In this article, a novel cutting force modelling approach is proposed by employing the specific cutting force and corresponding quantitative analysis on the dynamic cutting process in diamond turning so as to accurately represent the dynamic cutting behaviour including both amplitude and spatial aspects simultaneously. The specific cutting forces at the unit cutting length and area as the so-called amplitude aspect can provide insight into the micro cutting phenomena particularly in relation to the chip formation and size effects. The cutting forces are analysed against the dynamically varied cutting time interval, as the so-called spatial aspect using wavelet transform technique and standard deviation analysis can render their dynamic components to particularly represent dynamic effects of the cutting process and their correlation with tool wear. The cutting trials on titanium, silicon, and aluminium are carried out at a diamond turning test rig and supported with finite element analysis-based simulations, to further investigate the cutting force modelling and its correlation with the dynamic cutting process with a focus on the pressure distribution on the tool cutting edge, chip formation, and corresponding tool wear.

The condition of a cutting tool is an important factor to ultraprecision machining processes. Tool wear has a strong influence on the cutting forces, resulting in poor surface roughness and dimensional tolerance of the workpiece, particularly in ultraprecision machining hard brittle materials. This article presents a cutting force–based analysis and correlative observations on diamond tool wear in machining of single-crystal silicon. The Daubechies wavelet (dB3, level 4) was employed to correlate standard deviation of magnitude on the decomposed cutting and radial forces with initial diamond tool wear. Moreover, the flank wear and the micro-fracture were observed using scanning electron microscopy on the respective flank face and rake face of the diamond cutting tool used. No crater wear was detected on the rake face of the diamond tool until cutting distance of up to 9 km.

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Tool wear monitoring and real-time predicting tool life during the machining process is becoming a crucial element in modern manufacturing to properly determine the ideal point to replace tool, remains a challenge currently. In this paper, the model approach for in-process monitoring and predicting progressive tool wear by using machine vision is proposed. The developed method adopts machine vision to acquire tool wear images from a CCD camera. The emerged wear analysis is conducted based on the in-progress of signal processing on captured tool wear images, received throughout the cutting process. This automated analysis is carried out with programming to assess and compare a number of pixels of cutting edge images between cutting tools before machining and during the machining process.The developed system is evaluated through experiments of actual cutting conducted on the CNC turning machine with the proposed system installed to evaluate progressive wear during the machining process.Experimental results are capable of indicating the emerged wear at the current state by comparing the number of pixels between the new and used tools. Average ank wear (VB) is also evaluated linked to tool rejection criteria.The developed system is validated by the 3D microscope measuring actual wear on the used tool after cutting experiments. Comparative wear analysis is then performed by nding the correlation equation of pixels examined by a developed system and SMr2 value measured by the microscope. The results showed that the relationship between the number of pixels and SMr2 is a strong correlation.