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.

Design of a training tool for improving the use of hand-held detectors in humanitarian demining

Purpose – The purpose of this paper is to introduce the design of a training tool intended to improve deminers' technique during close-in detection tasks. Design/methodology/approach – Following an introduction that highlights the impact of mines and improvised explosive devices (IEDs), and the importance of training for enhancing the safety and the efficiency of the deminers, this paper considers the utilization of a sensory tracking system to study the skill of the hand-held detector expert operators. With the compiled information, some critical performance variables can be extracted, assessed, and quantified, so that they can be used afterwards as reference values for the training task. In a second stage, the sensory tracking system is used for analysing the trainee skills. The experimentation phase aims to test the effectiveness of the elements that compose the sensory system to track the hand-held detector during the training sessions. Findings – The proposed training tool will be able to evaluate the deminers' efficiency during the scanning tasks and will provide important information for improving their competences. Originality/value – This paper highlights the need of introducing emerging technologies for enhancing the current training techniques for deminers and proposes a sensory tracking system that can be successfully utilised for evaluating trainees' performance with hand-held detectors.

Integration of Correlation Processes in Design Systems.

The integration of correlation processes in design systems has as a target measurements in 3D directly and according to the users criteria in order to generate the required database for the development of the project. In the phase of photogrammetric works, internal and external orientation parameters are calculated and stereo models are created from standard images. The aforementioned are integrated in the system where the measurement of the selected items is done by applying developed correlation algorithms. The processing period has the tools to carry out the calculations in an easy and automatic way, as well as image measurement techniques to acquire the most correct information. The proposed software development is done on Visual Studio platforms for PC, applying the most apt codes and symbols according to the terms of reference required for the design. The results of generating the data base in an interactive way with the geometric study of the structures, facilitates and improves the quality of the works in the projects.