The origin of graphite morphology in cast iron

Tewary, Ujjal; Paul, Deepanjan; Mehtani, Hitesh Kumar; Bhagavath, S.; Alankar, Alankar; Mohapatra, Goutam; Sahay, Satyam S.; Panwar, Ajay S.; Karagadde, Shyamprasad; Samajdar, Indradev

doi:10.1016/j.actamat.2022.117660

Cited by 25 publications

(14 citation statements)

References 41 publications

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“…However, the possibility of gray cast-iron applications in challenging areas demands the characteristic morphology of the graphite phase. The matrix of gray cast iron consists of a soft ferrite phase and lamellar pearlite phase [10,11]. However, mechanical properties depend on the chemical composition, the type of matrix and form of graphite (flake, compacted, nodular), or heat and cooling cycle [12,13].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

et al. 2022

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The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut ap and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

et al. 2022

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“…Cast iron offers high castability and good machining properties, as well as interesting mechanical properties [1][2]. Due to the economic benefits it offers, cast iron is used for many applications such as water pipes [3][4][5], machine building, in the automotive industry and in metal fabrication and processing [2].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the economic benefits it offers, cast iron is used for many applications such as water pipes [3][4][5], machine building, in the automotive industry and in metal fabrication and processing [2]. Until today, cast iron still constitutes the majority (≈70%) of metal castings [1,6]. In addition, specific cast irons are the material of choice for seawater pump casings, rolling mill rolls and earthmoving machinery parts [2,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Production of Lamellar Cast Iron EN-GJL-150 From Local Manganese-Rich Pig Iron by Modification of the Melting Process

Sitouah

Cherfi

Oualit

et al. 2022

Advances in Materials Science

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In the present study, a modified duplex melting process was set up so as to be able to produce an EN-GJL-150 gray cast iron from a local manganese-rich pig iron. A descriptive statistics showed an average Mn and Si content in raw material such that: Mn % = 2.457±0.133 and Si % = 0.682±0.088. The demanganization process was run and monitored in a cascade of two industrial-scale furnaces: a rotary kiln and an electric arc furnace. The performed experiments indicated that: 1) the manganese content decreased from 2.45 % to 0.94 %, 2) the manganese oxidation obeys the first order kinetic model, 3) Brinell and Rockwell hardness’s decreased by 38.83% and 27.81% respectively, and 4) the produced cast iron has a pearlitic microstructure with a small fraction of ferrite (1 to 5%) in the matrix and traces of cementite. All results showed that the produced castings comply with the standards in force for EN-GJL-150 cast irons, similar to gray cast iron ASTM A48 Class 20.

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“…Sulfur promotes precipitation of such non-metallic substrates, increasing the nodule count [3]. Applications of cast irons, from mundane to more challenging, are decided by the morphology of free graphite in the metallic matrix [4].…”

Section: Introductionmentioning

confidence: 99%

Influence of Si content on the machinability of ductile iron

Xavier¹,

Silva²,

Guesser³

et al. 2022

Tecnol. Metal. Mater. Min.

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The development of new chemical compositions of cast iron contributes to increasing the competitiveness of foundry and manufacturing companies that always seek to optimize the properties of these materials. Previous works detected machinability variation along the cross-section of nodular cast irons (DI) bars produced by continuous casting due to the variation of the microstructure and mechanical properties presented, with decreasing machinability from the periphery to the core. This variation imposes complications, as it implies different ideal (or optimal) cutting conditions along the crosssection of the round bars. Assays with ductile irons (DI) with varying silicon content showed that the reduction in silicon decreased the microhardness of ferrite and increased the percentage of pearlite in the matrix, thus generating a decrease in the ductility of the material. Thus, this work investigates the effect of silicon content on machinability variation along the cross-section of nodular cast iron bars in the turning process. Three different types of nodular cast iron alloys were used, differing by the amount of silicon in the composition. Material characterization and machining tests were carried out in three regions: the periphery, the intermediate region, and the core. The hardness, metallography, size of graphite nodules, and amount per region were analyzed in the characterization tests and compared to the evolution of wear and tool life machining force and surface roughness. The addition of different percentages of silicon did not avoid the differences in behavior between the three regions of the same material, but it changed the behavior of the three alloys studied. The silicon content affects the material's microstructure, which, together with the cooling rate, favors the differentiation of graphite nodules in different sizes, arrangements, and spacing. This differentiation contributes to the difference in the material's behavior in the machining between the regions along the cross-section. Strengthening in the core region was a trend in all three alloys. There was a statistical difference in roughness between the regions of the material. Cast iron with medium silicon content showed lower machining force and roughness values but close to those of high silicon. As with high-silicon cast iron, the forces remained at a more stabilized level, without significant variations between regions of the material.

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The origin of graphite morphology in cast iron

Cited by 25 publications

References 41 publications

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

Production of Lamellar Cast Iron EN-GJL-150 From Local Manganese-Rich Pig Iron by Modification of the Melting Process

Influence of Si content on the machinability of ductile iron

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