White Paper – How Fiber Lasers and Artificial Intelligence Can Work Together
Artificial intelligence (AI) is being applied to a whole range of processes as the advantages of machine learning algorithms are explored across scientific and industrial processes.
Fiber lasers are just one type of tech which can be used more efficiently and effectively in combination with AI solutions. In this white paper, the different instances of fiber lasers and artificial intelligence working hand in hand which are being developed at the moment will be outlined and discussed to provide information that will broaden the understanding of those new to the concept
The use of fiber optics is nearly ubiquitous in the communications sector, meaning that while copper wire is still deployed in many scenarios, some aspect of the transmission of data will take place optically during one stage of its journey.
This can include everything from mobile phone calls to fixed line internet access, meaning that optical transmission is a very important area in which pursuing improvements to efficiency and reliability can make a significant difference on a large scale.
In the context of optical transmission, fiber lasers are deployed through the use of mode-locking to allow information to be transferred quickly, accurately and in high capacities.
Of course there is always room for optimisation, especially as instability can occur if the process is disrupted, the energy supply is interrupted or some other external influence impacts the optical transmission in a negative way.
Thankfully the power of AI can be brought to bear on this decades-old issue, with the use of machine learning algorithms allowing for fiber lasers to become self-tuning. This means that they can automatically respond to changing conditions and make adjustments which allow them to operate optimally, without the need for human intervention.
The key advantage of machine learning algorithms is that, as the name suggests, they automatically ameliorate performance over time. Once they are up and running, they will track performance and alter themselves accordingly in the event that any obvious inadequacies appear. Big data can be used to collect large amounts of information and seek out patterns that provide insights with regards to optimisation, while the always-on nature of AI means that sudden changes or clear trends will not be missed.
This again means that mode-locked fiber lasers can become a more efficient and robust option for optical transmission. As more data is accrued, additional optimisations can be made, ensuring that this is a viable long term solution.
Fiber lasers are not just relevant in optical transmission solutions but can also be harnessed for a variety of other applications. This includes things like materials processing, where fiber lasers are used for welding, cutting, additive manufacturing, marking, cleaning and even drilling.
All of these operations are computerised and at least partially automated thanks to cutting edge systems being developed. However, even with the precision that is afforded by computer-controlled fiber laser machinery in a manufacturing context, there is still room for faults to arise.
In industries like aerospace, it is particularly vital for components to be manufactured as perfectly as possible, since even tiny errors can lead to serious catastrophes further down the line. When the tolerances are so small, being able to identify issues in materials which have been processed by fiber laser equipment is vital. This allows problematic parts to be pinpointed and discarded, rather than going forwards in the production line and potentially posing a risk.
Through the use of AI, researchers are working on solutions which all for errors to be detected with greater accuracy and within smaller time frames. This makes AI especially well suited to use with fiber laser equipment in a materials processing context.
Once again, the power of machine learning algorithms in terms of pattern recognition can make a major difference here. Welds carried out using fiber lasers can be assessed to check for errors and even if high volumes of materials and components need to be scanned over extended periods, this will be straightforward with AI on tap.
Early testing has shown that machine learning not only allows for errors in laser welding to be identified but also for welds to be graded and categorised according to their quality. This means that adjustments can be made depending on the tolerances that are acceptable in different industries.
Experts recognise that at the moment, there are some limitations to take into account when it comes to combining fiber laser equipment with machine learning algorithms. In fact it is the speed of fiber laser processing that puts AI at a disadvantage, at least in certain situations. Algorithms need a lot of processing horsepower to work quickly, so if this is not available on-site it will need to be procured remotely.
Companies like Microsoft and Intel are at the forefront of developing cloud-based AI platforms which can deliver the necessary power without requiring every business user to have suitably beefy hardware in-house. This is good news since it means that there will be more capacity for machine learning algorithms to work with fiber laser solutions going forwards, fuelling the development of new products as well as their rollout to the wider marketplace.
Road safety is an international issue which is being addressed through the development of systems that allow vehicles to operate autonomously. This began with more basic systems such as automatic emergency braking and is evolving to include systems that are almost entirely autonomous. This means that vehicles can drive from A to B, navigating traffic and other obstacles, without putting passengers or other road users in harm’s way.
