In-Process Monitoring of Remote Laser Welding Process ... · Welding Process Based on Decoupled...
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Key Quality Indicator Cross Section Quality requirements Penetration ≥ 0.3 × Interface Width ≥ 0.9 × Top Concavity ≤ 0.5 × Upper Bottom Concavity ≤ 0.5 × In-Process Monitoring of Remote Laser Welding Process Based on Decoupled Multiple-physics Simulation Erkan Caner OZKAT, Pasquale FRANCIOSA, Darek CEGLAREK 2. Physics of Laser Welding There are two main operational modes in (remote) laser welding: keyhole and conduction modes. The keyhole mode occurs when the laser power intensity at a given scanning speed is sufficient to generate a deep narrow cavity, known as keyhole. The laser beam is absorbed by in-Bremsstrahlung thorough keyhole and Fresnel absorption at the keyhole wall. The keyhole is stabilised by the pressure from the metallic vapour (plasma) being generated and responsible for the quality of the weld. Therefore, choosing robust process parameters is crucial. In this research, the selection of process parameters is based on the manufacturing requirements [1]. Penetration Interface Width Top Concavity Bottom Concavity 4.a. Monitoring Expectations 6. Research Impact Current practice is mainly based on post process inspection which is time consuming and costly. This research offers the capability for in-process monitoring of multiple joint quality indicators and linking them to welding process parameters. The results of the research can be exploited on a broader spectrum and integrated as a closed-loop quality control strategy which helps to eliminate, reduce and correct defects before they occur which will lead to increased productivity and product quality. 4.b. Co-axial In-process Monitoring Methods Photodiode Based Monitoring Data-driven Approaches The existing (photodiode- and high speed camera-based) monitoring methods extract patterns from data gathered in real-time and correlate them only to a single joint key quality indicator. The lack of a comprehensive correlation between gathered data and multiple joint quality indicators underscores the limitations of current methods towards delivering automatic closed loop quality control system, with capabilities for adjustment on-the-fly. (1) Ozkat, E. C. et al. (2016), Process Parameters Selection for Laser Dimpling Process based upon Nested Hetero-Skedasticity Modelling and Reliable Parameter Space, submitted to JIM (2) Ozkat, E. C. et al. (2016), In-Process Monitoring of Remote Laser Welding Process Based on Decoupled Multiple-physics Simulation, submitted to ICALEO Conference 2016 (3) Kaplan, A. F. H. (1994), A Model of Deep Penetration Laser Welding Based on Calculation of the Keyhole Profile, Journal of Physics D: Applied Physics, 27, 1805–1814 1. Motivation & Objective A leading challenge preventing the systematic uptake of Remote Laser Welding (RLW) is the lack of efficient in-process monitoring and control to achieve and guarantee high quality weld under the process variability. Current solutions for in-process monitoring are data-driven, implying that predictive models are trained on gathered data using secondary information (plasma intensity, 2D images, etc.) and cannot be fully exploited outside of the training data set. As a consequence, changes in welding process parameters, such as laser power, welding speed, focal offset, and material thickness can be only handled by re-building, case-by-case, the predictive models. The objective of this research is to develop a novel scalable physics-driven model linking in- process monitoring data (plasma charge, acoustic, optical emissions, and, infrared); with, (i) multiple joint quality indicators (penetration depth, interface width, and, top & bottom concavity); and (ii) welding process parameters (laser power, scanning speed, focal point position) which provide necessary capabilities for on-the-fly process adjustment in overlap joint configuration with consideration of part-to-part gap. 3. Process Parameters Involved During the (Remote) Laser Welding Remote laser welding process with embedded in-process monitoring capability based on photodiode ( WMG RLW Lab. ) Example of closed-loop quality control for remote laser welding process with embedded (multi) physics-driven simulation Erkan Caner Ozkat E [email protected] DLM Group, WMG Process Parameter Manufacturing System Requirements Incidence Angle Accessibility Focal Offset Laser beam quality System calibration Laser Power Intensity Weldability Investment Cost Laser Scanning Speed Cycle Time Laser Track High Speed Camera Based Monitoring 4.c. Proposed Method Physics-driven Approach 2 To collect measurable data •Process parameters (Power, Speed, Focal offset, etc.) •Process data (Temp., Plasma charge, optical emissions, etc. ) 5. Research Methodology & Results The energy balance method was proposed to calculate keyhole in one single thickness [3]. In this research, it was extended into two thicknesses which makes applicable to overlap joint. In proposed model, part-to-part gap was considered as an obstacle and the strength of the line source reduced and obtained from the experiments. Measured vs simulation key quality indicators for different part-to-part gap and scanning speed
Transcript of In-Process Monitoring of Remote Laser Welding Process ... · Welding Process Based on Decoupled...
