It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.

No abstract

No abstract

A flexible new technique is presented to calibrate the monocular system of phase-based fringe projection profilometry, which is made of a camera, a projector, and a computer. The proposed algorithm mainly consists of a more flexible phase-to-height conversion model and a minimum norm solution, followed by a nonlinear optimization based on the maximum likelihood criterion. In the whole calibration procedure, this method only requires the camera to capture a few two-dimensional checkerboard target images and several deformed fringe images with at least three different orientations. The proposed technique neither subjects to certain limitations nor measures additional geometry parameters in advance. Also, no highly precise gauge blocks or extra reference phases are involved. In contrast with the existing methods, the proposed technique is easier to use and more flexible. Experiments have been performed to validate the performance of this technique.

No abstract