PEMODELAN PREDIKSI KEKASARAN PERMUKAAN PADA PADUAN ALUMINIUM 7075
In the manufacturing industry, surface roughness is one aspect in determining the quality of a product. To get a quality that is qualified requires an experiment that incurs costs and time in order to get the desired quality. To generate efficiency, the research continued with the development of a p...
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2024-01-09.
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| 100 | 1 | 0 | |a Muhammad Destri Mardhani, . |e author |
| 245 | 0 | 0 | |a PEMODELAN PREDIKSI KEKASARAN PERMUKAAN PADA PADUAN ALUMINIUM 7075 |
| 260 | |c 2024-01-09. | ||
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| 500 | |a http://repository.upnvj.ac.id/29001/27/ARTIKEL%20KI.pdf | ||
| 520 | |a In the manufacturing industry, surface roughness is one aspect in determining the quality of a product. To get a quality that is qualified requires an experiment that incurs costs and time in order to get the desired quality. To generate efficiency, the research continued with the development of a prediction model for surface roughness output performance using artificial neural networks. The observed parameters involve spindle speed, feed, and depth of cut. There are 27 data samples obtained from the machining process using a CNC Router with variations in the specified parameters, then modeling using an artificial neural network model. The artificial neural network achieved Normalize Root Mean Squared Error (NRMSE) with an error rate of 17.5% for all patterns. The results showed a good correlation between the actual values of the surface roughness experiments and the artificial neural network prediction model | ||
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| 690 | |a QA75 Electronic computers. Computer science | ||
| 690 | |a T Technology (General) | ||
| 690 | |a TJ Mechanical engineering and machinery | ||
| 690 | |a TS Manufactures | ||
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