Assembly Assistance System with Decision Trees and Ensemble Learning.
Radu Sorostinean1,2, Arpad Gellert1, Bogdan-Constantin Pirvu3
1Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania.
Sensors (Basel, Switzerland)
|June 2, 2021
Summary
This study introduces decision tree and ensemble learning methods for predicting assembly steps. These methods enhance sensor-based assembly assistance systems by providing adaptive instructions for workers.
Area of Science:
- Manufacturing Engineering
- Human-Computer Interaction
- Artificial Intelligence
Background:
- Assembly processes require efficient support systems for workers of all experience levels.
- Current systems may lack adaptivity to real-time assembly progress and user states.
- Sensor-based systems offer potential for enhanced worker support through intelligent feedback.
Purpose of the Study:
- To develop and evaluate novel prediction methods for suggesting next assembly steps.
- To integrate these predictors into a sensor-based assembly assistance system.
- To compare decision tree and ensemble learning approaches against existing methods.
Main Methods:
- Utilized decision tree and ensemble learning algorithms for state prediction.
- Developed predictors as components for a sensor-based assembly assistance system.
- Evaluated predictors on experimental data from trainees, experienced workers, and mixed datasets.
Main Results:
- Decision tree-based prediction of assembly states offers novelty over stochastic or neural methods.
- Ensemble learning incorporating decision trees demonstrated superior performance.
- The proposed methods are well-suited for adaptive assembly support systems.
Conclusions:
- Ensemble learning with decision tree components is optimal for adaptive assembly support.
- The developed predictors can enhance manufacturing worker training and performance.
- Future work includes incorporating user emotion estimation for more adaptive support.
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