In order to achieve this, LiDAR (light detection and ranging) is combined with AI software in order to sense the vehicle’s surroundings from second to second, interpret this data accurately and take action accordingly.
Fiber lasers are being harnessed as part of autonomous driving solutions because they offer a number of advantages, not just in terms of accuracy but also safety. For example, fiber lasers can operate at wavelengths which do not pose a threat to humans if they come in contact with their eyes, whereas the older diode lasers used for some LiDAR systems did not adhere to this same level of safety. Meanwhile fiber lasers are also able to provide sensing capabilities over longer ranges at higher resolutions, all while requiring that the systems themselves do not require as many individual laser units.
All of this essentially means that the software which is used to interpret LiDAR data is being fed more information of a higher quality at a faster rate. As such it is possible for algorithms to detect hazards in the road ahead more quickly and thus react with less of a delay.
As with other areas of AI, this is still a very new and exciting concept and one which is likely to evolve significantly over the coming decade. It also demonstrates that fiber lasers are a good match for machine learning because their precision and accuracy goes along with repeatability, giving algorithms access to reliable, consistent data with which to draw conclusions.
It will be necessary to encourage automakers to adopt fiber laser based LiDAR systems for their own autonomous vehicles as this market progresses, or else ensure that more is done to unify and standardise the technology so that as many people as possible have access to the benefit, as has happened in previous eras with car safety systems like ABS.
Neural network creation
Fiber lasers and AI could soon work more closely together than ever before thanks to the development of photonic neural networks which harness optics hardware such as this in order to mirror the way that biological brains operate.
Neural networks based on this type of tech are being researched at the moment, with the idea being that materials which alter their behaviours depending on the wavelength of the light they are exposed to can stand in for synapses that exist in biological networks. Because fiber lasers can be controlled to emit light across different wavelengths with characteristic precision, it makes sense that they could one day find a home in such a setup,
Ultimately it may be possible for photonic neural networks made in this way will serve the function of helping machine learning algorithms and entire AI ecosystems to operate effectively.
Relying solely on silicon-based hardware to replicate the function of an entire biological brain is incredibly challenging in its own right, hence the need for finding different approaches such as this.
The networks described here may be some time away from moving from theoretical concepts to tangible, functional products. However, the mere fact that this is being considered should be enough to show just how diverse fiber laser technology can be.
Planning out maintenance schedules efficiently is important, since the disruption caused by necessary downtime is a cost that businesses have to bear and will want to minimise. Thankfully, AI can be applied to this taxing task because of the flexibility and adaptability of machine learning algorithms. The aforementioned data-delving that can be done, using both historic information and details relayed in real time by integrated sensors onboard machinery, means that there is no need to rely on guesswork when maintaining kit. Instead, AI can tell you exactly when it makes the most sense to carry out repairs or replacements, all while making sure that this does not get in the way of important production runs.
Rather than rolling the dice, relying on intuition or spending a lot of time with a trial and error approach, AI can assist. The scan of the faulty component can be analysed and compared against hundreds, thousands or millions of other examples, allowing algorithms to reverse-engineer the issue and give operators a good idea of what is going wrong within the equipment.
Why fiber lasers and AI work well together
Fiber lasers are well equipped to emerge as a vital technology used alongside AI because they are a much better bridge between the digital world and the analogue reality which humans inhabit.
It makes more sense to use AI with a fiber laser than, say, a mechanical milling machine because a laser’s beam suffers from none of the physical constraints of a cutting bit or other direct-contact hardware. Indeed it is this non-contact approach which gives fiber lasers the edge in several scenarios, including where AI is involved. At the moment, the obstacles facing the use of AI in industrial settings can be overwhelming, yet the perks innate to the way that fiber lasers operate provides a way forwards.
As we have discussed, whether used as a means of capturing data in autonomous driving, sending data in optical transmission or acting upon data to process materials efficiently, fiber lasers and AI are ideal partners.
Of course it is also worth noting that a lot of these systems and solutions are still in the early stages of development, meaning that it will be some time before their widespread availability arrives. Even so, the idea behind AI is that it can build momentum over time, learn from mistakes, get better at what it does and, most importantly of all, speed up as it acquires knowledge. This suggests that the future will see implementation on a larger scale, not just where fiber lasers are concerned but in every other technological niche.
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