Multi Class Multi Output Auto ML Model for Two Axis Precision Gantry

Multi Class Multi Output Auto ML Model for Two Axis Precision Gantry

Deepak Mohan Dharne

Mechanical Engineering Dept,

DPCOE, Pune, India

Dharne.deepak@gmail.com

Prof. Anantharama

Mechanical Engineering Dept,

DPCOE, Pune, India

baip1066@gmail.com

Abstract - The accurate prediction of design parameters in a 2-axis gantry system is crucial for optimizing performance, efficiency, and reliability. A 2-axis pick-and-place gantry is widely used in industrial automation for precise material handling, assembly, and packaging applications. It consists of linear actuators that enable movement along the X and Y axes, ensuring fast and accurate positioning. This study leverages AutoGluon Tabular AutoML, an advanced automated machine learning framework, to enhance predictive modeling for gantry design data. By integrating multiple machine learning models, AutoGluon autonomously selects the most effective algorithms, feature engineering techniques, and hyperparameter tuning strategies. The proposed approach significantly reduces the manual effort required for model selection and optimization while achieving high predictive accuracy. Experimental results demonstrate that AutoGluon outperforms conventional machine learning methods, making it a robust solution for automating design data predictions in industrial and automation applications.

Keywords: ML, Auto ML, Two axis Gantry, Industrial Automation, Driveworks, Autogluon, CAD.