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Vibrations are one of the obstacles to productivity of machining process since their presence reduces surface quality, dimensional accuracy and tool life. This article proposes a vision-based approach for determining vibration level in metal cutting. Vibration level of cutting tool is controlled by changing the tool overhang, and the resulting irregularity of surface texture is used as a criterion for determining the cutting tool vibration. Undecimated wavelet transform is used to decompose the surface image of the workpiece into sub-images in which the cutting tool vibration can be indicated. The texture of the preferred sub-image is analyzed using gray-level co-occurrence matrix texture features. In order to validate the proposed vision-based method, an accelerometer was attached to the shank of the cutting tool to measure vibrations in tangential direction. The experimental results showed that the combination of undecimated wavelet decomposition and gray-level co-occurrence matrix texture features can be used as a robust method for determining vibration level in the turning process.

The amount of flank wear is often used as the criterion for tool life assessment because it influences work material surface roughness and accuracy. In this study, the influence of cutting tool geometry (angles) on morphology of flank wear land during turning of low carbon steel is investigated by using image processing techniques and Response Surface Methodology (RSM). The analysis was based on a second order model in which the flank wear (area and shape factors) is expressed as a function of three cutting tool geometry parameters (relief angle, rake angle and cutting edge angle) and the effect of tool geometry parameters and their interactive effect on flank wear land morphology have been investigated. It is found that the area and shape factors of flank wear land are highly affected by the cutting tool geometry parameters considered in the present study.

To address the compounded uncertainty in the observed output data, we introduce a new method of fuzzy regression modeling which is based on quadratic programming and fuzzy weights, so that the objective function represents the quadratic error for all of the central tendencies and the spreads. Also, the fuzzy weights are optimized for the fuzzy regression model estimation with crisp input and fuzzy output based on the adaptive fuzzy networks, considering symmetrical triangular fuzzy output. This paper aims to use the proposed method for the prediction of the output value in empirical applications where the observed value is a range or mean of several values, rather than a real fixed number. Two numerical examples were employed to demonstrate the efficiency of the method and compare the results of the proposed method with the previous ones such as linear programming (LP), quadratic programming (QP), as well as combination of linear programming and fuzzy weights (FWLP). The results show that the proposed method provides better prediction accuracy than other methods in surface roughness prediction of the grinding process.