Advances in process monitoring benefit laser microwelding
Coherent’s Florian Furger, Markus Danner and Roland Mayerhofer discuss how combining real-time readings from multiple sensors enables better control of high-precision welding
Accurate process monitoring is particularly critical in high-precision materials processing applications.
This is because the cost impact of scrapping or reworking a part due to processing errors is more costly the more expensive a part is, the more complex the processing step involved, or the later the error is detected in the production chain.
In addition, the probability that a part will fail to meet specifications increases as the task or specifications themselves become more challenging.
Precision laser materials processing applications, such as microwelding and microcutting, represents the ‘perfect storm’ of these conditions. This is because the tasks are invariably demanding, have a narrow process window, and the parts to which they are applied frequently have inherently high value or are safety or health related – for example in medical device manufacturing. Another example is battery production for e-mobility, in which laser processes are applied in late stages of production – after significant value has been built into the product.
This article explores how recent advances in process monitoring technology promise better results in the most demanding applications.
Process monitoring phases
Process monitoring can be applied at three distinct phases of a laser application. The first of these is pre-process inspection. At this stage, vision systems, sometimes coupled with automated pattern recognition software, are used to assess part fit, weld gap dimensions, overall part position, and even to verify that the correct parts have been supplied.
The next step is actual, real-time monitoring of the process itself. This can be accomplished in several different ways, including machine vision in the visible and infrared, and the use of pyrometers and acoustic sensors. Other analysis tools such as optical coherence tomography (OCT) can be used to assess weld depth as well.
The third phase is post-operation inspection. This can include measurement and assessment of the physical dimensions and characteristics of the laser processed area, which is usually performed using various camera and machine vision tools. Parameters assessed usually include seam width, and the identification of weld gaps or spattered material. OCT and other tools are also used to determine weld dimensions and mechanical characteristics (porosity, the presence of voids, grain size, etc.).
Real-time process monitoring
In-process monitoring is the most critical and complex of these three phases, so it’s worth examining this step in depth. Because there is so much going on during actual laser processing, numerous parameters need to be measured. Plus, data sampling rates must typically be very high, so as to be able to accurately identify any transient problems. And, the more quickly a problem can be detected, the better the opportunity to stop or correct the process before a bad part is produced.
The most important areas that must be assessed include laser operation, weld geometry, weld metallurgical characteristics, assist gas flow and material surface characteristics (contamination, scratches, etc.). All of these are somewhat interrelated.
Laser operation is measured with tools such as laser power and energy meters, and beam diagnostic cameras. These can measure and flag changes in the laser output itself, drops in delivered laser power (perhaps due to spatter on a protective window), and changes in delivered laser power density caused by focal position changes. But, these measurements are only accurately performed off-line, that is, in between process operations.
Weld geometry measurements include seam width and position, and especially a determination of whether the weld has wandered off-seam. Seam gaps or a lack of fusion must also be identified. Additionally, humping, undercut or other undesirable seam properties should be recognised. Measurement of weld depth is also critical.
Figure 1: The occurrence of a deviation in the weld seam position is identified in real-time data from the plasma, laser back reflection and temperature signals during this pulsed laser welding process
In terms of weld characteristics, cracks and porosity are key parameters. Other weld quality issues, such as spatter or cosmetic changes (discoloration, etc.), are frequently important as well.
There are several diagnostic tools in use for assessing all these factors. Various camera and machine vision systems are employed for monitoring weld joint and bead geometry, as well as performing seam tracking and checking gap size.
Measurements of plasma emissions in the visible spectrum are well-established as a good way to sense weld quality, and can provide an immediate indication if there are problems with the laser focus or shield gas. Problems with weld penetration and the presence of defects also show up in the plasma signature.
The back reflection of the infrared light spectrum is another sensitive indicator of weld pool conditions, particularly its turbulence and temperature. Process temperature is also measured directly using a pyrometer or infrared camera. These sense the thermal (infrared) emission of the melt pool, and are typically monitored at a wavelength much longer than the laser emission wavelength. Temperature measurements provide a particularly sensitive indicator of process conditions, and provide information on penetration depth, surface quality and gaps.