Key Quality Indicator Cross Section Quality requirements
Penetration ≥ 0.3 × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝐿𝐿𝑇𝑇
Interface Width ≥ 0.9 × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝐿𝐿𝑇𝑇
Top Concavity ≤ 0.5 × Upper 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝐿𝐿𝑇𝑇
Bottom Concavity ≤ 0.5 × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝐿𝐿𝑇𝑇
In-Process Monitoring of Remote Laser Welding Process Based on Decoupled Multiple-physics Simulation Erkan Caner OZKAT, Pasquale FRANCIOSA, Darek CEGLAREK
2. Physics of Laser WeldingThere are two main operational modes in (remote) laser welding: keyhole and conductionmodes. The keyhole mode occurs when the laser power intensity at a given scanning speed issufficient to generate a deep narrow cavity, known as keyhole. The laser beam is absorbed byin-Bremsstrahlung thorough keyhole and Fresnel absorption at the keyhole wall. The keyhole isstabilised by the pressure from the metallic vapour (plasma) being generated and responsiblefor the quality of the weld. Therefore, choosing robust process parameters is crucial. In thisresearch, the selection of process parameters is based on the manufacturing requirements [1].
Penetration
Interface Width
TopConcavity
BottomConcavity
4.a. Monitoring Expectations
6. Research ImpactCurrent practice is mainly based on post process inspection which is time consuming andcostly. This research offers the capability for in-process monitoring of multiple joint qualityindicators and linking them to welding process parameters. The results of the research can beexploited on a broader spectrum and integrated as a closed-loop quality control strategy whichhelps to eliminate, reduce and correct defects before they occur which will lead to increasedproductivity and product quality.
4.b. Co-axial In-process Monitoring Methods
PhotodiodeBased Monitoring
Data-driven Approaches
The existing (photodiode- and high speed camera-based) monitoring methods extract patterns from data gathered in real-time and correlate them only to a single joint key quality indicator. The lackof a comprehensive correlation between gathered data and multiple joint quality indicators underscores the limitations of current methods towards delivering automatic closed loop quality controlsystem, with capabilities for adjustment on-the-fly.
(1) Ozkat, E. C. et al. (2016), Process Parameters Selection for Laser Dimpling Process based upon Nested Hetero-Skedasticity Modelling and Reliable Parameter Space, submitted to JIM(2) Ozkat, E. C. et al. (2016), In-Process Monitoring of Remote Laser Welding Process Based on Decoupled Multiple-physics Simulation, submitted to ICALEO Conference 2016(3) Kaplan, A. F. H. (1994), A Model of Deep Penetration Laser Welding Based on Calculation of the Keyhole Profile, Journal of Physics D: Applied Physics, 27, 1805–1814
1. Motivation & ObjectiveA leading challenge preventing the systematic uptake of Remote Laser Welding (RLW) is thelack of efficient in-process monitoring and control to achieve and guarantee high quality weldunder the process variability. Current solutions for in-process monitoring are data-driven,implying that predictive models are trained on gathered data using secondary information(plasma intensity, 2D images, etc.) and cannot be fully exploited outside of the training dataset. As a consequence, changes in welding process parameters, such as laser power, weldingspeed, focal offset, and material thickness can be only handled by re-building, case-by-case,the predictive models.The objective of this research is to develop a novel scalable physics-driven model linking in-process monitoring data (plasma charge, acoustic, optical emissions, and, infrared); with, (i)multiple joint quality indicators (penetration depth, interface width, and, top & bottomconcavity); and (ii) welding process parameters (laser power, scanning speed, focal pointposition) which provide necessary capabilities for on-the-fly process adjustment in overlapjoint configuration with consideration of part-to-part gap.
3. Process Parameters Involved During the (Remote) Laser Welding
Remote laser welding processwith embedded in-processmonitoring capability based onphotodiode (WMG RLW Lab.)
Example of closed-loop quality control for remote laser welding process with embedded (multi) physics-driven simulation
Erkan Caner [email protected] Group, WMG
Process Parameter
Manufacturing SystemRequirements
Incidence Angle 𝜶𝜶 Accessibility
Focal Offset 𝑭𝑭𝑶𝑶Laser beam qualitySystem calibration
Laser Power Intensity 𝑷𝑷𝑰𝑰
WeldabilityInvestment Cost
Laser Scanning Speed 𝑺𝑺𝒔𝒔 Cycle Time
Laser Track 𝑳𝑳𝑻𝑻
High Speed Camera Based Monitoring
4.c. Proposed MethodPhysics-driven Approach2
To collect measurable data•Process parameters (Power, Speed, Focal offset, etc.)•Process data(Temp., Plasma charge, optical emissions, etc. )
5. Research Methodology & ResultsThe energy balance method was proposed to calculate keyholein one single thickness [3]. In this research, it was extended intotwo thicknesses which makes applicable to overlap joint. Inproposed model, part-to-part gap was considered as anobstacle and the strength of the line source reduced andobtained from the experiments.
Measured vs simulation key quality indicators for different part-to-part gap and scanning speed
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