Soft computing for sustainable drilling of AISI 316L stainless steel under formulated neem oil minimum quantity lubrication condition

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY
C.P. Natesh, Y.M. Shashidhara, H.J. Amarendra, Raviraj Shetty, Rajesh Nayak, S. V. UdayKumar Shetty, Madhukara Nayak, Adithya Hegde
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Abstract

This paper discusses about process input optimization to obtain desired output characteristics such as Surface Roughness (microns), Thrust Force (N) and Torque (N-m) during drilling of AISI 316 L Stainless Steel under minimum quantity formulated neem oil lubrication condition based on Taguchi Design of Experiments (TDOE), Response Surface Methodology (RSM) and Desirability Functional Analysis (DFA) by varying flow rate (ml.min−1), stand-off distance (mm), flow pressure (Bar) and nozzle exit diameter (mm). Formulated Neem Oil possesses natural lubricating properties that reduce friction and heat generation, thus prolonging the tool life and improving surface finish. Additionally, it is biodegradable and environmentally friendly, making it a sustainable choice for machining operations. From the experimental investigation using TDOE, it was observed that there was considerable improvement in thrust force, surface roughness and torque with modified neem oil as a lubricant. Further, plot for main effects and Analysis of Variance (ANOVA) are successfully used to identify the optimum process input parameters and their percentage of contribution (P%) on output parameters. RSM is successfully used to generate a second order mathematical model which can be effectively used to analyze the process parameters. Further, from Desirability Functional Analysis (DFA), minimum surface roughness (0.34 microns), thrust force (1292.37 N) and torque (14.71 N-m) value were predicted. Finally, Back Propagation Artificial Neural Network (BPANN) analysis has been adopted to predict the surface roughness, thrust force and torque with a minimal error of 1.46%, 0.017% & 0.17%, respectively. The adoption of Neem oil formulations has been successful in improving machining characteristics. Its versatility across an array of machining processes and materials, in tandem with the global momentum toward greener manufacturing paradigms, positions it as a promising lubricant for various machining practices.
配方印楝油最少量润滑条件下aisi316l不锈钢可持续钻井的软计算
本文基于田口实验设计(TDOE)、响应面法(RSM)和期望功能分析(DFA),通过改变流量(ml.min−1)、间隔距离(mm)、流量(ml.min−1),对aisi316l不锈钢在最少量配方印草油润滑条件下的钻孔过程进行工艺输入优化,以获得所需的输出特性,如表面粗糙度(微米)、推力(N)和扭矩(N-m)。流量压力(Bar)和喷嘴出口直径(mm)。配方印楝油具有天然的润滑性能,减少摩擦和热量的产生,从而延长工具寿命和改善表面光洁度。此外,它是可生物降解和环保的,使其成为加工操作的可持续选择。从TDOE的实验研究中可以看出,改性印楝油作为润滑剂对推力、表面粗糙度和扭矩都有很大的改善。此外,主效应图和方差分析(ANOVA)成功地用于确定最佳过程输入参数及其对输出参数的贡献百分比(P%)。利用RSM成功地建立了二阶数学模型,该模型可有效地用于工艺参数分析。此外,通过理想泛函分析(Desirability Functional Analysis, DFA),预测了最小表面粗糙度(0.34微米)、推力(1292.37 N)和扭矩(14.71 N-m)值。最后,采用反向传播人工神经网络(BPANN)对表面粗糙度、推力和扭矩进行预测,误差最小,分别为1.46%、0.017%和0.17%。采用印度楝油配方已成功地改善了加工特性。它在一系列加工工艺和材料中的多功能性,与全球向绿色制造范式的发展势头相结合,使其成为各种加工实践的有前途的润滑剂。
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来源期刊
Cogent Engineering
Cogent Engineering ENGINEERING, MULTIDISCIPLINARY-
CiteScore
4.00
自引率
5.30%
发文量
213
审稿时长
13 weeks
期刊介绍: One of the largest, multidisciplinary open access engineering journals of peer-reviewed research, Cogent Engineering, part of the Taylor & Francis Group, covers all areas of engineering and technology, from chemical engineering to computer science, and mechanical to materials engineering. Cogent Engineering encourages interdisciplinary research and also accepts negative results, software article, replication studies and reviews.
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