Abstract

Cutting force prediction is the key issue for planning and optimising the machining process. To explore the cutting of difficult-to-machine 1Cr13 martensitic stainless steel, an orthogonal test was conducted to study the cutting force and surface roughness under the dry, full-width milling. In addition, an empirical model, applied to the exponential forms of the cutting force and surface roughness of 1Cr13 martensitic stainless steel, was established by data processing and linear regression. It can be seen that the significance levels of the influence of cutting parameters on cutting force, surface roughness were cutting depth [Formula: see text], feed per tooth f and cutting speed v during APMT 1135 PDTR coated carbide tool milling of 1Cr13 stainless steel at high speed by testing the significance of regression relationships and their coefficients. It was found that the predictive model offered high accuracy. The cutting parameters that affect the cutting force and surface roughness in milling machining were analysed and the common rules were concluded in this article based on a series of cutting experiments. And those rules can be used as the scientific theory evidence for cutting-parameter selection in the milling machining.

Highlights

  • Numerical control (NC) milling machining has been widely used in aerospace, automotive, machinery applications and so on

  • (6)–(9)), it can be seen that the significance levels of the influence of cutting parameters on cutting force, namely, Fx, Fy and Fz as well as surface roughness were cutting depth ap, feed per tooth f and cutting speed v during APMT 1135 PDTR coated carbide tool milling of 1Cr13 stainless steel at high speed

  • The changes in cutting force were predicted by the proposed statistical model of the metal cutting process

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Summary

Introduction

Numerical control (NC) milling machining has been widely used in aerospace, automotive, machinery applications and so on. Mathematical models of the cutting force and surface roughness, as influenced by cutting speed, feed per tooth and cutting depth, were established using probabilistic methods, such as the least squares method, with regression analysis used to test the significance of the relationship and its coefficients. On this basis, the influence of cutting parameters on milling force was analysed to predict and control the cutting force during process design to improve the quality of the milled surface. Fz (f) with radial cutting force Fr (f), tangential cutting force Ft (f) and axial cutting force Fa (f) can be expressed as follows

54 Fr ðfÞ 5
Experimental results and analysis
Conclusion
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