Automated drilling optimization method

A method execution of an automated machining process using a numerical control machine comprising:

• i) identifying, with a unique identifier defined by the numerical control machine, a drill process to be performed by a drilling tool at an event (t);
• ii) storing a theoretical position of the drill process executed at the event (t), wherein the theoretical position is a pre-configured static position;
• iii) calculating a learned position for the drilling tool using a machine learning model and based on the stored theoretical position for the event (t);
• iv) estimating an intermediate position for the drilling tool by applying a statistical tendency function,
• v) if a difference between the intermediate position and the learned position is less than a pre-configured threshold, using the intermediate position to position the drilling tool to execute another drill process at subsequent event (t+1);
• and
• vi) if the difference between the intermediate position and the learned position is greater than the pre-configured threshold, storing the intermediate position for the subsequent event (t+1) and repeating the steps iii) and iv),
• wherein the step iii) of calculating the learned position at the event (t) comprises:
• calculating a normal vector for the learned position at the event (t), wherein the normal vector is orthogonal to a surface on a workpiece to be drilled, and the normal vector is defined by components It, Jt and Kt;
• and
• calculating, using a machine learning model, a correction vector for the event (t), wherein the machine learning model is defined by components Correction_It, Correction_Jt and Correction_Kt, wherein the machine learning model combines a first machine learning model for the component It, a second machine learning model for the component Jt and a third machine learning model for the component Kt,
• wherein the machine learning model receives as input data comprising a theoretical vector defined by Theoretical_Xt, Theoretical_Yt and Theoretical_Zt, which are coordinates in the three dimensional space defining the theoretical position of the drill to be executed at the event (t);
• wherein the first machine learning model returns as output the first component Correction_It of the correction vector;
• wherein the second machine learning model returns as output the second component Correction_Jt of the correction vector;
• wherein the third machine learning model returns as output the third component Correction_Kt of the correction vector, and
• wherein the learned position being defined in the three dimensional space by the coordinates Xt, Yt and Zt.