Research of multi-response optimization of milling process of hardened S50C steel using minimum quantity lubrication of Vietnamese peanut oil

This study aims to build a regression model when surveying the milling process on S50C steel using Minimum Quantity Lubrication (MQL) of Vietnamese peanut oil-based on Response Surface Methodology. The paper analyses and evaluates the effect of cutting parameters, flow rates, and pressures in minimum quantity lubrication system on cutting force and surface roughness in the milling process of S50C carbon steel materials after heat treatment (reaching a hardness of 52 HRC). The Taguchi method, one of the most effective experimental planning methods nowadays, is used in this study. The statistical analysis software, namely Minitab 19, is utilized to build a regression model between parameters of the cutting process, flow rates and pressures of the minimum quantity lubrication system and the cutting force, surface roughness of the part when machining on a 5-axis CNC milling machine. Thereby analyzing and predicting the effect of cutting parameters and minimum quantity lubrication conditions on the surface roughness and cutting force during machining to determine the influence level them. In this work, the regression models of Ra and F were achieved by using the optimizer tool in Minitab 19. Moreover, the multi-response optimization problem was solved. The optimum cutting parameters and lubricating conditions are as follows: Cutting velocity Vc=190.909 m/min, feed rate fz=0.02 mm/tooth, axial depth of cut ap=0.1 and nozzle pressure P=5.596 MPa, flow rate Q=108.887 ml/h. The output parameters obtained from the above parameters are Ra=0.0586  and F=162.035 N, respectively. This result not only provides the foundation for future research but also contributes reference data for the machining proces

Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

With modern production, Minimum Quantity Lubricant (MQL) technology has emerged as an alternative to conventional liquid cooling. The MQLs is an environmentally friendly lubricant method with low cost while meeting the requirements of machining conditions. In this study, the experimental and analytical results show that the obtained acoustic emission (AE) and vibration signal components can effectively monitor various circumstances in the SCM440 steel turning process with MQL, such as surface quality and chip formation as cutting tool conditions. The AE signals showed a significant response to the tool wear processes. In contrast, the vibration signal showed an excellent ability to reflect the surface roughness during turning with MQL. The chip formation process through the cutting mode parameters (cutting speed, feed and depth of cut) was detected through analysis amplitude of the vibration components Ax, Ay and Az and the AE signal. Finally, Gaussian process regression and adaptive neuro-fuzzy inference systems (GPR-ANFIS) algorithms were combined to predict the surface quality and tool wear parameters of the MQL turning process. Tool condition monitoring devices assist the operator in monitoring tool wear and surface quality limits, stopping the machine in case of imminent tool breakage or lower surface quality. With the unique combination of AE and vibration analysis model and the training and testing samples established by the experimental data, the corresponding average prediction accuracy is 97.57 %. The highest prediction error is not more than 3.8 %, with a confidence percentage of 98 %. The proposed model can be used in industry to predict surface roughness and wear of the tools directly during turnin

A new environment-friendly magnetorheological finishing and fuzzy grey relation analysis in Ti-6Al-4V alloy polishing

In this study, a naturally sourced cutting oil mixture using for the magnetorheological finishing (MRF) as an environmentally friendly carrier liquid. In addition, fuzzy grey relation analysis has been developed to predict and give optimal cutting parameters, the main factors affecting surface quality and material removal rate (MRR) identified. Experimental polishing procedures Ti-6Al-4V alloy were performed to confirm the availability of MRF models of the surface quality and MRR proposed. The fuzzy grey levels of elements to the polishing surface quality, namely the workpiece speed (nw), working distances (K), MRF carrier speed (nMRF) and feed rate (F), were 0.6983, 0.8057, 0.7818, and 0.7817, respectively. The analysis showed that the working distances (K) showed the most remarkable influence on the polishing effect, while the effect of workpiece speed (nw) was the least important. Microscopic observations significantly minimize scratches on the surface. This observation provides an excellent reference value for high surface quality and material removal rate when polishing Ti-6Al-4V alloys

Application of vibration singularity analysis, stochastic tool wear, and GPR-MOPSO hybrid algorithm to monitor and optimise power consumption in high-speed milling

Power consumption in manufacturing direct affects production costs and the environment. Therefore, the process of evaluating and researching power consumption in the machining process is very important. During high-speed milling, the power consumption varie`s due to tool wear and radial deviation. Therefore, a new model power consumption optimization is proposed based on cutting mode factors taking into account tool wear and radial deviation. In the existing power consumption models, studies on the effects of radial deviation and tool wear have not been thoroughly investigated. Stochastic tool wears established during high-speed milling is established in combination with the cutting force analysis model and wavelet singularity vibration point analysis. The nonlinear processes due to stochastic tool wear and cutting edge geometry were considered in the model. To optimize power consumption and establish a model for the real-time prediction of power consumption, a new GPR–MOPSO hybrid algorithm was developed based on Gaussian process regression (GPR) and multi-objective particle swarm optimizations (MOPSO). In order to verify the feasibility proposed monitoring and optimization model, experimental processes high-speed milling have been established. Results showed that the presented improvement model will reduce power consumption by 20.38% compared with manufacturer manuals chosen process parameters