Relationship between mechanical and metallurgical properties with machinability when drilling high-strength cast irons

Silva, Aline Elias da; Silva, Leonardo Rosa Ribeiro da; Reis, Alcione dos; Machado, Álisson Rocha; Guesser, Wilson Luiz; Ezugwu, E. O.

doi:10.1007/s00170-019-04677-z

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…The CGI C material showed slightly inferior machinability than CGI B, with the probable explanation caused by the presence of Mo carbides, which, due to their high hardness (Figure 3(d)), increases the average hardness of the material (Figure 3(c)), thus increasing the tool wear. The correlation between the tool life and the mechanical properties of the graphitic cast irons was also observed in a similar trend by Da Silva et al 22 in the drilling process and Da Silva et al 23…”

Section: Resultssupporting

confidence: 81%

Tool wear monitoring in drilling of high-strength compacted graphite cast irons

Silva

Claro

Andrade³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Machining is such a complex system that any foreseen result is practically impossible. However, research always helps to further understand the process, which contributes to providing positive results. Tool wear is always difficult to foresee, but it can be measured and related to several output parameters. In each individual application, there will be the best parameter that most reliably represents the tool wear. In the present investigation, the hole quality parameters (roughness and cylindricity), power consumption, electrical consumption of the machine tool and the acoustic emission signals were recorded and correlated to the tool condition in order to find the best output parameter for tool wear monitoring during the drilling of compact graphite cast irons. Two high-strength grades of compacted graphite cast irons (both CGI 500 with changes in the matrix and graphitic structure) were machined and compared to the standard grade (CGI 450) usually used in the manufacturing of engines using TiAlN-coated carbide drills at a constant cutting condition. The results showed that the best output parameter to monitor the tool wear was the electric current signal.

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Section: Resultssupporting

confidence: 81%

Tool wear monitoring in drilling of high-strength compacted graphite cast irons

Silva

Claro

Andrade³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…To evaluate the correlations is proposed the representation shown in Table 6. The average values of iartificially manufactured tool wear (Table 4) will be correlated using the Pearson index with the roughness parameters of the workpiece (Table 5), in a similar way that was proposed by Da Silva, Da Silva et al 20 . The direct correlations will have the corresponding cell coloured in green and the inverse correlation in red.…”

Section: Roughness Parametersmentioning

confidence: 99%

Engenharia Mecânica: Inovações Tecnológicas de Elevado Valor

Holzmann¹,

Dallamuta²

2021

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Direitos para esta edição cedidos à Atena Editora pelos autores. Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

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“…When machining cast iron, wear occurs in various forms, i.a. abrasion, chipping, diffusion and adhesion [1][2][3]. There are many articles the concerns monitoring the condition of cutting tools.…”

Section: Introductionmentioning

confidence: 99%

Identification of Tool Wear During Cast Iron Drilling Using Machine Learning Methods

Twardowski¹,

Tabaszewski²,

Felusiak-Czyryca³

et al. 2022

Adv. Sci. Technol. Res. J.

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The paper concerns the monitoring of the tool condition on the basis of vibration acceleration signals. The cutting edge condition is determined by wear on the flank surface of the drill. As tools, a twist drills made of cemented carbide were used. A gray cast iron plate EN-GJL-250 was used as the workpiece. Based on the signals, appropriate measures correlated with the wear of the drill were developed. By using binary decision trees CART (Classification and Regression Tree) with two data partitioning methods (Gini index and Cross-entropy), the original number of measures was limited to the most common and those that provide the smallest error in the tool condition classification. Comparing the results for the best trees built with different measures of partition quality in nodes for all available data indicated a better performance of the Gini index. The applied solution allows for high accuracy of the tool classification. The solution is to be used in industry.

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Relationship between mechanical and metallurgical properties with machinability when drilling high-strength cast irons

Cited by 12 publications

References 16 publications

Tool wear monitoring in drilling of high-strength compacted graphite cast irons

Tool wear monitoring in drilling of high-strength compacted graphite cast irons

Engenharia Mecânica: Inovações Tecnológicas de Elevado Valor

Identification of Tool Wear During Cast Iron Drilling Using Machine Learning Methods

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