As we move further along in the development of our MVP for an ML-based system in composite manufacturing we see results that verify the robustness of our models. Part of our expertise is building ML enabled systems that are data agnostic and facilitate niche applications from different industries. In this particular use case, we are predicting the overshoot value and total curing time during the resin curing process in a composite manufacturing model set to be deployed in industrial environment.

Predicted vs True values of Overshoot (degC)

In composite manufacturing, the resin curing process is used to make strong composite materials. However, predicting how long the curing process will take and how much the temperature will overshoot can be difficult. Knowing the total curing time and overshoot value beforehand helps manufacturers to optimize the curing process, reduce waste, and improve the quality of the final product. This can lead to significant cost savings and a more efficient manufacturing process. Our AI solution is aimed at deployment in an industrial environment to improve the efficiency and accuracy of the manufacturing process.

Ai and the Evolution of the small tech business paradigm

Only a decade ago it would have been difficult to see the way small and midsize business (SMBs) could keep pace or even compete with larger companies when it comes to data technology and innovation. The paradigm of the small, agile business that can leverage AI and analytics has only become dominant in the last few years enabling all SMBs,from disruptive start -ups to innovation hubs,  become very competitive in their respective industries.

The recent surge of AI advances, that have also stolen a lot of the limelight in the last year, has driven the point home even further. Applications like ai-driven chat bots and even coding facilitators, can accelerate the generated value and provide to an  SMB a broader spectrum of services and products.

However, this expansion of capabilities and the use of AI and analytics, does come with its own set of challenges, which include:

Cost of implementation. SMBs may not have the resources to develop their own AI solutions. Nevertheless, as the technology continues to evolve, a growing number of cloud-based AI solutions has become available, which can be implemented without significant upfront investment.

Cultural shift in the way SMBs approach this technology. Many SMBs are hesitant to adopt these new technologies, viewing them as costly or disruptive or “suitable for much larger companies”, running the risk of being left behind. Realisation that AI and analytics have a firm place in ANY size company and a careful and step-wise approach in adopting these technologies can be employed to mitigate this challenge.

Both of these define a high-level view of this paradigm shift, and the adaptation is like moving from checkers to chess.

The Advisory services provided by Talos Analytics help navigate these challenges and offer an accelerated way forward to this transition. The use of a state-of-the-art technology stack

along with the TCSA Advisory Suite results in a flexible end-to-end solution that is Scalable, Adaptive, Iterative and offers guidance and communication with the customer at every step with continuous feedback.

In the coming weeks we will break down how this process works and the milestones SMBs can look forward to.

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