Multi-Objective Optimization of Magneto Rheological Abrasive Flow Nano Finishing Process on AISI Stainless Steel 316L

2020 ◽  
Vol 63 ◽  
pp. 98-111
Author(s):  
S. Kathiresan ◽  
B. Mohan
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Initial Surface ◽  
Multi Objective Optimization ◽  
Stainless Steel 316L ◽  
Volume Percentage ◽  
Multi Objective ◽  
Input Variables ◽  
Number Of Cycles ◽  
Magneto Rheological

In this experimental work, Magneto rheological abrasive flow nano finishing processes were conducted on AISI Stainless steel 316L work pieces that are widely used in medical implants. The focus of the present study is to assess the effect of input variables namely the volume percentage of iron (Fe) particles, silicon carbide (SiC) abrasive particles in the Magneto rheological abrasive fluid and number of cycles on the final surface roughness at nano level as well as the material removal rate. The volume % of Fe particles were taken as 20, 25 and 30 and the volume % of SiC particles were taken as 10, 15 and 20. The different number of cycles considered for the study is 100,200 and 300. There are 20 different set of experiments with different combinations of input variables mentioned have been carried out based on the experimental design derived through central composite design technique. The minimum surface roughness observed is 23.34 nanometer (nm) from the initial surface roughness of 1.92 micro meter (µm). Towards optimizing the input process variables, a multi objective optimization was carried out by using response surface methodology.

scholarly journals Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

2020 ◽  
Vol 4 (3) ◽  
pp. 64 ◽  
Author(s):  
Mahamudul Hasan Tanvir ◽  
Afzal Hussain ◽  
M. M. Towfiqur Rahman ◽  
Sakib Ishraq ◽  
Khandoker Zishan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Optimization Algorithm ◽  
Cutting Parameters ◽  
Whale Optimization Algorithm ◽  
Multi Objective Optimization ◽  
Turning Operation ◽  
Multi Objective ◽  
Whale Optimization ◽  
Optimum Cutting

In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.

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.

In Vitro Biocompatibility Study on Stainless Steel 316L After Nano Finishing

2017 ◽  
Author(s):  
Kathiresan Sundararaj ◽  
Mohan Bangaru
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Metal Removal ◽  
In Vitro Study ◽  
Fibroblast Cell ◽  
Machining Process ◽  
Stainless Steel 316L ◽  
In Vitro Biocompatibility ◽  
Magneto Rheological

In this present study, the nano finishing of stainless steel 316L (SS316L) was obtained by means of magneto rheological abrasive flow finishing (MRAFF) process by varying the amount of current to the electromagnet. The MRAFF process is an advanced machining process in which the metal removal process is effectively controlled by means of the rheological property of the magneto rheological abrasive (MRA) fluid. After the finishing process, the surface roughness profiles and parameter were obtained with the help of Talysurf coherence correlation interferometer (CCI). Stainless steel 316L sample surfaces obtained by means of MRAFF process with different nano roughness values are utilized to study its biocompatibility by an in vitro study to examine the cell viability, proliferation of a fibroblast cell line (NIH-3T3) by means of MTT assay. The optical density (OD) values were utilized to determine the amount of viable cells. The cell proliferations studies were conducted and observed for 1, 3 and 7 days of incubation period with respect to the absorbance value of the samples. The protein adsorption studies are also made by using bicinchoninic acid assay (BCA) kit. The characters of biocompatibility are correlated with the nano scale surface roughness parameters of the SS316L samples.

scholarly journals Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4693
Author(s):  
Feilong Du ◽  
Lin He ◽  
Haisong Huang ◽  
Tao Zhou ◽  
Jinxing Wu
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Multi Objective Optimization ◽  
Multi Objective

Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

scholarly journals Post-Processing and Surface Characterization of Additively Manufactured Stainless Steel 316L Lattice: Implications for BioMedical Use

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1376
Author(s):  
Alex Quok An Teo ◽  
Lina Yan ◽  
Akshay Chaudhari ◽  
Gavin Kane O’Neill
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Characterization ◽  
High Surface ◽  
Surface Characteristics ◽  
Post Processing ◽  
Stainless Steel 316L ◽  
Processing Methods ◽  
Particulate Debris

Additive manufacturing of stainless steel is becoming increasingly accessible, allowing for the customisation of structure and surface characteristics; there is little guidance for the post-processing of these metals. We carried out this study to ascertain the effects of various combinations of post-processing methods on the surface of an additively manufactured stainless steel 316L lattice. We also characterized the nature of residual surface particles found after these processes via energy-dispersive X-ray spectroscopy. Finally, we measured the surface roughness of the post-processing lattices via digital microscopy. The native lattices had a predictably high surface roughness from partially molten particles. Sandblasting effectively removed this but damaged the surface, introducing a peel-off layer, as well as leaving surface residue from the glass beads used. The addition of either abrasive polishing or electropolishing removed the peel-off layer but introduced other surface deficiencies making it more susceptible to corrosion. Finally, when electropolishing was performed after the above processes, there was a significant reduction in residual surface particles. The constitution of the particulate debris as well as the lattice surface roughness following each post-processing method varied, with potential implications for clinical use. The work provides a good base for future development of post-processing methods for additively manufactured stainless steel.

Nanofinishing of Microslots on Surgical Stainless Steel by Abrasive Flow Finishing Process: Experimentation and Modeling

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Sachin Singh ◽  
Deepu Kumar ◽  
Mamilla Ravi Sankar ◽  
Kamlakar Rajurkar
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Critical Role ◽  
Viscoelastic Model ◽  
Three Dimensional ◽  
Drug Eluting Stent ◽  
Initial Surface ◽  
Technological Advancement ◽  
Drug Eluting ◽  
Abrasive Flow Finishing

Miniaturization of components is one of the major demands of the today's technological advancement. Microslots are one of the widely used microfeature found in various industries such as automobile, aerospace, fuel cells and medical. Surface roughness of the microslots plays critical role in high precision applications such as medical field (e.g., drug eluting stent and microfilters). In this paper, abrasive flow finishing (AFF) process is used for finishing of the microslots (width 450 μm) on surgical stainless steel workpiece that are fabricated by electrical discharge micromachining (EDμM). AFF medium is developed in-house and used for performing microslots finishing experiments. Developed medium not only helps in the removal of hard recast layer from the workpiece surfaces but also provides nano surface roughness. Parametric study of microslots finishing by AFF process is carried out with the help of central composite rotatable design (CCRD) method. The initial surface roughness on the microslots wall is in the range of 3.50 ± 0.10 μm. After AFF, the surface roughness is reduced to 192 nm with a 94.56% improvement in the surface roughness. To understand physics of the AFF process, three-dimensional (3D) finite element (FE) viscoelastic model of the AFF process is developed. Later, a surface roughness simulation model is also proposed to predict the final surface roughness after the AFF process. Simulated results are in good agreement with the experimental results.

Sign in / Sign up

Export Citation Format

Share Document