Evaluation of crude watermelon oil as lubricant in cylindrical turning of AISI 1525 steel employing Taguchi and grey relational analyses techniques.

Cutting fluids are used for cooling and lubricating the machining area of components used in manufacturing industries such as aerospace, automotive, petroleum, and heavy machinery. Mineral oils derived from petroleum are commonly utilized as cutting fluids. Mineral oil is hazardous to the health of workers and damaging to the environment. There is a need for a substitute for mineral oil. Vegetable oil is increasingly being used as a cutting fluid. Vegetable oils are easily accessible and have benefits including excellent biodegradability, resistance to fire, low humidity rates, and a low coefficient of expansion under heat. This study adopts watermelon oil as a lubricant in MQL machining of AISI 1525 steel using tungsten tools. In the experiment, the feed rate, depth of cut (DC) and spindle speed were varied using the Taguchi L9 orthogonal array. Grey relational analysis was conducted to obtain optimum cutting parameters for surface roughness, machine vibration, and cutting temperature. Hardness and microstructural analysis of the workpiece were also conducted. Results showed that vegetable oil performed much more effectively than mineral oil in most experiments. The DC was shown to be the most efficient cutting parameter after applying ANOVA analysis based on the parameters that were evaluated. Additionally, models for cutting temperature, machine vibration, and surface roughness values have been developed with accuracy between 69.73 % and 99.05 %. The hardness of the workpiece increases with an increase in diameter, which was attributed to the increase in the steel rod (workpiece) cross-sectional area and the likelihood of a more uniform stress distribution. Moreover, finer grain sizes were observed at 70 mm diameter, with the predominant presence of pearlites. These characteristics were reportedly beneficial to the material's toughness and strength.

Dataset and ANN model prediction of performance of graphene nanolubricant with R600a in domestic refrigerator system.

This work evaluated the steady state performance of R600a in the base lubricant and graphene nanolubricant. The measuring instruments required and their uncertainties were provided, step by step method and procedures for preparation of graphene nanolubricant concentration and substituting it with the base lubricant in domestic refrigerator system are described. The system temperatures data was captured at the inlet and outlet of the system components. Also, the pressures data was recorded at the compressor inlet and outlet. The data was recorded for 3 h at 30 min interval at an ambient temperature of 27 °C. The experimental dataset, Artificial Neural Network (ANN) training and testing dataset are provided. The artificial intelligence approach of ANN model to predict the performance of graphene nanolubricant in domestic refrigerator is explained. Also, the ANN model prediction statistical performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2) are also provided. The data is useful to researchers in the field of refrigeration and energy efficiency materials, for replacing nanolubricant with the base lubricant in refrigerator systems. The data can be reuse for simulation and modelling vapour compression energy system.

Dataset of experimental and adaptive neuro-fuzzy inference system (ANFIS) model prediction of R600a/MWCNT nanolubricant in a vapour compression system.

This research paper assessed the performance of R600a with the base lubricant and Multi-walled Carbon Nanotube (MWCNT) nanolubricant at steady state. It describes the instruments required for measurement of the data parameter and its uncertainties, steps involved in preparing and replacing the MWCNT nanolubricant concentration with base lubricant in vapour compression refrigeration. The system's temperature data was collected at the components inlets and outlets. Pressure data was also registered at the compressor outlet and inlet. The data was captured at 27 °C ambient temperature at an interval of 30 min for 300 min. The experiment includes the experimental data collection, Adaptive Neuro-Fuzzy Inference System (ANFIS) training and testing dataset. The use of ANFIS model is explained in predicting the efficiency of MWCNT nanolubricant in a vapour compression refrigerator system. The ANFIS model also provides statistical output measures such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and determination coefficient (R 2). The data is useful and important for replacing MWCNT nanolubricant with base lubricant in a vapour compression refrigeration system for researchers in the specialisation of energy-efficient materials in refrigeration. The data present can be reused for vapour compression refrigeration systems simulation and modelling.

Data related to optimized process parameters influence on hardness, microstructural evolution and wear resistance performance of Al-Si-Sn-Cu/Ti-6Al-4V composite coatings.

This study investigated the metallurgical, mechanical properties and quality of coatings fabricated by direct laser metal deposition (DLMD) on Ti-6Al-4V, which were affected by the DLMD optimized process parameters. A 3-kW continuous wave ytterbium laser system (YLS) attached to a KUKA robot was used for the process. An analysis was conducted to determine the quality of the coatings in terms of hardness and wear resistance. Variables such as the time of interlayer deposition, thickness of the substrate, the initial temperature of the substrate, and the number of deposited layers were also investigated. The independent/collective effect that each process parameter had on the metallurgical and mechanical properties of the deposited Ti-6Al-4V were made clear when the processing parameters were varied. Minute pores/defects that significantly affect the metallurgical and mechanical properties of clads were also identified. The results obtained from the designed experiments showed that the depth of Heat Affected Zone (HAZ) was inversely proportional to the thickness of the substrate; as the thickness of the substrate was increased, the HAZ depth decreased. Moreover, the intensity of the laser power also affects the HAZ depth. In addition, it was discovered that the initial conditions of the substrate at room temperature also affected the coatings in relation to pre-heated conditions. The analysis conducted in identifying and quantifying the porosity showed indication that the factors such as scanning speed, laser power and powder feed rate had a predominant influence on the porosity. The grain form and structure as well as the mechanical properties of the cladded layer were significantly affected by the optimized process parameters of DLMD process. The parameters investigated had a significant impact on the hardness and wear resistance performance. Furthermore, the results revealed that the highest hardness of one of the coatings was 1.97-times the substrate which had a hardness value of 302 HV. The outstanding wear resistance performance of Al-Si-Sn-Cu/Ti-6Al-4V composite coating is attributed to major hard intermetallic phases.