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This work was prepared within the project PM/SP/0063/2021/1 titled 'Innovative measurement technologies supported by digital data processing algorithms for improved processes and products', financed by the Ministry of Education and Science (Poland) as a part of the Polish Metrology Programme.
Materiał konferencyjny:
TAK
Nazwa konferencji:
11th International Workshop on Metrology for AeroSpace
The paper presents the results of temperature
measurements after machining samples made of AW-2024
aluminum alloy and S235 steel. The temperature was measured
at eight measurement points of examples. The machining
process was performed using variable cutting parameters, i.e.
wall thickness after machining, cutting depth and cutting speed.
The results estimation was made based on the statistical analysis
of the obtained measurements. The average values were used to
determine the time courses of cooling curves. To perform
calculations and numerical simulation, the artificial neural
network algorithm of the Matlab 2023b package was used.
Thanks to this, the time after which the workpiece can be
measured with an inspection probe was determined. As a result,
the measurement results showed that the use of artificial neural
networks allows for accurate prediction of the start time of the
probe measurement, which improves the efficiency of the
process and minimizes prediction errors.