scholarly journals Fine Surface Finish of a Hardened Stainless Steel Using a New Burnishing Tool

2017 ◽  
Vol 10 ◽  
pp. 208-217 ◽  
Author(s):  
Fang-Jung Shiou ◽  
Shih-Ju Huang ◽  
Albert J. Shih ◽  
Jiang Zhu ◽  
Masahiko Yoshino
Keyword(s):  
Stainless Steel ◽  
Surface Finish ◽  
Fine Surface

Effects of Flap Wheel Grinding Parameters on Surface Roughness for Stainless Steel

2014 ◽  
Vol 548-549 ◽  
pp. 506-509 ◽  
Author(s):  
Jirayu Somgumnerd ◽  
Viboon Tangwarodomnukun ◽  
Suksan Prombanpong
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Operating Conditions ◽  
Success Factor ◽  
Polishing Process ◽  
Fine Surface ◽  
Key Success Factor ◽  
Selection Of

The polishing process plays an important role in the stainless steel cookware since an appearance is one of the prime quality criterions of the product. Typically, there are three sequential steps in the polishing process using abrasive flap wheel, sisal and cloth respectively. The abrasive flap wheel is the first step in the process which aims to rapidly create fine surface finish on the product. Thus, the selection of appropriate flap wheel as well as operating conditions in order to achieve surface finish within the required cycle time i.e. 12 seconds are the key success factor. Therefore, the experimental design is conducted and analyzed. It is found that there are four factors which influence the surface roughness: grits size of flap wheel, polishing time, velocity, and force. It can also be concluded from the analysis that the roughness is directly proportional to grit size and force but it is inversely proportional to velocity. In addition, the optimal condition for the case study can also be obtained.

Microthreading in Whirling

2015 ◽  
Vol 3 (4) ◽  
Author(s):  
Masaki Serizawa ◽  
Motohiro Suzuki ◽  
Takashi Matsumura
Keyword(s):  
Stainless Steel ◽  
Dynamic Response ◽  
Machine Tool ◽  
Surface Finish ◽  
Clamping Force ◽  
Thin Wires ◽  
Steel Wires ◽  
Fine Surface ◽  
Clamping System ◽  
The Wire

Whirling is applied to machining of microscrews on thin wires. A micro whirling machine has been developed for this. In order to suppress the vibration of the workpiece, the wire is inserted in polyurethane tubes clamped on a metal bar. Frequency analyses have been conducted by loading impulse forces at the center of the wire. The dynamic response is improved with reducing the vibration in the clamping force by the developed clamping system. Thirty micrometers microgrooves have been machined on 0.3 mm diameter stainless steel wires with fine surface finish, with the developed machine tool.

SANDVIK BIOLINE HIGH-N

Alloy Digest ◽  
2000 ◽  
Vol 49 (6) ◽  
Keyword(s):  
Stainless Steel ◽  
Corrosion Resistance ◽  
Physical Properties ◽  
Tensile Properties ◽  
Surface Finish ◽  
High Purity

Abstract Sandvik Bioline High N is a molybdenum-alloyed stainless steel for implant applications. Corrosion resistance to physiological environments is good. The alloy is of high purity and has an excellent surface finish. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on corrosion resistance. Filing Code: SS-793. Producer or source: Sandvik.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

Effects of Surface Finish and Loading Conditions on the Low Cycle Fatigue Behavior of Austenitic Stainless Steel in PWR Environment for Various Strain Amplitude Levels

2009 ◽  
Author(s):  
Jean Alain Le Duff ◽  
Andre´ Lefranc¸ois ◽  
Jean Philippe Vernot
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Strain Amplitude ◽  
Surface Finish ◽  
Low Cycle Fatigue ◽  
Fatigue Behavior ◽  
Complex Loading ◽  
Loading Conditions ◽  
Test Results ◽  
Cycle Fatigue

