Adaptive Feed Rate Policies for Spiral Drilling Using Markov Decision Process

TL;DR

This paper introduces an MDP-based model to optimize feed rates in spiral drilling, aiming to enhance efficiency by making data-driven decisions that reduce cost and time.

Contribution

It presents a novel application of Markov Decision Processes to optimize feed rate policies in spiral drilling, based on experimental data and value iteration.

Findings

Optimal feed rate decisions improve drilling efficiency.

The model reduces drilling time and costs.

Decision policies adapt to different axial force conditions.

Abstract

In this study, the feed rate optimization model based on a Markov Decision Process (MDP) was introduced for spiral drilling process. Firstly, the experimental data on spiral drilling was taken from literature for different axial force parameters and with various feed rate decisions made, having the length of a hole being drilled as a reward. Proposed optimization model was computed using value iteration method. Secondly, the results of computations were displayed for optimal decision to be made on each state. Proposed decisions for an optimal feed rate could be utilized in order to improve the efficiency of spiral drilling process in terms of cost and time.