Research on the Relationship between Cutting Force and Machined Surface Quality in Micro Ball End-Milling of Potassium Dihydrogen Phosphate Crystal

Abstract

Potassium dihydrogen phosphate (KDP or KH2PO4) crystal is widely used as terminal frequency converters in inertial confinement fusion (ICF). However, KDP crystal is a typical difficult-to-cut optical crystal with the characteristic of soft-brittle. In this work, the relationship between cutting force and processed surface quality in micro ball end-milling of KDP crystal with various depth of cut and spindle speed is studied by carried out the micro-milling experiments. Fast Fourier Transform (FFT) algorithm is used to diagnose the recorded cutting force. The periodic change of cutting force and the cutting force after filtering noises can be got through FFT analysis. Through calculating the correlation coefficients between the static component of thrust force and roughness value Ra of machined grooves, as well as the peak-valley (P-V) value of thrust force and dimensional error of machined grooves, the roughness value Ra and dimensional error of machined grooves would be predicted by monitoring the static component and P-V value of the thrust force, respectively. The relatively large spindle speed helps to reduce the roughness value Ra. The spindle speed with moderate value is recommended to reduce the dimensional error of machined groove because the dimensional error of machined groove will increase when the spindle speed is small enough (causing brittle cutting) or large enough (reducing cutting stability).

Micro-Milling Tool Wear Monitoring via Nonlinear Cutting Force Model

“…Generally, tool wear monitoring could be categorized into two methods: data-driven monitoring and mechanism-based monitoring [5,6]. In the data-driven methods, the monitoring model is built with the tool wear data and the cutting signals such as the cutting force [7], vibration [8], and acoustic emission [9,10] collected in the practical machining process. Hsieh et al [11] collected the vibration signals to train the backpropagation neural network for tool wear monitoring.…”

Identification method for the dynamic cutting force variation of a high energy efficiency milling cutter

“…The dynamic cutting force is affected by the machine tool, workpiece, tool, cutting parameters, and other factors. 5–15 Among them, the relationship between the dynamic cutting behavior is the key to unveil the dynamic cutting force variation. Different distribution of cutter teeth errors are inevitable in terms of manufacturing and installation.…”