CFRP drilling process control based on spindle power consumption from real production data in the aircraft industry

• Published in: Procedia CIRP Vol. 107; pp. 1533 - 1538
• Main Authors: Domínguez-Monferrer, C., Fernández-Pérez, J., Santos, R.De, Miguélez, M.H., Cantero, J.L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2022
• Subjects: Advanced manufacturing control; Data analysis; Digitization; Industry 4.0; Digitization; Data analysis; Industry 4.0; Advanced manufacturing control
• DOI: 10.1016/j.procir.2022.05.187
• ISSN: 2212-8271

Ongoing challenges in advanced manufacturing highlight the need of improved control of the production system, higher production speed, lower tool wear, top quality standards together with reduced material waste to minimize associated costs, time and environmental footprint. The organization of production resources through the integration of data along the value chain using Information Technologies is required to achieve these challenges. Thus, the emergence of the fourth industry revolution brings with it the organization of productive resources through computational intelligence and connectivity. This research seeks to analyze the spindle power consumption in Carbon-fiber-reinforced polymer composites (CFRPs) drilling operations as a process control indicator in terms of tool wear. This signal stands out among others available because it can be obtained in real time, with high quality and through a non-intrusive methodology. In particular, the study is framed in the real production system in factories of Airbus. The industrial process data were directly collected from the manufacturing plant in Getafe (in the Madrid-Spain region) and correspond to more than 3000 holes drilled with diamond-coated tungsten carbide tools. The variability of machining conditions in the aeronautical component drilling process and inherent noise level of signals obtained in industrial environments required the development of a data wrangling methodology to structure and clean the information. As a result, different magnitudes were obtained from spindle power consumption signal related to tool wear with low levels of dependence on drilling conditions. The conclusions of this work are directly applicable to the control of industrial production systems within the framework of Industry 4.0, searching new improvement opportunities through Data analytics and Artificial Intelligence such as tool breakage detection or the optimization of cycle times.