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Communication Dans Un Congrès Année : 2022

Fault diagnosis of CNC machine-tools for drilling Titanium alloy

Résumé

The development of intelligent techniques based on real-time monitoring for machining applications is one of the challenges of Industry 4.0, as in the Aerospace Industry. Drilling is the most used process before the assembly of airplane sheets, that nowadays are composed of different layers of materials with different optimized cutting conditions. The fault diagnosis during drilling stack materials is important to reduce cost and improve the process quality. Using a machine-tool, it is important that the fault diagnoser does not use a large amount of memory and be capable of detecting faults in a fast manner. In this paper, we propose a timed automaton model representing the drilling process of a Titanium plate on a CNC machine, which is suitable for fault diagnosis without any additional sensors. The diagnoser uses only the spindle power and Z axis displacement read directly from the system controller. The target faults in this case are: (i) excessive tool-wear or tool breakage; (ii) the tool finds an off-centered hole while producing a blind-hole; (iii) the tool finds an under layer of a different material, as it occurs in a bi-layer material; and (iv) the plate thickness is below the desired one and a though hole is produced. The results show that the model is capable of identifying all faults and it could be used to alert a problem on the sequence of machining holes in the industry.
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Dates et versions

hal-03904691 , version 1 (17-12-2022)

Identifiants

  • HAL Id : hal-03904691 , version 1

Citer

Anna Carla Araujo, Marcos Vicente Moreira, Yann Landon. Fault diagnosis of CNC machine-tools for drilling Titanium alloy. 15th Conference on Intelligent Computation in Manufacturing Engineering, "Innovative and Cognitive Production Technology and Systems", Jul 2021, Naples, Italy. ⟨hal-03904691⟩
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