Height Control in Laser Cladding Using Adaptive Sliding Mode Technique: Theory and Experiment

Ƶeinali, Meysar; Khajepour, Amir

doi:10.1115/1.4002023

Cited by 32 publications

(11 citation statements)

References 16 publications

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“…Therefore, the CMOS cameras have better signal processing speed, but the capture area is limited compared to a CCD camera. The main disadvantage of using cameras for in situ sensing of the L-PBF process is the speed of the process, which requires a high frame rate camera that results in an enormous size of data that are difficult to post-process [74][75][76].…”

Section: Digital Camerasmentioning

confidence: 99%

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

et al. 2020

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In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology.

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Section: Digital Camerasmentioning

confidence: 99%

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

et al. 2020

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“…Most of the commercial cameras readily have their custom software for image processing; however, many works in the literature relied on devel oping tailored algorithms and software to fulfill their specific needs [27][28][29][30]. High-speed cam eras with the average frame rate of 1000 frames per second have been used for this purpose.…”

Section: Pyrometersmentioning

confidence: 99%

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

Tapia

Elwany

2014

Journal of Manufacturing Science and Engineering

550

188

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There is consensus among both the research and industrial communities, and even the general public, that additive manufacturing (AM) processes capable o f processing metal lic materials are a set o f game changing technologies that offer unique capabilities with tremendous application potential that cannot be matched by traditional manufacturing technologies. Unfortunately, with all what AM has to offer, the quality and repeatability o f metal parts still hamper significantly their widespread as viable manufacturing proc esses. This is particularly true in industrial sectors with stringent requirements on part quality such as the aerospace and healthcare sectors. One approach to overcome this challenge that has recently been receiving increasing attention is process monitoring and real-time process control to enhance part quality and repeatability. This has been addressed by numerous research efforts in the past decade and continues to be identified as a high priority research goal. In this review paper, we fill an important gap in the liter ature represented by the absence o f one single source that comprehensively describes what has been achieved and provides insight on what still needs to be achieved in the field o f process monitoring and control fo r metal-based AM processes.

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“…The experimental results showed satisfactory outcomes in the laser cladding process. Finally, Zeinali et al [27] presented a real-time acquisition and control of the clad height. The substrate velocity is taken as the input of the system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of Layer Height in Laser Wire Additive Manufacturing

et al. 2022

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Laser Wire Additive Manufacturing (LWAM) is a flexible and fast manufacturing method used to produce variants of high metal geometric complexity. In this work, a physics-based model of the bead geometry including process parameters and material properties was developed for the LWAM process of large-scale products. The developed model aimed to include critical process parameters, material properties and thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate, and the travel speed). Then, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant taking into consideration the constraints faced in the LWAM technology. Experimental validation results were performed to check the accuracy of the proposed model and the results revealed that the developed model matches the experimental data. Finally, the designed MPC controller was able to track a predefined layer height reference signal by controlling the temperature input of the system.

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Height Control in Laser Cladding Using Adaptive Sliding Mode Technique: Theory and Experiment

Cited by 32 publications

References 16 publications

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

Modeling and Control of Layer Height in Laser Wire Additive Manufacturing

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