovered steel shell following immersion testing. The effects of silicon and manganese on the shell growth and mold temperature maps have been examined in relation to the peritectic transformation for varying carbon contents. Results of thermodynamic and kinetic simulations using ThermoCalc and its kinetic module—DICTRA predicted that silicon expands the carbon content range to values between (0.12 pct C to 0.21 pct C) where peritectic behavior occurs and that manganese shrinks the carbon content range and shifts the range to lower carbon contents (0.06 pct C to 0.12 pct C). The results of these simulations were used to guide the selection of chemistries examined in the dip test experiments with the aim of validating the software predictions. Dip tests were performed at similar steel making temperatures that were used for the software simulations between 1600 °C and 1650 °C. Both software results and experimental observation showed relatively good agreement for the compositions examined.