30 research outputs found

    Application of Response Surface Methodology for Modeling of Postweld Heat Treatment Process in a Pressure Vessel Steel ASTM A516 Grade 70

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    This research studied the application of the response surface methodology (RSM) and central composite design (CCD) experiment in mathematical model and optimizes postweld heat treatment (PWHT). The material of study is a pressure vessel steel ASTM A516 grade 70 that is used for gas metal arc welding. PWHT parameters examined in this study included PWHT temperatures and time. The resulting materials were examined using CCD experiment and the RSM to determine the resulting material tensile strength test, observed with optical microscopy and scanning electron microscopy. The experimental results show that using a full quadratic model with the proposed mathematical model is YTS=-285.521+15.706X1+2.514X2-0.004X12-0.001X22-0.029X1X2. Tensile strength parameters of PWHT were optimized PWHT time of 5.00 hr and PWHT temperature of 645.75°C. The results show that the PWHT time is the dominant mechanism used to modify the tensile strength compared to the PWHT temperatures. This phenomenon could be explained by the fact that pearlite can contribute to higher tensile strength. Pearlite has an intensity, which results in increased material tensile strength. The research described here can be used as material data on PWHT parameters for an ASTM A516 grade 70 weld

    Influence of Shielding Gas on Aluminum Alloy 5083 in Gas Tungsten Arc Welding

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    AbstractThe influence of shielding gas parameter was affect to mechanical properties and microstructures of heat-affected zone and fusion zone on gas tungsten arc welding (GTAW) in aluminium alloy AA 5083. The factorial experiment was designed for this research. The factors of AA 5083 weld used in the study types of shielding gas in argon and helium, gas flow rate at 6, 10 and 14 litres per minute. Then the results were using microstructure and vickers hardness test. The result showed that types of shielding gas and gas flow rate interaction hardness at heat-affected zone and fusion zone with a P–value < .05. The factor which was the most effective to the hardness at heat-affected zone and fusion zone was argon with a flow rate of 14 litres per minute at heat-affected zone with 74.27 HV and fusion zone with 68.97 HV. The helium was high thermal conductivity, resulting in a large amount of heat. The grain size was grain growth in larger grain size. This can result in decreased of hardness. Experimental results showed that the argon condition provided smaller grain size, suitable size resulting in higher hardness both in weld metal and HAZ. This research can be used for data on considering on gas tungsten arc welding of aluminium alloy 5083

    Investigation of the Effects of Submerged Arc Welding Process Parameters on the Mechanical Properties of Pressure Vessel Steel ASTM A283 Grade A

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    The pressure vessel steel is used in boilers and pressure vessel structure applications. This research studied the effects of submerged arc welding (SAW) process parameters on the mechanical properties of this steel. The weld sample originated from ASTM A283 grade A sheet of 6.00-millimeter thickness. The welding sample was treated using SAW with the variation of three process factors. For the first factor, welding currents of 260, 270, and 280 amperes were investigated. The second factor assessed the travel speed, which was tested at both 10 and 11 millimeters/second. The third factor examined the voltage parameter, which was varied between 28 and 33 volts. Each welding condition was conducted randomly, and each condition was tested a total of three times, using full factorial design. The resulting materials were examined using tensile strength and hardness tests and were observed with optical microscopy (OM) and scanning electron microscopy (SEM). The results showed that the welding current, voltage, and travel speed significantly affected the tensile strength and hardness (P value < 0.05). The optimum SAW parameters were 270 amperes, 33 volts, and 10 millimeters/second travel speed. High density and fine pearlite were discovered and resulted in increased material tensile strength and hardness

    The Impact of Sweetener Type on Physicochemical Properties, Antioxidant Activity and Rheology of Guava Nectar during Storage Time

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    This study aimed to evaluate the differences in physicochemical properties, antioxidant properties, and rheology between guava nectar with sucrose and guava nectar with neotame and stevia addition (0.01% and 0.05%). All parameters were investigated during refrigerated storage at 4 °C for 15 days. The result showed that all sweetened guava juice samples led to the juice with the greatest presence of overall appearance and antioxidant property. The flow behavior of sweetened guava juice was shear-thinning which was not different from guava juice without sweetener. However, the major volatile flavor compounds identified in all guava juice samples were Îē-caryophyllene, Îą-caryophyllene, bisabolene, aromadendrene, Îą-humulene, and nerolidol, which is not different from guava juice without sweeteners during storage. It indicated that stevia and neotame are good alternative sweeteners to produce low caloric juice production

