Analysis of Peritectic Transformation Contraction of 304 Stainless Steel Using Surface Roughness

2020 ◽  
Vol 1005 ◽  
pp. 10-17
Author(s):  
Jun Li Guo ◽  
Guang Hua Wen ◽  
Ping Tang ◽  
Jiao Jiao Fu
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cooling Rate ◽  
304 Stainless Steel ◽  
Peritectic Transformation ◽  
Laser Confocal Microscopy ◽  
Solidification Model ◽  
Formation Of Cracks ◽  
Grain Surface ◽  
Initial Solidification

Peritectic transformation contraction of ferrite to austenite plays an important role in the formation of cracks for steels. In order to evaluate the peritectic transformation contraction of steels at the initial solidification, the solidification of 304 stainless steel under different cooling rates were carried out by using high temperature laser confocal microscopy, and then the surface roughness and peritectic transformation contraction were analysed in combination with the microstructure of solidified steel. The result shows that the solidification model of 304 stainless steel was ferrite-austenite model in the experiments, and peritectic transformation occurred during solidification. The residual ferrite in the as-cast structure were vermicular, skeletal and reticular in turn with the increase of cooling rate. The volume contraction caused by peritectic transformation resulted in wrinkles (surface roughness) appearing on the grain surface. The peritectic transformation contraction that was affected by surface roughness increased first and then decreased with cooling rate increasing, indicating the peritectic transformation contraction can be evaluated by the surface roughness.

scholarly journals Study of the Effect of Carbon on the Contraction of Hypo-Peritectic Steels during Initial Solidification by Surface Roughness

Metals ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 982 ◽  
Author(s):  
Dazhi Pu ◽  
Guanghua Wen ◽  
Dachao Fu ◽  
Ping Tang ◽  
Junli Guo
Keyword(s):  
Surface Roughness ◽  
Cooling Rate ◽  
Solidification Process ◽  
Casting Process ◽  
Continuous Casting Process ◽  
Peritectic Steel ◽  
Laser Confocal Microscope ◽  
Initial Solidification

In the continuous casting process, the shrinkage of the peritectic phase transition during the initial solidification process has an important influence on the surface quality of peritectic steel. The initial solidification process of 0.10C%, 0.14C%, and 0.16C% peritectic steels was observed in situ by a high temperature laser confocal microscope, and the contraction degree during initial solidification was characterized by surface roughness. The results showed that under the cooling rate of 20 °C/s, the surface roughness value Ra(δ/γ) of 0.10C% peritectic steel was 32 μm, the Ra(δ/γ) value of 0.14C% peritectic steel was 25 μm, and the Ra(δ/γ) value of 0.16C% peritectic steel was 17 μm. With increasing carbon content, the contraction degree of the δ→γ transformation decreased, and the value of the surface roughness Ra(δ/γ) declined. Therefore, surface roughness can characterize the contraction degree of the δ→γ transformation in the initial solidification process of peritectic steel under the condition of a large cooling rate.

Characterization of Cofficient of Friction in Dry Contact for Control of Vibrations in Machine Systems

2000 ◽  
Author(s):  
Jamil Abdo ◽  
Kambiz Farhang ◽  
Glenn Meinhardt
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Normal Load ◽  
304 Stainless Steel ◽  
Stick Slip ◽  
Dry Contact ◽  
Machine Systems ◽  
Alloy 6061

Abstract A 2k factorial experiment is performed to ascertain the effect of four factors and their cross influence on friction between dry surfaces. The factors in this study include materials Young’s modulus, applied normal load, surface roughness and relative surface speed. For each combination of factors four replicates in addition to two center points are used to obtain an average coefficient of friction for dry contact. In the experiment 304 Stainless Steel and Alloy 6061 Aluminum are employed to provide the high and low levels of Young’s modulus. Results suggest that Young’s modulus has the most significant influence followed by velocity/modulus cross-coupling, surface roughness, load, and modulus/roughness. Analyses are carried out separately for the 304 Stainless Steel and alloy 6061 Aluminum to remove the effect of Young’s modulus. The results are used to obtain iso-friction curves that serve to establish force-speed control for prevention of stick-slip vibration.

Investigations on the Microstructure and the Fracture Surface of Pulsed Nd: YAG Laser Welding of AISI 304 Stainless Steel

2012 ◽  
Vol 445 ◽  
pp. 418-423
Author(s):  
Seyed Ali Asghar Akbari Mousavi ◽  
A. Garehdaghi
Keyword(s):  
Stainless Steel ◽  
Laser Welding ◽  
Cooling Rate ◽  
304 Stainless Steel ◽  
Chemical Compositions ◽  
Operational Parameters ◽  
Δ Ferrite ◽  
Higher Power ◽  
304 Austenitic Stainless Steel ◽  
Pulsed Nd:Yag Laser Welding

The paper presents pulsed Nd:YAG laser welding of the 304 stainless steels. The welding tests were carried out with various operational parameters. The effects of laser welding variables on the geometry, microstructure and solidification of the weld are considered. The austenitic or ferritic solidification is produced in the 304 austenitic stainless steel depended upon the cooling rate and its chemical compositions. The possiblity of austenitic solidification compared with the ferritic solidification decreases with the chromium to nickel equivalent ratio and that increases with cooling rates. Moreover, more δ ferrite is obtained if the cooling rate is increased or the higher power laser is used. The surface of fracture samples was considered and the reason for failure was investigated. The study shows that the fracture is in ductile type.

