Titanium alloys are widely used in the manufacture of aircraft and aeroengine components. However, tool wear is a serious concern in milling titanium alloys, which are known as hard-to-cut materials. Trochoidal milling is a promising technology for the high-efficiency machining of hard-to-cut materials. Aiming to realize green machining titanium alloy, this paper investigates the effects of undeformed chip thickness on tool wear and chip morphology in the dry trochoidal milling of titanium alloy Ti–6Al–4V. A tool wear model related to the radial depth of cut based on the volume of material removed (VMR) is established for trochoidal milling, and optimized cutting parameters in terms of cutting speed and axial depth of cut are selected to improve machining efficiency through reduced tool wear. The investigation enables the environmentally clean rough machining of Ti–6Al–4V.

In the milling process, cutting forces contain key information about the machining process status in terms of workpiece quality and tool condition. On-line cutting force measurement is key for machining condition monitoring and machined surface quality assurance. This paper presents a novel instrumented working table with integrated polyvinylidene fluoride (PVDF) thin-film sensors, thus enabling the dynamic milling force measurement with compact structures. To achieve this, PVDF thin-film sensors are integrated into the working table to sense forces in different directions and the dedicated cutting force decoupling model is derived. A prototype instrumented working table is developed and validated. The validation demonstrates that profiles of the forces measured from the developed instrumented working table prototype and the dynamometer match well. Furthermore, the milling experiment results convey that the instrumented working table prototype could also identify the tool runout due to tool manufacturing or assembly errors, and the force signal spectrum analysis indicates that the developed working table can capture the tool passing frequency correctly, therefore, is suitable for the milling force measurement.

Thin-walled workpieces, such as aero-engine blisks and casings, are usually made of hard-to-cut materials. The wall thickness is very small and it is easy to deflect during milling process under dynamic cutting forces, leading to inaccurate workpiece dimensions and poor surface integrity. To understand the workpiece deflection behavior in a machining process, a new real-time nonintrusive method for deflection monitoring is presented, and a detailed analysis of workpiece deflection for different machining stages of the whole machining process is discussed. The thin-film polyvinylidene fluoride (PVDF) sensor is attached to the non-machining surface of the workpiece to copy the deflection excited by the dynamic cutting force. The relationship between the input deflection and the output voltage of the monitoring system is calibrated by testing. Monitored workpiece deflection results show that the workpiece experiences obvious vibration during the cutter entering the workpiece stage, and vibration during the machining process can be easily tracked by monitoring the deflection of the workpiece. During the cutter exiting the workpiece stage, the workpiece experiences forced vibration firstly, and free vibration exists until the amplitude reduces to zero after the cutter exits the workpiece. Machining results confirmed the suitability of the deflection monitoring system for machining thin-walled workpieces with the application of PVDF sensors.

The artificial fish swarm algorithm adopts a new top-down way of think-ing,starting from the realization of individual behaviors of artificial fish and then re-aching the global optimum of the group through the local optimization of fish individuals of the group.Based on the finite element analysis softwareMSC.Marc, this article makes inversion analysis for mechanical parameters of dam.By combining the monitoring data of a dam,inversion analysis [ is made on the elastic modulus of the dam body,dam foundation,both dam shores and the fault fracture zone by using the artificial fish swarm algorithm.Results show that using artificial fish swarm algorithm to make inversion analysis on dam mechanical parameters not only has faster con-vergence rate but also have higher accuracy.

The traditional analytical cutting force prediction method for ball-end milling ignores the effect of the inclination angle on cutting forces. In this paper, a new experimental method for cutting force prediction methods considering the inclination angle in the ball-end milling process is proposed. First, the actual immersion ranges of cutter in the ball-end milling process with and without an inclination angle are analyzed by a geometrical method and the cutting force prediction model with an inclination angle is developed by a numerical integration method. Second, considering that entry and exit angles of cutting zones for different cutter layers vary due to the inclination angle, a milling force coefficients identification approach for different cutter layers is established by experimental calibration. Comparing the traditional analytical cutting force prediction method that ignores the inclination angle, the numerical simulation results show that the prediction force values calculated by the proposed method have a better consistency with the measured values.