    Investigation into the influence of post-weld heat treatment on the microstructure and hardness of Inconel X-750

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    This work describes a post-weld heat treatment for a precipitation-hardened nickel alloy. Inconel X-750 is a nickel-based superalloy for gas tungsten arc welding processes. The materials were heat-treated in two steps: solution and aging. The post-weld heat treatment variables examined in this study included post-weld heat treatment temperatures of 705°C, 775°C, and 845°C and post-weld heat treatment time of 2–24 h in 2-h increments. The resulting materials were examined using the full factorial design of experiments to determine the resulting material hardness and observed with optical microscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy in the fusion zone and heat-affected zone. The results show that a longer post-weld heat treatment time corresponds to larger Îģâ€ē precipitates and a smaller amount of Cr 23 C 6 at the grain boundaries, which can decrease the overall hardness. The post-weld heat treatment analysis indicates that an increase in the amount of Îģâ€ē results in better mechanical properties for particles with octagonal shapes and a small size. A factorial analysis, which was conducted on the relationship between the post-weld heat treatment temperature and time to the hardness of the fusion zone, had a 95% confidence level

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    āļšāļ—āļ„āļąāļ”āļĒāđˆāļ­āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļŠāļĢāđ‰āļēāļ‡āđāļĨāļ°āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļĢāļđāļ›āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ—āļēāļ‡āļ„āļ“āļīāļ•āļĻāļēāļŠāļ•āļĢāđŒāļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄāļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļāļēāļĢāļžāļ·āđ‰āļ™āļœāļīāļ§āļ•āļ­āļšāļŠāļ™āļ­āļ‡ āđ‚āļ”āļĒāļ­āļ­āļāđāļšāļšāļāļēāļĢāļ—āļ”āļĨāļ­āļ‡āđāļšāļšāļŠāđˆāļ§āļ™āļ›āļĢāļ°āļŠāļĄāļāļĨāļēāļ‡ āđāļĨāļ°āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāđ‚āļ„āļĢāļ‡āļ‚āđˆāļēāļĒāļ›āļĢāļ°āļŠāļēāļ—āđ€āļ—āļĩāļĒāļĄ āđƒāļ™āļāļēāļĢāļ—āļģāļ™āļēāļĒāļ„āđˆāļēāđāļĢāļ‡āļ”āļķāļ‡āđ€āļ‰āļ·āļ­āļ™āđāļĨāļ°āļ‚āļ™āļēāļ”āļ™āļąāļāđ€āļāļ•āļŠāļģāļŦāļĢāļąāļšāļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄāļ„āļ§āļēāļĄāļ•āđ‰āļēāļ™āļ—āļēāļ™āļŠāļ™āļīāļ”āļˆāļļāļ”āđƒāļ™āđ€āļŦāļĨāđ‡āļāđ€āļ„āļĨāļ·āļ­āļšāļŠāļąāļ‡āļāļ°āļŠāļĩ JIS G3313 āļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļĻāļķāļāļĐāļē 3 āļ›āļąāļˆāļˆāļąāļĒāđ„āļ”āđ‰āđāļāđˆ āļāļĢāļ°āđāļŠāđ„āļŸāļŸāđ‰āļē āđ€āļ§āļĨāļē