Study on effect of temperature smelting and pouring to mechanical properties Aluminum 7075

Klemens, A.; Rahangmetan,; Cipto,; Sariman, Farid; Syahid, Muhammad

doi:10.1088/1755-1315/343/1/012166

Cited by 5 publications

(2 citation statements)

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“…Moisture content, sand grain fineness number, green compression strength, permeability, mold hardness, pouring time, pouring temperature and mold pressure significantly affect the casting defects (Upadhye, 2012). It was also statistically established that pouring temperature significantly affects mechanical properties of casting (Ding et al, 2014;Jahangiri et al, 2017;Klemens et al, 2019;Li et al, 2020). Weight of CO2, mold hardness, sand particle size, content (Noorul Haq et al, 2009).…”

Section: Literature Review Methodsmentioning

confidence: 99%

Analytical modeling of quality parameters in casting process – learning-based approach

Suthar

Persis

Gupta

2022

IJQRM

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PurposeFoundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.Design/methodology/approachThe literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.FindingsThe authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.Originality/valueThis study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

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Section: Literature Review Methodsmentioning

confidence: 99%

Analytical modeling of quality parameters in casting process – learning-based approach

Suthar

Persis

Gupta

2022

IJQRM

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“…Aluminum is a type of lightweight material that is widely consumed, not only for its own purposes but also for the needs of aircraft companies, cars, ships and other construction materials. Al alloy is not difficult to melt because it has a melting temperature of 600℃ -700℃ Therefore, casting Al is widely applied to companies both in small [3], medium and large This allows casting to be carried out in small industries where the availability of energy and machinery is limited. Besides the mechanical properties of this alloy, especially its hardness value can be improved by taking into account casting process parameters such as pouring temperature and melting temperature [4].…”

Section: Introductionmentioning

confidence: 99%

Utilization of bus tree wood (Melaleuca sp) as environmentally friendly fuel for household industrial scale Aluminum smelting stoves in Merauke, Papua

et al. 2021

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The purpose of this study is the use of bus wood trees as fuel for aluminum smelting for the home industry because it has a good heating value and is very efficient, from the laboratory test results, the bus tree wood has a calorific value of 5981 (calories/g). The methods used experimental and analytical to get the results from the use of bus wood as fuel for aluminum smelting which will certainly produce good mechanical properties. The results obtained a good combustion process that releasing all the heat contained in a fuel and done with three combustion control processes, including a fairly high temperature to ignite and maintain the flame of the fuel, turbulence or mixing O2 and the use of good fuel and sufficient time to complete combustion process. The fuel (bus wood) which is inputted in the furnace is designed so that the combustion process takes place more completely with minimal heat losses. The results of temperature changes seem a thermocouple and crucible furnace insulator material were very good heat resistance hence the time of the melting process it doesn't take longer which is 15 minutes. The aluminum in crucibles has melted at a temperature of 723℃.

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Design and Fabrication of a Dip Immersion Probe for Melting and Pouring Practice in Casting Technology

Hong,

Kamis,

Guan

2023

Advanced Structured Materials

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Study on effect of temperature smelting and pouring to mechanical properties Aluminum 7075

Cited by 5 publications

References 1 publication

Analytical modeling of quality parameters in casting process – learning-based approach

Analytical modeling of quality parameters in casting process – learning-based approach

Utilization of bus tree wood (Melaleuca sp) as environmentally friendly fuel for household industrial scale Aluminum smelting stoves in Merauke, Papua

Design and Fabrication of a Dip Immersion Probe for Melting and Pouring Practice in Casting Technology

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