Research on machining parameter optimization in finishing milling with multiple constraints

High efficiency and high precision milling, as the eternal goal of CNC machining, needs to balance many constraints for selecting the most reasonable processing parameters. This paper presents an efficient machining parameter optimization method for finishing milling operation with multiple constraints. Firstly, under the multiple constraints of parameter feasible region, milling force, milling stability, roughness, and machining contour accuracy, a multi-variable parameter optimization model with machining efficiency as the objective is established. A four level cycle optimization strategy has been detailly described for solving the optimization problem, in which the feed per tooth is optimized by using the golden section method, and with the aid of the random vector search method, the spindle speed, radial, and axial depth cuts are both numerically iterated. The optimal machining parameter combination of the tooth number, feed per tooth, spindle speed, radial, and axial depth of cuts are achieved at last. Finally, the experimental verification results show that the proposed method can greatly improve the machining efficiency under chatter free condition and achieve an efficient finishing milling with consideration of the multiple constraints.

Computational Modeling of Flow Characteristics in Three Products Hydrocyclone Screen

Three products hydrocyclone screen (TPHS) can be considered as the combination of a conventional hydrocyclone and a cylindrical screen. In this device, particles are separated based on size under the centrifugal classification coupling screening effect. The objective of this work is to explore the characteristics of fluid flow in TPHS using the computational fluid dynamics (CFD) simulation. The 2 million grid scheme, volume fraction model, and linear pressure–strain Reynolds stress model were utilized to generate the economical grid-independence solution. The pressure profile reveals that the distribution of static pressure was axisymmetric, and its value was reduced with the increasing axial depth. The maximum and minimum were located near the tangential inflection point of the feed inlet and the outlets, respectively. However, local asymmetry was created by the left tangential inlet and the right screen underflow outlet. Furthermore, at the same axial height, the static pressure gradually decreased along the wall to the center. Near the cylindrical screen, the pressure difference between the inside and the outside cylindrical screen dropped from positive to negative as the axial depth increased from −35 to −185 mm. Besides, TPHS shows similar distributions of turbulence intensity I, turbulence kinetic energy k, and turbulence dissipation rate ε; i.e., the values fell with the decrease in axial height. Meanwhile, from high to low, the pressure values are distributed in the feed chamber, the cylindrical screen, and conical vessel; the value inside the screen was higher than the outer value.

Influence of the Cut Axial Depth on Surface Roughness at High-Speed Milling of Thin-Walled Workpieces

The paper presents the results of research on the dynamics of end milling of thin-walled work-pieces having complex geometric shapes. Since the milling process with shallow depths of cut is characterized by high intermittent cutting, the proportion of regenerative vibrations decreases, and the effect of forced vibrations on the dynamics of the process, on the contrary, increases. The influence of axial depth of cut on the vibrations arising during processing, and roughness of the processed surface have been studied in paper. The experiments have been carried out in a wide range of changes in the spindle speed at different axial cutting depths. Vibrations of a thin-walled work-piece have been recorded with an inductive sensor and recorded in digital form. Then an oscillogram has been used to estimate the amplitude and frequency of oscillations. The profilograms of the machined surface have been analysed. Roughness has been evaluated by the parameter Ra. The results have shown similar relationships for each of the investigated axial cutting depths. The worst cutting conditions have been observed when the natural vibration frequency coincided with the tooth frequency or its harmonics. It is shown that the main cause of vibrations in high-speed milling is forced rather than regenerative vibrations. Increasing the axial depth of cut at the same spindle speed increases the vibration amplitude. However, this does not significantly affect the roughness of the processed surface in cases when it comes to vibration-resistant processing.

Experimental Investigation and Multi-Response Optimization of Machinability of AA5005H34 Using Composite Desirability Coupled with PCA

Minimum quantity lubricant (MQL) is an advanced technique in machining to achieve sustainability, productivity, higher precision, economic benefits, and a reduction in carbon footprints. The present research work aims to investigate the effect of the cutting process parameters of the end milling of AA5005H34 material under dry and MQL cutting environments. The key performance indicators of machining include the surface roughness profile, the material removal rate, and tool wear. Surface roughness parameters are measured with the help of the Mitutoyo surface roughness tester, and the cutting tool wear is measured according to the ISO 8688-2:1989 standard using a scanning electron microscope (SEM). Sixteen experiments are designed based on the Taguchi orthogonal array mixture design. Single responses are optimized based on signal to noise ratios, while for multi-response optimization composite desirability function coupled with principal component analysis is applied. Analysis of variance (ANOVA) results revealed that the feed rate followed by spindle speed, axial depth of the cut, width of the cut, and cutting environment are the most significant factors contributing to the surface roughness profile, material removal rate, and tool wear. The optimized parameters are obtained as cutting speed of 3000 rev/min, feed rate of 350 mm/min, axial depth of cut of 2 mm, and width of cut of 6 mm under an MQL environment.

Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA

Super-alloys have high thermal and mechanical strength and are widely used for heat exchangers, turbine blades, and other parts which work under severe creep conditions. Machinability of these alloys is directly affected by mechanical and physical properties. In addition, cutting force and surface roughness are two important factors in machinability of alloys. Hence, numerous studies have been conducted in order to illustrate their influences. However, among these alloys, the machining of Inconel 738 has been less studied. Milling parameters such as cutting speed, feed rate, the axial depth of cutting, and coolant have the most effects on machinability of nickel-based super-alloys. Therefore, in this research, they are considered as input parameters for investigation of milling of Inconel 738. The present study utilizes artificial intelligence as an effective method for predicting milling forces and surface roughness based on experimental results. To investigate the behavior of this alloy, four levels for the two former input parameters and two levels for the two other, totally 64 experiments, were fulfilled and studied. Based on the experimental results, the effect of input parameters on the outputs, that is, cutting force and surface roughness, was investigated, and then, neural network for modeling and predicting and genetic algorithm for the optimization of the outputs have been utilized. The optimized artificial network, which was obtained in this research, is useful for prediction of machining force and surface roughness of milling based on the values of cutting speed, feed rate, and the axial depth of cutting, for wet and dry milling of Inconel 738.