Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with, an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.
Receipt date: 8/8/2020 acceptance date: 9/11/2020 Publication date: 31/12/2021
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American-Iranian relations have been characterized by tensions since the arrival of the guardian jurist regime in Iran to leadership in 1979, as it was considered a turning point not only in the cont
... Show MoreBackground: Alterations in the microhardness and roughness are commonly used to analyze the possible negative effects of bleaching products on restorative materials. This in vitro study evaluated the effect of in-office bleaching (SDI pola office +) on the surface roughness and micro-hardness of four newly developed composite materials (Z350XT –nano-filled, Z250XT-nano-hybrid, Z250-mico-hybrid and Silorane-silorane based). Materials and methods: Eighty circular samples with A3 shading were prepared by using Teflon mold 2mm thickness and 10mm in diameter. 20 samples for each material, 10 samples for base line measurement (surface roughness by using portable profillometer, and micro-hardness by usingDigital Micro Vickers Hardness Test
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... Show MoreObjective: In order to evaluate the effect of different typed of Separating Medium on the roughness of the fitted
tissue surface of acrylic denture base.
Methodology: Chosen three types of separating medium (Group A Tin foil), (Group B Detery Isolant),(Group C
Cold Mould Seal),used 30 samples of hot cure acrylic resin ,10 samples for each group, after complete curing of
these samples , Profilometer device was used to measure the surface roughness of each sample in all groups.
Results: Using One Way ANOVA Test and LSD test, the results were highly significant in differences among all
groups. Although (Group A) showing lest roughness, (Group B) showing a satisfactory result of roughness, While
(Group C) Showing the hig
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