A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems

Digital Twin Framework for Lathe Tool Condition Monitoring in Machining of Aluminium 5052

Digital Twin (DT) is a virtual representation of a product system that exhibits the properties and analyzes the system’s functions. The significant impact of DT extends to several fields, which increases productivity and reduces wastage. This article focuses on developing a Digital twin model of a Lathe machine for Tool Condition Monitoring (TCM). DT implementation in industries is challenging due to simulating online cutting forces and wear. Even though several pieces of research have been carried out in the prediction of tool conditions using machine learning, Artificial Neural network models, only a few pieces of research have been made in digital twins for TCM. This article provides the technique for implementing the DT model of a lathe tool. The feasibility of the DT Model framework is verified by a case study of the turning process with a CNC Lathe machine while machining of Aluminium 5052 workpiece using Titanium Nitride coated tool inserts. The sensor’s data are acquired and fed to the microcontroller for real-time data acquisition. The real-time dataset is processed in the DT model for monitoring and predicting the tool conditions. The tool wear classification using the DT model is achieved. Developing the Digital Twin model in machining increases productivity and assists in predictive maintenance

Multilayer vectorization to develop a deeper image feature learning model

Computer-Aided Diagnosis (CAD) approaches categorise medical images substantially. Shape, colour, and texture can be problem-specific in medical imagery. Conventional approaches rely largely on them and their relationship, resulting in systems that can't illustrate high-issue domain ideas and have weak prototype generalization. Deep learning techniques deliver an end-to-end model that classifies medical photos thoroughly. Due to the improved medical picture quality and short dataset size, this approach may have high processing costs and model layer restrictions. Multilayer vectorization and the Coding Network-Multilayer Perceptron (CNMP) are merged with deep learning to handle these challenges. This study extracts a high-level characteristic using vectorization, CNN, and conventional characteristics. The model's steps are below. The input picture is vectorized into a few pixels during preprocessing. These pixel images are delivered to a coding network being trained to create high-level classification feature vectors. Medical imaging fundamentals determine picture properties. Finally, neural networks combine the collected features. The recommended technique is tested on ISIC2017 and HIS2828. The model's accuracy is 91% and 92%