Processing map approach for surface defect prediction in the hot bar rolling

Hy, Kim; Kwon, Hyuck-Cheol; Hw, Lee; Im, Yong‐Taek; Byon, SM; Park, HD

doi:10.1016/j.jmatprotec.2007.11.099

Cited by 23 publications

(5 citation statements)

References 11 publications

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“…Notably, work hardening is primarily completed with a certain small strain that could not exceed 0.03 to 0.06. However, for the ordinary steel materials, the corresponding peak strain is believed to be relatively large and ceaselessly changes with thermodynamic parameters [15].…”

Section: Analysis Of Thermal Deformation Characteristicsmentioning

confidence: 99%

Application of mathematical modelling for hot rolled Ti-6Al-4V alloy

Shuai¹,

Zhang²,

Li³

et al. 2021

Eng. Res. Express

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It is an urgent need to develop a new kind of rolling process for high-quality titanium alloy bars. Establishing a series of mathematics models prior to experiments may be an effective method to save costs and optimize the rolling schedules. In this paper, based on the thermal deformation characteristics under different temperatures varied strain rates, the constitutive models of a b + twophase region and b single-phase region are established respectively and used to reveal hardening and softening mechanisms. The temperature models including the contact deformation zone, the interstand zone and the roll gap zone could well describe the heat exchange during the hot rolling process. The recrystallization models about critical stress, recrystallization fraction and dislocation density are also applied to simulate microstructure changes. And the shape models of a deformed bar in flat and round triangular passes are predicted respectively according to the metal flow rule and spread models. Compared with rolling tests, the simulating results involving the above mathematical models are in good agreements with the experimental ones. RECEIVED

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Section: Analysis Of Thermal Deformation Characteristicsmentioning

confidence: 99%

Application of mathematical modelling for hot rolled Ti-6Al-4V alloy

Shuai¹,

Zhang²,

Li³

et al. 2021

Eng. Res. Express

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“…m values vary in different temperature, strain, and strain rate in single forming process. 15 The variation of m values represents different transition mechanisms in hot deformation process. In previous research, 16 the change of m value may indicate the occurrence level of non-basal slip.…”

Section: Strain-rate Sensitivity In Ta2mentioning

confidence: 99%

Evaluation on the hot workability of as-extruded TA2 pure titanium using processing map

Wang

Luo

et al. 2016

Advances in Mechanical Engineering

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The processing map on the basis of dynamic material model has been recognized to be an effective method to evaluate the hot workability of metal alloys. To evaluate the mechanical behavior of TA2 pure titanium, a sets of hot compression tests were conducted on the TMTS machine in the temperature range of 800°C-1000°C and the strain rate range of 0.01-10 s 21 . Then, the processing map is constructed from the strain-stress data with the strain of 0.25, 0.4, 0.55, and 0.7. The fact that the strain will not impact the workability obviously for the little change of the stable regions of the processing maps in different strains was discussed. Finally, an overall consideration was given in the processing maps and the optimized workability of TA2 should be within the strain rate range of 0.5-1 s 21 and temperature range of 900°C-925°C.

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“…Existing studies to improve the surface quality of small steel bars assume that these quality factor have been identified and focus on diagnosis of defects [5], [6]. However, there are no attempts to validate the quality factor based on data.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Method of Statistical Difference Testing and K-Means Clustering for Identifying Quality Factors of Small Steel Bars

Lee,

Jeong

2023

Advances in Transdisciplinary Engineering

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As profit of steel enterprises is getting smaller and comparison is getting harder, in steel manufacturing industry quality inspection and control have become main measures for getting over the difficulties. In sequential manufacturing process, failure occurred in preceding process make final quality worse. Detecting the factor which affects to final quality is necessary to make process stable and efficient. Existing studies to improve the surface quality of small steel bars assume that these quality factor have been identified and focus on diagnosis of defects. However, there are no attempts to validate the quality factor based on data. In this paper, we attempt to verify the quality factor based on data using statistical and data-mining techniques. To get over practical problems from variation of quality due to operation date and merge measurement of quality, we suggest method using statistical significance difference and using k-means clustering (k = 2). Method using statistical significance difference considers the overall tendency about quality and method using k-means clustering consider the tendency toward outlier. Quality factors of small steel bar can be detected by using both methods serially. And we apply this method to real world data in case study.

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Processing map approach for surface defect prediction in the hot bar rolling

Cited by 23 publications

References 11 publications

Application of mathematical modelling for hot rolled Ti-6Al-4V alloy

Application of mathematical modelling for hot rolled Ti-6Al-4V alloy

Evaluation on the hot workability of as-extruded TA2 pure titanium using processing map

A Two-Step Method of Statistical Difference Testing and K-Means Clustering for Identifying Quality Factors of Small Steel Bars

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