Additive Manufacturing (AM) is revolutionizing how we design and produce components, offering unmatched geometric freedom, material efficiency, and on-demand fabrication. Its applications span from aerospace to biomedical engineering. However, widespread industrial adoption still faces obstacles such as:

• Ensuring process repeatability and mechanical consistency

• Achieving real-time quality assurance

• Optimizing build parameters and support strategies

These issues present a clear opportunity for innovation in monitoring, control, and predictive process management.

At SMARC, we are actively crafting an AI-IoT framework tailored for next-generation additive systems. Our goal is to enable intelligent sensing, predictive analytics, and adaptive control through scalable, cost-effective technologies. Rather than relying on traditional infrastructure-heavy solutions, we aim to build tools that bring insight and stability to AM environments, bridging lab-scale innovation with real-world constraints.

This initiative is currently in its conceptual design phase, driven by SMARC’s commitment to building practical, research-aligned solutions.

If you are interested in learning more about our AI-driven additive system or discussing potential applications, please don’t hesitate to reach out.

 

At SMARC, we are designing an advanced solution that integrates AI models, lightweight sensing, and simulation-driven feedback loops to develop adaptive laser control strategies. The objective is to enhance responsiveness, improve consistency, and reduce energy inefficiencies across a broad range of operating conditions.

Though currently in the research and prototyping phase, this initiative reflects SMARC’s broader mission: to deliver scalable, intelligent, and industry-ready solutions without the weight of expensive legacy systems.

If you are interested in learning more about our AI-integrated laser manufacturing solutions or discussing potential applications, please don’t hesitate to reach out.

Preventive maintenance has long been the backbone of reliability engineering, aiming to reduce equipment failures and unplanned downtime. However, traditional calendar-based approaches often lead to over-maintenance, unexpected breakdowns, and inefficient use of resources.

Today’s manufacturers are facing critical challenges:

• Lack of real-time health visibility for legacy or cost-sensitive equipment

• