Tool wear does great damage to product quality and machining efficiency. In this paper, a new tool condition monitoring (TCM) method based on a multi-sensor data fusion imaging and attention mechanism is proposed to indirectly measure and monitor tool wear. First, the multi-sensor signals collected during the operation of the machine tool are encoded and fused. A novel triangular matrix of angle summation method is proposed to fuse multi-sensor time-series signals at the data layer and to image them as two-dimensional images. The method used here effectively retains the detailed characteristics of the data, and also effectively retains the internal time relationship of the signal. In addition, a deep residual network with a convolution block attention module is used to extract the deep features from the encoded image and identify the wear stage of the tool. The proposed deep-learning network is then applied to TCM. It selects information in the channel and spatial domains based on its attention mechanism, and increases network depth by combining residual blocks to realize deep feature extraction. Compared with traditional machine learning methods, this end-to-end deep learning model does not rely on feature engineering, which requires a lot of expert knowledge and artificial experience. In our experimental study, a public data set was used to train the model, and the results showed that the accuracy of the model proposed in this paper was as high as 94%. To further verify its feasibility and effectiveness, the proposed model was also compared with other methods.

In order to guarantee machining quality and production efficiency and as well as to reduce downtime and cost, it is essential to receive information about the cutting tool condition and change it in time if necessary. To this end, tool condition monitoring systems are of great significance. Based on the data fusion technology of multi-sensor integration and the learning ability of deep residual network, a tool wear monitoring system is proposed in this paper. For data acquisition, multiple sensors are used to collect vibration, noise and acoustic emission signals in the machining process. For signal processing, the data fusion method of angular summation of matrices is used, so that the multi-source data are fused into two-dimensional pictures. Then, the feature extraction module of the proposed system uses the pictures as input and takes advantage of the deep residual convolutional network in extracting the deep features from the pictures, and finally the identification module completes the recognition of the tool wear type. To verify its feasibility, a testing bed is built on a CNC milling machine, and multi-sensor data are collected during the machining process of a simple workpiece with one of the two selected cutting tools each time. The proposed system is trained using the collected data and then is used to monitor the wear condition of a new cutting tool by collecting real-time data under similar condition. The results show that the accuracy of wear state recognition is as high as 90%, which verifies the feasibility of the proposed tool wear monitoring system.

Tomorrow's manufacturing workstation will be autonomous and intelligent with the ability to interpret geometric and manufacturing knowledge and automatically to manufacture components. STEP-NC is one of the building blocks that will support this endeavour for a new paradigm of the intelligent autonomous manufacturing. This paper outlines a framework for a STEP-NC controller, which represents one of the building blocks for tomorrow's manufacturing workstation. The conceptual controller consists of an interpreting module (interpreter), a planning module (planner), a simulation module and a CNC kernel. The interpreter reads an AP238 file and converts it into internal data, which the planner utilizes to sequence the specific machining operations and processes. The work illustrates the feasibility of the framework for interpreting STEP-NC information in an AP238 format and storing the data within a STEP compliant data structure for use in machine planning. The implementation tools and methods for interpreting the AP238 file are outlined through an example part/case study to demonstrate the STEP-NC conversion process.