Advances in Abrasive Flow Finishing

2020 ◽  
pp. 147-181
Author(s):  
Vivek Rana ◽  
Anand C. Petare ◽  
Neelesh Kumar Jain
Keyword(s):  
Abrasive Flow Finishing

Optimal parameters of abrasive flow finishing for hip joint implants

2021 ◽  
pp. 095440542199561
Author(s):  
Yahya Choopani ◽  
Mohsen Khajehzadeh ◽  
Mohammad Reza Razfar
Keyword(s):  
Surface Roughness ◽  
Standard Deviation ◽  
Femoral Head ◽  
Hip Joint ◽  
Spherical Shape ◽  
Shape Deviation ◽  
Head Surface ◽  
Joint Implants ◽  
Finishing Processes ◽  
Abrasive Flow Finishing

Total hip arthroplasty (THA) is one of the most well-known orthopedic surgeries in the world which involves the substitution of the natural hip joint by prostheses. In this process, the surface roughness of the femoral head plays a pivotal role in the performance of hip joint implants. In this regard, the nano-finishing of the femoral head of the hip joint implants to achieve a uniform surface roughness with the lowest standard deviation is a major challenge in the conventional and advanced finishing processes. In the present study, the inverse replica fixture technique was used for automatic finishing in the abrasive flow finishing (AFF) process. For this aim, an experimental setup of the AFF process was designed and fabricated. After the tests, experimental data were modeled and optimized to achieve the minimum surface roughness in the ASTM F138 (SS 316L) femoral head of the hip joint through the use of response surface methodology (RSM). The results confirmed uniform surface roughness up to the range of 0.0203 µm with a minimum standard deviation of 0.00224 for the femoral head. Moreover, the spherical shape deviation of the femoral head was achieved in the range of 7 µm. The RSM results showed a 99.71% improvement in the femoral head surface roughness (0.0007) µm under the optimized condition involving the extrusion pressure of 9.10 MPa, the number of finishing cycles of 95, and SiC abrasive mesh number of 1000.

Rotary ultrasonic-assisted abrasive flow finishing and its fundamental performance in Al6061 machining

2021 ◽  
Vol 113 (1-2) ◽  
pp. 473-481
Author(s):  
Qiuyan Wang ◽  
Moiz Sabbir Vohra ◽  
Shuowei Bai ◽  
Swee Hock Yeo
Keyword(s):  
Ultrasonic Assisted ◽  
Abrasive Flow Finishing

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

Abrasive flow finishing of stereolithography prototypes

1998 ◽  
Vol 4 (2) ◽  
pp. 56-67 ◽  
Author(s):  
Robert E. Williams ◽  
Vicki L. Melton
Keyword(s):  
Abrasive Flow Finishing

scholarly journals Quality influence and process parameter optimization of T-pipe in abrasive flow finishing

2017 ◽  
Vol 9 (8) ◽  
pp. 168781401771898 ◽  
Author(s):  
Junye Li ◽  
Zengwei Zhou ◽  
Lili Wei ◽  
Xinming Zhang ◽  
Ying Xu
Keyword(s):  
Parameter Optimization ◽  
Process Parameter ◽  
Process Parameter Optimization ◽  
Abrasive Flow Finishing
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