Casting and Casting Processes

Mahto, D. G.

doi:10.2139/ssrn.2776565

Cited by 2 publications

(5 citation statements)

References 107 publications

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“…The physical properties of sand depends upon its grain size, grain shape, moisture, and clay contents of the moulding sand . The Grain shape and grain distribution areimportant factors in the selection of sands for the various processes [16].…”

Section: Resultsmentioning

confidence: 99%

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Study of the Reclamation of Waste CO2 Moulding Sand in Foundry Industry

2020

IJETER

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The study of the reclamation of waste CO 2 moulding sands aims to exercise further studies on treatment of CO 2 waste moulding sands and to ensure the final chemical composition of the CO 2 waste moulding sands. The research gives information about the potential of waste sand reclamations. In addition, this research aims to develop SOPs or signs in treatment waste sand.Reclamation process was begun in the crushing of sand lump by hammer and mixing machine reached granular size. The filtered process was done to separate the sand grains from the large size. Then, the washing process of the waste CO 2 moulding sand using detergent solution with variation concentrations of detergent 0.2; 0.3; and 0.4%, respectively. A newand waste silica sand were characterized to study physical and chemical properties. The characterization including grain shape, clay content, and grain fineness number (GFN). The morphology and element content in sands were observed under SEM-EDS instrument.The research shows that the optimum reclamation method is using 0.3% detergent. It results angular grain shape, clay content 2.58%, and GFN 42.16. The morphology and elemental scanning of using 0.3% detergent shows decrement 10.97% of sodium.Based on study was concluded that the detergent wash method is effective to improve the properties of the waste silica sand nearer to new silica sands. The CO 2 moulding sand reclamation can be reused again. In addition, the method of this research can be used in reference of the reclamation of waste CO 2 moulding sand.

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Section: Resultsmentioning

confidence: 99%

“…The optimum reclamation method is using 0.3% detergent, it results angular grain shape, clay content 2.58%, GFN 42. 16. The morphology and elemental scanning of using 0.3% detergent shows decrement of sodium 10.97%.…”

Section: Discussionmentioning

confidence: 97%

Study of the Reclamation of Waste CO2 Moulding Sand in Foundry Industry

2020

IJETER

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“…So we need to take that public data to process the dataset optimally. Machine learning has more benefits by making the standard datasets [24] If we want to use any unfilled dataset, we need to incorporate the missing data as zero or average value or mode setting. Machine learning has some procedures to solve this issue by feature scaling, average removal, and scaling differ-entiation.…”

Section: Implementation Of Machine Learningmentioning

confidence: 99%

“…Machine learning has some procedures to solve this issue by feature scaling, average removal, and scaling differ-entiation. Using this method, we can improve the accuracy of output [24].…”

Section: Implementation Of Machine Learningmentioning

confidence: 99%

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Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

BramahHazela

Hymavathi²,

Kumar³

et al. 2022

Journal of Nanomaterials

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In this paper, we represent a method for machine learning to predict the defect in foundry operation. Foundry has become a driving tool to produce the part to another industry like automobile, marine, and weapon. These foundry processes mainly have two critical problems to decrease the quality assurance. Now, we have to predict the defect to increase the quality of foundry operation. The foundry process’s failure is associated with micro shrinkage and ultimate tensile strength. We process by utilizing a machine learning classifier to predict the micro shrinkage and maximum tensile strength and describe the process, learning process, and evaluate the predataset from the foundry process to compare the accuracy and stability.

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Casting and Casting Processes

Cited by 2 publications

References 107 publications

Study of the Reclamation of Waste CO2 Moulding Sand in Foundry Industry

Study of the Reclamation of Waste CO2 Moulding Sand in Foundry Industry

Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

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