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http://localhost:8080/xmlui/handle/123456789/91| Title: | Application of RSM and ANN for the Predication and optimization of the circularity Error of DSS 2205 Under Hybrid Cryo-MQL Process |
| Authors: | Sharma, Rajeev Jha, Binit Kumar Pahuja, Vipin |
| Keywords: | Response surface method (RSM) Artificial Neural Network (ANN) DSS 2205 Circularity Drilling Process |
| Issue Date: | Jun-2022 |
| Publisher: | Journal of Xi’an Shiyou University |
| Series/Report no.: | Natural Science Edition (June, 2022);VOLUME 18 ISSUE 6 ;Pages 773-781 |
| Abstract: | In this research paper, parametric optimization was carried out by using the response surface method (RSM) with the Box-Behan design of the matrix under a hybrid (the combination of MQL and the LCO2) process. The hybrid process is an environmentally friendly machining process. For optimization, select three input parameters e.g. drill diameter, spindle speed, and feed rate while circularity error is an output parameter. After the analysis of variance (ANOVA) analysis, it was observed that feed rate is the most effective process parameter compared to other process parameters on circularity error. In MATLAB software Artificial Neural Network (ANN) implements for validation of experimental results. Also, it was observed that the experimental results and predictive results are in close agreement with each other. |
| Description: | Application of RSM and ANN for the Predication and optimization of the circularity Error of DSS 2205 Under Hybrid Cryo-MQL Process |
| URI: | http://localhost:8080/xmlui/handle/123456789/91 |
| ISSN: | 1673-064X |
| Appears in Collections: | Faculty of Manufacturing Skills Education |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 0041 V18I06-94.pdf | Application of RSM and ANN for the Predication and optimization of the circularity Error of DSS 2205 Under Hybrid Cryo-MQL Process | 382.41 kB | Adobe PDF |  View/Open |
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