āđāļĨāļ°āđāļĢāļ‡āļāļ”āļ­āļīāđ€āļĨāđ‡āļāđ‚āļ—āļĢāļ”āđƒāļ™āļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄ āļŦāļĨāļąāļ‡āļˆāļēāļāļ—āļģāļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄāđ„āļ”āđ‰āļĄāļĩāļāļēāļĢāļ—āļ”āļŠāļ­āļšāđāļĢāļ‡āļ”āļķāļ‡āđ€āļ‰āļ·āļ­āļ™ āļāļēāļĢāļ§āļąāļ”āļ‚āļ™āļēāļ”āļ™āļąāļāđ€āļāļ• āđāļĨāļ°āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđ‚āļ„āļĢāļ‡āļŠāļĢāđ‰āļēāļ‡āļˆāļļāļĨāļ āļēāļ„āļ”āđ‰āļ§āļĒāļāļĨāđ‰āļ­āļ‡āļˆāļļāļĨāļ—āļĢāļĢāļĻāļ™āđŒāļ­āļīāđ€āļĨāđ‡āļāļ•āļĢāļ­āļ™āđāļšāļšāļŠāđˆāļ­āļ‡āļāļĢāļēāļ” āļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļžāļšāļ§āđˆāļēāļŠāļ āļēāļ§āļ°āļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄāļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄ āļ„āļ·āļ­ āļāļĢāļ°āđāļŠāđ„āļŸāļŸāđ‰āļēāđƒāļ™āļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄ 12 āļāļīāđ‚āļĨāđāļ­āļĄāđāļ›āļĢāđŒ āđ€āļ§āļĨāļēāđƒāļ™āļāļēāļĢāđ€āļŠāļ·āđˆāļ­āļĄ 9 āđ„āļ‹āđ€āļ„āļīāļĨ āđāļĨāļ°āđāļĢāļ‡āļāļ”āļ­āļīāđ€āļĨāđ‡āļāđ‚āļ—āļĢāļ” 1.5 āļāļīāđ‚āļĨāļ™āļīāļ§āļ•āļąāļ™ āļŠāđˆāļ‡āļœāļĨāđƒāļŦāđ‰āļĄāļĩāļ„āđˆāļēāđāļĢāļ‡āļ”āļķāļ‡āđ€āļ‰āļ·āļ­āļ™ āļĄāļĩāļ‚āļ™āļēāļ”āļ™āļąāļāđ€āļāļ•āļ•āļēāļĄāđ€āļāļ“āļ‘āđŒāļāļēāļĢāļĒāļ­āļĄāļĢāļąāļšāļ•āļēāļĄāļĄāļēāļ•āļĢāļāļēāļ™ JIS Z3140:2017  āđ‚āļ„āļĢāļ‡āļŠāļĢāđ‰āļēāļ‡āļˆāļļāļĨāļ āļēāļ„āļšāļĢāļīāđ€āļ§āļ“āđ€āļ‚āļ•āļ­āļīāļ—āļ˜āļīāļžāļĨāļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļĄāļĩāđ€āļāļĢāļ™āļ‚āļ­āļ‡āđ€āļŸāļ­āļĢāđŒāđ„āļĢāļ—āđŒ āđ€āļžāļīāļĢāđŒāļĨāđ„āļĢāļ—āđŒāļĨāļ°āđ€āļ­āļĩāļĒāļ”āđāļĨāļ°āļŦāļ™āļēāđāļ™āđˆāļ™ āļšāļĢāļīāđ€āļ§āļ“āļ™āļąāļāđ€āļāļ•āđ€āļāļīāļ”āđ‚āļ„āļĢāļ‡āļŠāļĢāđ‰āļēāļ‡āđ€āļŸāļ­āļĢāđŒāđ„āļĢāļ—āđŒāļĢāļđāļ›āđ€āļ‚āđ‡āļĄāļ—āļĩāđˆāļĄāļĩāļ„āļ§āļēāļĄāļĨāļ°āđ€āļ­āļĩāļĒāļ” āļˆāļķāļ‡āļŠāđˆāļ‡āļœāļĨāđƒāļŦāđ‰āļŠāļīāđ‰āļ™āļ‡āļēāļ™āđ€āļŠāļ·āđˆāļ­āļĄāļĄāļĩāļ„āļ§āļēāļĄāđāļ‚āđ‡āļ‡āđāļĢāļ‡āļŠāļđāļ‡ āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ—āļēāļ‡āļ„āļ“āļīāļ•āļĻāļēāļŠāļ•āļĢāđŒāļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄ āļ„āļ·āļ­ āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ—āļēāļ‡āļ„āļ“āļīāļ•āļĻāļēāļŠāļ•āļĢāđŒāļˆāļēāļāļ§āļīāļ˜āļĩāđ‚āļ„āļĢāļ‡āļ‚āđˆāļēāļĒāļ›āļĢāļ°āļŠāļēāļ—āđ€āļ—āļĩāļĒāļĄ āđ‚āļ”āļĒāđ‚āļ„āļĢāļ‡āļŠāļĢāđ‰āļēāļ‡āđ‚āļ„āļĢāļ‡āļ‚āđˆāļēāļĒāļ›āļĢāļ°āļŠāļēāļ—āđ€āļ—āļĩāļĒāļĄāļŠāļģāļŦāļĢāļąāļšāļāļēāļĢāļ—āļģāļ™āļēāļĒāļ„āđˆāļēāđāļĢāļ‡āļ”āļķāļ‡āđ€āļ‰āļ·āļ­āļ™ āļ„āļ·āļ­ āļŠāļąāđ‰āļ™āļ­āļīāļ™āļžāļļāļ—āļˆāļģāļ™āļ§āļ™ 3 āļ™āļīāļ§āļĢāļ­āļ™ āļŠāļąāđ‰āļ™āļ‹āđˆāļ­āļ™āļˆāļģāļ™āļ§āļ™ 10 āļ™āļīāļ§āļĢāļ­āļ™ āđāļĨāļ°āļŠāļąāđ‰āļ™āđāļŠāļ”āļ‡āļœāļĨāļˆāļģāļ™āļ§āļ™ 1 āļ™āļīāļ§āļĢāļ­āļ™ (3-10-1) āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļāļģāļĨāļąāļ‡āļŠāļ­āļ‡āļ—āļĩāđˆ 0.0026 āļ„āđˆāļēāļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāļ—āļĩāđˆ 0.956 āļŠāļģāļŦāļĢāļąāļšāļ—āļģāļ™āļēāļĒāļ‚āļ™āļēāļ”āļ™āļąāļāđ€āļāļ• āļŠāļąāđ‰āļ™āļ­āļīāļ™āļžāļļāļ—āļˆāļģāļ™āļ§āļ™ 3 āļ™āļīāļ§āļĢāļ­āļ™ āļŠāļąāđ‰āļ™āļ‹āđˆāļ­āļ™āļˆāļģāļ™āļ§āļ™ 5 āļ™āļīāļ§āļĢāļ­āļ™ āđāļĨāļ°āļŠāļąāđ‰āļ™āđāļŠāļ”āļ‡āļœāļĨāļˆāļģāļ™āļ§āļ™ 1 āļ™āļīāļ§āļĢāļ­āļ™ (3-5-1) āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļāļģāļĨāļąāļ‡āļŠāļ­āļ‡āļ—āļĩāđˆ 0.0004 āļ„āđˆāļēāļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāļ—āļĩāđˆ 0.958 āđ‚āļ”āļĒāļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļ āļēāļ„āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāļāļēāļĢāļœāļĨāļīāļ•āļŠāļēāļĄāļēāļĢāļ–āļ™āļģāļ‚āđ‰āļ­āļĄāļđāļĨāļ§āļīāļˆāļąāļĒ āđāļĨāļ°āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ—āļēāļ‡āļ„āļ“āļīāļ•āļĻāļēāļŠāļ•āļĢāđŒāļ”āļąāļ‡āļāļĨāđˆāļēāļ§āđ„āļ›āđƒāļŠāđ‰āđ€āļžāļ·āđˆāļ­āļžāļĒāļēāļāļĢāļ“āđŒ āļ„āļ§āļšāļ„āļļāļĄāļ„āļļāļ“āļ āļēāļžāļ‚āļ­āļ‡āļĢāļ­āļĒāđ€āļŠāļ·āđˆāļ­āļĄāđƒāļŦāđ‰āđ„āļ”āđ‰āļ„āđˆāļēāđāļĢāļ‡āļ”āļķāļ‡āđ€āļ‰āļ·āļ­āļ™ āđāļĨāļ°āļ‚āļ™āļēāļ”āļ™āļąāļāđ€āļāļ•āļ•āļēāļĄāđ€āļāļ“āļ‘āđŒāļāļēāļĢāļĒāļ­āļĄāļĢāļąāļšāļ•āđˆāļ­āđ„āļ›AbstractThis research described to the determine an optimization mathematic model using response surface methodology in central composite design method and artificial neural network (ANN) for predicting the of tensile shear and nugget size in the zinc coated steel JIS G3313. The following resistance spot welding (RSW) parameters were studied: the welding current, welding time, and electrode force. The resulting materials were examined using tensile shear tests which were observed nugget size and microstructure with scanning electron microscopy (SEM). The microstructure phenomenon could be explained by the welding optimum condition that fine pearlite and intensity in heat affected zone. The research results reveal that an optimum RSW parameters were welding current of 12 kilo amperes, welding time of 9 cycle and 1.5 kilo newton electrode force. The fine acicular ferrite occurred in the nugget size, which results in increased welding material high mechanical property. The ANN model with the proposed mathematical model, which tensile shear represents 3 neurons for the input 10 neurons for 1 hidden layer and 1 output neurons (3-10-1). The ANN model was developed to establish of the nugget predict represents 3 neurons for the input 5 neurons for 1 hidden layer and 1 output neurons (3-5-1). The mean square error (MSE) and coefficient of determination (R2) for tensile shear predict was showed that of 0.0026 and 0.956 respectively, which nugget size predicted MSE of 0.0004 and R2 of 0.958. This research, the related manufacturing sector can use research data and mathematical models was used to predict and quality control of the RSW processes to obtain tensile shear and the nugget size according to the acceptance criteri
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