The effect of niobium addition on the microstructure and properties of cast iron used in cylinder head

Pan, Sining; Zeng, Fang; Su, Nanguang; Xian, Zhaokun

doi:10.1016/j.jmrt.2019.11.076

Cited by 31 publications

(15 citation statements)

References 22 publications

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“…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]. Furthermore, due to the specific distribution of graphite in gray iron microstructure, the tool wear mechanism during machining has been a research topic in recent years [14][15][16][17].…”

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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“…They decrease the quantity of carbon soluble in the austenite and thus increase the quantity of free carbide in the iron at a given carbon content. Recent research works [13,15,[19][20][21][22][23][24] have shown that, Si, Mn, Ni, Mo, V, Cu, Ti, Nb, W, Sn… are the typical alloying elements used to control ferrite and pearlite contents and increases mechanical properties in ductile iron as-cast grades. Mn and Cu are used to promote pearlite, Si is used to promote ferrite and to strengthen it.…”

Section: Introductionmentioning

confidence: 99%

Study of the Influence of Alloying Elements on the Mechanical Characteristics and Wear Behavior of a Ductile Cast Iron

Boulifa

Hadji

2021

Frattura Ed Integrità Strutturale

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In the present work, the influence of alloying elements, on the mechanical characteristics and wear behavior by modification of the chemical composition of the ductile iron was studied, in order to improve these characteristics for the manufacture of agricultural tractors parts in particular front and rear axles, ploughshares, gear crankcase, pinions, transmission shafts, crankshafts, etc... The cast iron investigated was prepared in an induction furnace at 1500°C and inoculated with a ferro-silicon-magnesium to 45% Si and 10% Mg. The specimens were casted into self-hardening sand moulds at 1450°C, after an addition of alloying elements, Manganese (0.6%), Nickel (0.5%), Molybdenum (0.2%) and Vanadium(0.1%)) in the base spheroidal graphite cast iron produced. Various techniques, Optical microscopy, Microhardness, Hardness, Tensile strength, Impact resistance, and Wear tests (Wear resistance and Friction coefficient) were used to characterize these specimens. The obtained results show that the tested samples have ductile iron structures formed by ferrite and pearlite. Moreover, mechanical and wear tests prove that the alloyed cast iron has improved characteristics compared to unalloyed cast iron and shows the positive effect of alloying elements on these characteristics.

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“…Grey cast iron is composed of a pearlite matrix, graphite flakes and precipitated carbides [ 1 , 2 , 3 ]. Due to the relatively simple production process, low cost, high recovery rate, high thermal conductivity, excellent machinability and casting performance, grey iron castings maintain a significant position in the market [ 4 , 5 , 6 ]. Recently, the method of adding a trace amount of niobium elements to improve the structure and mechanical properties of grey cast iron has received extensive attention [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

The Precipitation of Niobium Carbide and Its Influence on the Structure of HT250 for Automobile Wheel Hubs

et al. 2021

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Improving the strength of grey cast iron wheel hubs will improve the safety of automobiles and have a great significance for energy saving and environmental protection. This paper systematically compares the calculation results of Python-based precipitation calculation and JmatPro software simulation with experiments. The results show that with a low mass fraction of niobium (0.098%) cuboid Niobium Carbide (NbC) precipitates do not form in the liquid phase; however, an elongated NbC niobium-rich phase may form during the solidification process and in the solid phase. However, cuboid NbC precipitates can be precipitated from the liquid phase when the niobium mass fraction is higher (0.27%, 0.46%). These results indicate that with the increasing niobium content the amount, particle size, and initial precipitation temperature of niobium carbide precipitated in the matrix structure will increase. According to the observation and statistical analysis of the microstructure, it is found that tensile strength will be improved with an increase in niobium content due to the refinement of the graphite and pearlite interlamellar spacing. In this paper, adding less than 0.32% of Nb to grey cast iron is recommended, considering the comprehensive cost and the effect of niobium in the material structure.

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The effect of niobium addition on the microstructure and properties of cast iron used in cylinder head

Cited by 31 publications

References 22 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

Study of the Influence of Alloying Elements on the Mechanical Characteristics and Wear Behavior of a Ductile Cast Iron

The Precipitation of Niobium Carbide and Its Influence on the Structure of HT250 for Automobile Wheel Hubs

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