This paper presents OpenLMD, a novel open-source solution for on-line multimodal monitoring of Laser Metal Deposition (LMD). The solution is also applicable to a wider range of laser-based applications that require on-line control (e.g. laser welding). OpenLMD is a middleware that enables the orchestration and virtualization of a LMD robot cell, using several open-source frameworks (e.g. ROS, OpenCV, PCL). The solution also allows reconfiguration by easy integration of multiple sensors and processing equipment. As a result, OpenLMD delivers significant advantages over existing monitoring and control approaches, such as improved scalability, and multimodal monitoring and data sharing capabilities.
We present a novel approach for real-time defect detection and classification in laser welding processes based on the use of uncooled PbSe image sensors working in the MWIR range. The spatial evolution of the melt pool was recorded and analyzed during several welding procedures. A machine learning approach was developed to classify welding defects. Principal components analysis (PCA) is used for dimensionality reduction of the melt pool data. This enhances classification results and enables on-line classification rates close to 1 kHz with non-optimized code prototyped in Python. These results point to the feasibility of real-time defect detection.
The combination of flexibility, productivity, precision and zero-defect manufacturing in future laser-based equipment are a major challenge that faces this enabling technology. New sensors for online monitoring and real-time control of laserbased processes are necessary for improving products quality and increasing manufacture yields. New approaches to fully automate processes towards zero-defect manufacturing demand smarter heads where lasers, optics, actuators, sensors and electronics will be integrated in a unique compact and affordable device.
Many defects arising in laser-based manufacturing processes come from instabilities in the dynamics of the laser process. Temperature and heat dynamics are key parameters to be monitored. Low cost infrared imagers with high-speed of response will constitute the next generation of sensors to be implemented in future monitoring and control systems for laser-based processes, capable to provide simultaneous information about heat dynamics and spatial distribution.
This work describes the result of using an innovative low-cost high-speed infrared imager based on the first quantum infrared imager monolithically integrated with Si-CMOS ROIC of the market. The sensor is able to provide low resolution images at frame rates up to 10 KHz in uncooled operation at the same cost as traditional infrared spot detectors. In order to demonstrate the capabilities of the new sensor technology, a low-cost camera was assembled on a standard production laser welding head, allowing to register melting pool images at frame rates of 10 kHz. In addition, a specific software was developed for defect detection and classification. Multiple laser welding processes were recorded with the aim to study the performance of the system and its application to the real-time monitoring of laser welding processes. During the experiments, different types of defects were produced and monitored. The classifier was fed with the experimental images obtained. Self-learning strategies were implemented with very promising results, demonstrating the feasibility of using low-cost high-speed infrared imagers in advancing towards a real-time / in-line zero-defect production systems.
Laser cladding processing has been used in different industries to improve the surface properties or to reconstruct damaged pieces. In order to cover areas considerably larger than the diameter of the laser beam, successive partially overlapping tracks are deposited. With no control over the process variables this conduces to an increase of the temperature, which could decrease mechanical properties of the laser cladded material. Commonly, the process is monitored and controlled by a PC using cameras, but this control suffers from a lack of speed caused by the image processing step. The aim of this work is to design and develop a FPGA-based laser cladding control system. This system is intended to modify the laser beam power according to the melt pool width, which is measured using a CMOS camera. All the control and monitoring tasks are carried out by a FPGA, taking advantage of its abundance of resources and speed of operation. The robustness of the image processing algorithm is assessed, as well as the control system performance. Laser power is decreased as substrate temperature increases, thus maintaining a constant clad width. This FPGA-based control system is integrated in an adaptive laser cladding system, which also includes an adaptive optical system that will control the laser focus distance on the fly. The whole system will constitute an efficient instrument for part repair with complex geometries and coating selective surfaces. This will be a significant step forward into the total industrial implementation of an automated industrial laser cladding process.