Search Results (3)

In recent years, the application of environmentally friendly cutting fluids in the metal cutting industry has been a growing concern in all over the world. In this study, the minimum quantity cooling lubrication (MQCL) technique, which uses very small amount of cutting oil, is motivated to apply to the hard milling process of Hardox 500 steel. Further, rice bran oil, a natural biodegradable oil, is used as the base fluid of Al₂O₃/MoS₂ hybrid nanofluid. ANOVA analysis is used to study the influences of nanoparticle concentration, cutting speed, and feed rate on surface roughness. The obtained results indicate that good surface quality is achieved and the cutting speed is significantly increased to 140 m/min (about 2.55–2.80 times higher than the recommended values) due to the better cooling and lubricating effects from MQCL system and Al₂O₃/MoS₂ hybrid nanofluid. Moreover, the microstructure of the machined surface proves the formation of MoS₂ tribo film by using Al₂O₃/MoS₂ hybrid nanofluid, indicating that the effectiveness of each type of nanoparticle in hybrid nanofluid has been promoted. Furthermore, the important technical guides for machining Hardox 500 steel are provided. Full article

Electromagnetic actuator systems composed of an induction servo motor (ISM) drive system and a rice milling machine system have widely been used in agricultural applications. In order to achieve a finer control performance, a witty control system using a revised recurrent Jacobi polynomial neural network (RRJPNN) control and two remunerated controls with an altered bat search algorithm (ABSA) method is proposed to control electromagnetic actuator systems. The witty control system with finer learning capability can fulfill the RRJPNN control, which involves an attunement law, two remunerated controls, which have two evaluation laws, and a dominator control. Based on the Lyapunov stability principle, the attunement law in the RRJPNN control and two evaluation laws in the two remunerated controls are derived. Moreover, the ABSA method can acquire the adjustable learning rates to quicken convergence of weights. Finally, the proposed control method exhibits a finer control performance that is confirmed by experimental results. Full article

Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the physical properties of grain and the associated knowledge. In this present work, a model of quality grade testing and identification is proposed using a novel digital image processing and knowledge-based adaptive neuro-fuzzy inference system (ANFIS). The rationale behind adopting a grading system based on fuzzy rules relies on capabilities of ANFIS to simulate the behaviour of an expert in the characterization of rice grain using the physical properties of rice grains. The rice kernels are characterized with the help of morphological descriptors and geometric features which are derived from sample images of milled basmati rice. The predictive capability of the proposed technique has been tested on a sufficient number of training and test images of basmati rice grain. The proposed method outperforms with a promising result in an evaluation of rice quality with >98.5% classification accuracy for broken and whole grain as compared to standard machine learning technique viz. support vector machine (SVM) and K-nearest neighbour (KNN). The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency. Full article