Delivering faster anomaly detection, smarter alerts, and scalable AI-driven quality assurance for European manufacturers
IIntroduction
In today’s fast-evolving manufacturing landscape, adopting advanced technologies like artificial intelligence and robotics is no longer optional; it’s essential. For Simtera, a leading AIoT and digital manufacturing solutions provider specializing in cycle analysis, predictive maintenance, and production-quality intelligence, integrating AI into their operations was key to addressing the need to validate their Cycle Analysis Model in real manufacturing environments and upgrade it from TRL 5 to TRL 7.
Thanks to their collaboration with AI-MATTERS and the LMS team, Simtera was able to validate their solution in an industrial setting, strengthen their AI model’s robustness, and successfully reach TRL 6—accelerating the commercialization of their AI-powered SWCAS module. This is their story.
The Challenge
Every organization embarking on an AI transformation faces unique hurdles. For Simtera, the main challenge was validating their Resistance Spot Welding (RSW) Cycle Analysis Model in a real manufacturing environment and ensuring its robustness when processing high-frequency, noisy production data captured from industrial welding equipment. Additionally, they needed support in accessing a controlled testing environment, collecting and analysing real cycle-by-cycle welding data, and upgrading the technology’s TRL level from 5 to 6 through industrial-scale experimentation and expert guidance.
“Having an external company like Simtera join our work was also a first for us. It brought a fresh perspective and we were very pleased with the collaboration,” explains Jenny Leivadarou, Senior Researcher and Business Advisor at LMS.
The Solution
AI-MATTERS provided Simtera with the resources and expertise they needed to overcome these challenges. Through access to real industrial testing environments, technical guidance from domain experts, and support in collecting and analysing cycle-by-cycle welding data, the organization was able to validate their Resistance Spot Welding (RSW) Cycle Analysis Model, refine its performance, and successfully upgrade the technology from TRL 5 to TRL 6.
The Results
The collaboration between AI-MATTERS and Simtera delivered tangible outcomes. Some of the key results include:
- Result 1: Achieved ≥7% reduction in production errors, surpassing the ≥5% target.
- Result 2: Reached ≥2% improvement in factory integration readiness, supporting real-time adoption of the welding cycle analysis model.
- Result 3: Validated real-time anomaly detection in under 30 seconds with ≥90% detection accuracy, enabling earlier intervention and reducing quality risks.
- Result 4: Developed configurable operator alert rules, allowing flexible threshold-setting and improved decision-making.
- Result 5: Strengthened internal capabilities as six Simtera team members gained hands-on AIoT and welding-focused experimentation experience.
Looking Ahead
Working with AI-MATTERS has been a pivotal step for Simtera in their AI adoption journey. Moving forward, they plan to extend the validation of their Spot Welding Cycle Analysis solution in larger-scale industrial environments and collaborate with additional partners from different manufacturing sectors. This broader validation will enable the solution to progress toward higher TRL levels (7–9), ensuring that it becomes fully ready for market adoption and can be seamlessly integrated into real production lines.
Additionally, Simtera aims to enhance and commercialize the solution as part of their existing FabMetrics platform, making advanced cycle analysis and anomaly detection capabilities more accessible for SMEs, OEMs, and system integrators across Europe. Strengthening these capabilities will support manufacturers in reducing downtime, improving quality, and benefiting from early anomaly detection across diverse industrial use cases.
Conclusion
Simtera’s story demonstrates the transformative potential of AI in European manufacturing. By addressing challenges head-on and leveraging the resources of AI-MATTERS, they’ve paved the way for a future of smarter, more efficient operations.
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