Finally, acoustic sensing is a highly valuable adjunct to the various optical methods just described, because it provides a view of what is happening beneath the surface (where light cannot penetrate). This is especially of interest in keyhole welding, in which much of the process occurs beneath the part surface.
Acoustic sensing has proven a sensitive and reliable real-time indicator of several weld parameters, particularly penetration depth, and void and spatter creation. Acoustic sensing is usually accomplished using either microphones on the processing head, or piezoelectric crystals contacted on the part surface itself. The piezos directly transduce mechanical vibrations into an electrical signal.
It’s a learning process
For the most part, these process measurements are relative. The main exceptions to this are monitoring of spectral lines which have fixed wavelength, or pyrometer measurements that can provide an absolute temperature reading. But, usually the acquired data, especially back reflected light and acoustic data, must be compared to a ‘fingerprint’ that has been previously established for that particular process.
Building up this fingerprint requires running a number of experiments to determine the process window and parameters that deliver a result that is within established tolerances. Usually 10 test runs constitutes a minimum for establishing the necessary baseline data.
In the past, the process window and fingerprint were determined by examining the highest and lowest signal curves for various sensor measurements from several ‘good’ process runs.
This data was then used to establish tolerance bands for the various measured parameters. These upper and lower limits could then be used to signal when violations occurred during actual processes in a go/no go fashion.
Figure 2: Plasma, laser back reflection and acoustic sensors all show that a rapidly scanned laser welding process was interrupted by the presence of surface contamination
The availability of increasingly fast and powerful microprocessors, together with efficient machine learning software algorithms, has enabled a more sophisticated approach for creating process fingerprints. Instead of simply monitoring each process sensor and flagging any of these which go outside their established limits, this method can simultaneously compare the output from various sensors.
For example, a machine learning algorithm can simultaneously compare the output from optical and acoustic sensors and use this to develop a process fingerprint based on probabilities, rather than just absolute limits. The goal of this is to identify problems that occur even when no one specific parameter is outside of its limits, and, conversely, to avoid scrapping or reworking parts in which a limit was transgressed during production, but which are in fact ‘good.’ Plus, these systems can learn continually, using the results of every process run to add to their knowledge. This further ensures that process quality improves and costs drop.
To be precise
All the various sensing tools which have been described have been around in one form or another for many years. Plus, the application of machine learning to create process monitoring systems which deliver better results, and are more adaptable and cost effective, has also been occurring over the past few years. But, the most advanced commercial process monitoring systems have all targeted high-power laser welding using continuous wave (CW) lasers.
Now, technology and products are being introduced which specifically service high-precision tasks, such as microwelding, microcutting and marking. Specialised process sensing systems are required for these applications, because the sensors used for high power applications can’t readily be adapted to the requirements of these precision tasks. And, importantly, the sensors, processing electronics and analysis software must allow sampling at a high enough speed to work properly, with pulsed lasers operating at high repetition rates.
Figures 1 and 2 demonstrate how a pulsed laser system, designed and constructed at Coherent within an internal research project for just this purpose, can accurately pinpoint weld defects on-the-fly. Specifically, the slight deviation in the weld bead, circled in figure 1, is identified in both the plasma and temperature signals.
In figure 2, a laser beam was scanned perpendicular to the direction of the weld seam. The part itself was purposefully tilted so that the laser would eventually go out of focus. In this case, signals from the plasma, laser back reflection and acoustic (microphone) sensors all indicated when this occurred.
Online process monitoring saves time and money by increasing yields, improving quality, and reducing scrap and rework. It reduces machine downtime by identifying the need for preventative maintenance early on, while also providing a better level of traceability, which is particularly important in industries such as medical device manufacturing. It’s also a key element in making processes Industry 4.0 compatible. Now, laser process head manufacturers are using machine learning algorithms to combine and analyse the data from multiple high-speed sensors to bring these benefits to the most demanding, high precision laser materials processing tasks.
Florian Furger is research & development project manager; Markus Danner is a product line manager; and Roland Mayerhofer is a product marketing manager at Coherent.
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