Feasibility of Supercritical Carbon Dioxide Based Metalworking Fluids in Micromilling

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
S. D. Supekar ◽  
B. A. Gozen ◽  
B. Bediz ◽  
O. B. Ozdoganlar ◽  
S. J. Skerlos
Keyword(s):  
Carbon Dioxide ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Supercritical Carbon Dioxide ◽  
Tool Wear ◽  
304 Stainless Steel ◽  
Metalworking Fluids ◽  
Burr Formation ◽  
Specific Cutting Energy ◽  
Cutting Energy

This article investigates the feasibility of using supercritical carbon dioxide based metalworking fluids (scCO2 metalworking fluids (MWFs)) to improve micromachinability of metals. Specifically, sets of channels were fabricated using micromilling on 304 stainless steel and 101 copper under varying machining conditions with and without scCO2 MWF. Burr formation, average specific cutting energy, surface roughness, and tool wear were analyzed and compared. Compared to dry machining, use of scCO2 MWF reduced burr formation in both materials, reduced surface roughness by up to 69% in 304 stainless steel and up to 33% in 101 copper, tool wear by up to 20% in 101 copper, and specific cutting energy by up to 87% in 304 stainless steel and up to 40% in 101 copper. The results demonstrate an improvement in micromachinability of the materials under consideration and motivate future investigations of scCO2 MWF-assisted micromachining to reveal underlying mechanisms of functionality, as well as to directly compare the performance of scCO2 MWF with alternative MWFs appropriate for micromachining.

Experimental investigations of nanosecond-pulsed Nd:YAG laser beam micromachining on 304 stainless steel

2018 ◽  
Vol 1 (1) ◽  
pp. 62-75 ◽  
Author(s):  
Rasmi Ranjan Behera ◽  
Mamilla Ravi Sankar ◽  
Prahlad Kumar Baruah ◽  
Ashwini Kumar Sharma ◽  
Alika Khare
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Laser Beam ◽  
Nd:Yag Laser ◽  
Research Work ◽  
Scanning Speed ◽  
304 Stainless Steel ◽  
Experimental Investigations ◽  
Micro Channel ◽  
Micro Channels

The demand for miniaturized components is increasing day by day as their application varies from industry to industry such as biomedical, micro-electro-mechanical system and aerospace. In the present research work, high-quality micro-channels are fabricated on 304 stainless steel by laser beam micromachining process with nanosecond Nd:YAG laser. The laser pulse energy (LPE), scanning speed (SS) and scanning pass number (SP No.) are used as the process parameters, whereas the depth and width of the kerf as well as the surface roughness are used to characterize the micro-channels. It is found that the kerf depth, width and surface roughness decrease with increase in the SS. The kerf depth sharply increases with increase in the SP No. The kerf width is minimum at 30 mJ LPE, 400 µm s‒1 SS and 10 SP No. The minimum surface roughness is observed at 30 mJ LPE, 500 µm s‒1 SS and 10 SP No. The oxygen content is found to gradually decrease with the distance from the centre of the micro-channel. Based on the experimental results, optimized input parameters can be offered to control the micro-channel dimensions and improve their surface finish effectively on stainless steel.

scholarly journals Effect of Additional Boron Amount on the Surface Roughness after Lathe Turning of h-BN Dispersed Type 304 Stainless Steel

2014 ◽  
Vol 100 (12) ◽  
pp. 1542-1547
Author(s):  
Masahiro Kawajiri ◽  
Satoshi Emura ◽  
Xiaohua Min ◽  
Shigeo Yamamoto ◽  
Kazuyuki Sakuraya ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
304 Stainless Steel ◽  
Type 304 ◽  
Type 304 Stainless Steel

Influence of surface roughness of steel plate on self-assembly behavior of silica particles

2015 ◽  
Vol 29 (06n07) ◽  
pp. 1540042
Author(s):  
Takashi Yonezawa ◽  
Daisuke Yonekura
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Self Assembly ◽  
Steel Plate ◽  
Silica Particles ◽  
304 Stainless Steel ◽  
Silica Layer ◽  
Spherical Silica ◽  
Silica Layers ◽  
Assembly Behavior

This paper describes the influence of surface roughness of steel plate on self-assembly behavior of silica particles based on SEM observations and the wettability of the suspension. The 304 stainless steel plate having two different surface roughness and spherical silica powder were used for the investigation. The silica layer was obtained by dipping the steel plate into the suspension and drawing it under various drawing speed. As a result, silica particle layers were formed on the plate surface when the stainless steel had a rough surface. In contrast, it was difficult to obtain the silica layers for the smooth surface.

scholarly journals Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

2019 ◽  
Vol 9 (18) ◽  
pp. 3684 ◽  
Author(s):  
Tao Zhou ◽  
Lin He ◽  
Jinxing Wu ◽  
Feilong Du ◽  
Zhongfei Zou
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Quality ◽  
Regression Tree ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Gradient Boosting ◽  
Multi Objective Optimization ◽  
Multi Objective

Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

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