Artificial intelligence approaches for energy-efficient laser cutting machines

TL;DR

This paper introduces deep learning-based adaptive control systems for laser cutting machines that significantly reduce energy consumption by dynamically adjusting pump power based on material and smoke levels, promoting sustainability.

Contribution

It presents novel DL methodologies for real-time adaptive control of laser cutting exhaust systems, integrating material classification and smoke detection to optimize energy use.

Findings

20% to 50% reduction in energy consumption

Effective material classification with CNNs and transfer learning

Improved sustainability in manufacturing processes

Abstract

This research addresses the significant challenges of energy consumption and environmental impact in laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive control and the open-loop nature of CO2 laser suction pumps, this study utilizes closed-loop configurations that dynamically adjust pump power based on both the material being cut and the smoke level generated. To implement this adaptive system, diverse material classification methods are introduced, including techniques leveraging lens-less speckle sensing with a customized Convolutional Neural Network (CNN) and an approach using a USB camera with transfer learning via the pre-trained VGG16 CNN model. Furthermore, a separate DL model for smoke level detection is employed to simultaneously refine the pump's power output. This integration prompts the exhaust suction pump to automatically halt during inactive times and dynamically adjust power during operation, leading to experimentally proven and remarkable energy savings, with results showing a 20% to 50% reduction in the smoke suction pump's energy consumption, thereby contributing substantially to sustainable development in the manufacturing sector.