Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Intelligent sensing systems – Status of research at KaProm

Within Industrie 4.0 intelligent sensing systems represent an indispensable asset with significant role in enabling shifting from automated to intelligent manufacturing. Instead of being simple transducers, intelligent sensors are able to retrieve useful information from raw signal. They represent systems with integrated computation and communication capabilities, that run sophisticated and real time applicable algorithms and communicate the necessary information to the other elements of the manufacturing facility. In this paper we present the recent research results in the field of intelligent sensing systems that were accomplished at Laboratory for Manufacturing Automation and Laboratory for Robotics and Artificial Intelligence at Department for Production Engineering (KaProm) at Faculty of Mechanical Engineering in Belgrade. Presented systems are intended for application in various manufacturing processes, such as machining, assembly, manipulation, material transport, rubber processing lines. They are based on application of different non-stationary signal processing (Discrete Wavelet Transform, Huang-Hilbert transform) and machine learning and artificial intelligence techniques (Support Vector Machines, Artificial Neural Networks, bio-inspired algorithms, clustering methods, fuzzy inference mechanisms). The most of developed systems are implemented in embedded devices and their real-world applicability is demonstrated