In February/March 2007, The NRC issued Regulatory Guide “RG1.207” and Argonne National Laboratory issued NUREG/CR-6909 that is now applicable in the US for evaluations of PWR environmental effects in fatigue analyses of new reactor components. In order to assess the conservativeness of the application of this NUREG report, Low Cycle Fatigue (LCF) tests were performed by AREVA NP on austenitic stainless steel specimens in a PWR environment. The selected material exhibits in air environment a fatigue behavior consistent with the ANL reference “air” mean curve, as published in NUREG/CR-6909. LCF tests in a PWR environment were performed at various strain amplitude levels (± 0.6% or ± 0.3%) for two loading conditions corresponding to a simple or to a complex strain rate history. The simple loading condition is a fully reverse triangle signal (for comparison purposes with tests performed by other laboratories with the same loading conditions) and the complex signal simulates the strain variation for an actual typical PWR thermal transient. In addition, two various surface finish conditions were tested: polished and ground. This paper presents the comparisons of penalty factors, as observed experimentally, with penalty factors evaluated using ANL formulations (considering the strain integral method for complex loading), and on the other, the comparison of the actual fatigue life of the specimen with the fatigue life predicted through the NUREG report application. For the two strain amplitudes of ± 0.6% and ± 0.3%, LCF tests results obtained on austenitic stainless steel specimens in PWR environment with triangle waveforms at constant low strain rates give “Fen” penalty factors close to those estimated using the ANL formulation (NUREG/6909). However, for the lower strain amplitude level and a triangle loading signal, the ANL formulation is pessimistic compared to the AREVA NP test results obtained for polished specimens. Finally, it was observed that constant amplitude LCF test results obtained on ground specimens under complex loading simulating an actual sequence of a cold and hot thermal shock exhibits lower combined environmental and surface finish effects when compared to the penalty factors estimated on the basis of the ANL formulations. It appears that the application of the NUREG/CR-6909 in conjunction with the Fen model proposed by ANL for austenitic stainless steel provides excessive margins, whereas the current ASME approach seems sufficient to cover significant environmental effects for representative loadings and surface finish conditions of reactor components.

Effect of Surface Finish and Loading Conditions on the Low Cycle Fatigue Life of Stainless Steel Welded Piping in PWR Environment

2013 ◽  
Author(s):  
Yuichi Fukuta ◽  
Yuichiro Nomura ◽  
Seiji Asada
Keyword(s):  
Stainless Steel ◽  
Fatigue Life ◽  
Austenitic Stainless Steel ◽  
Surface Finish ◽  
Loading Condition ◽  
Low Cycle Fatigue ◽  
Complex Loading ◽  
Fatigue Tests ◽  
Cycle Fatigue ◽  
Effect Of Surface

NUREG/CR-6909 of USA and JSME of Japan proposed new rules for evaluating environmental effects in fatigue analyses of reactors components. These rules were established from a lot of fatigue data with polished specimens under simple loading condition. The effects of surface finish or complex loading condition were reported in some papers, but these data were obtained with the simple shaped specimens. In order to evaluate the effects of surface finish and loading condition and to confirm the applicability of the proposed rules to actual components, Low Cycle Fatigue tests are performed in PWR environment with the specimens cut from 316 austenitic stainless steel welded piping. The pipes are machined to have three levels of surface finish condition and the load pattern simulating the thermal stress is applied to specimens. In this study, the effect of surface finish on fatigue life is included to be small for 316 austenitic stainless steel welded piping. Considering the insensitive region in the current evaluation rule, predicted accuracy is increased and possibility of improving the current rule is indicated.

Experimental Investigation for the Multi-objective Optimization of Machining Parameters on AISI D2 Steel Using Particle Swarm Optimization Coupled with Artificial Neural Network

2020 ◽  
Vol 19 (03) ◽  
pp. 589-606 ◽  
Author(s):  
Vipin Gopan ◽  
K. Leo Dev Wins ◽  
Gecil Evangeline ◽  
Arun Surendran
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Surface Finish ◽  
Fitness Function ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Fine Surface ◽  
Aisi D2

High Carbon High Chromium (or AISI D2) Steels, owing to the fine surface finish they produce upon grinding, find lot of applications in die casting. Machining parameters affect the surface finish significantly during the grinding operation. In this context, this work puts an effort to arrive at the optimum machining parameters relating to fine surface finish with minimum cutting force. The material removal caused by the abrasive grinding wheel makes the process a very complex and nonlinear machining operation. In many situations, traditional optimization techniques fail to provide realistic optimum conditions because of the associated complexity. In order to overcome this issue, particle swarm optimization (PSO) coupled with artificial neural network (ANN) is applied in this research work for parameter optimization with the objective of achieving minimum surface roughness and cutting force. The machining parameters selected for the investigation were table speed, cross feed and depth of cut and the responses were surface roughness and cutting force. ANNs, inspired from biological neural networks, are well capable of providing patterns, which are too complex in behavior. The ANN model developed was used as the fitness function for PSO to complete the optimization. Optimization was also carried out using conventional response surface methodology-genetic algorithm (RSM-GA) approach in which regression equation developed with RSM was considered as the fitness function for GA. Confirmatory experiments were conducted and the comparison showed that PSO coupled with ANN is a reliable tool for complex optimization problems.

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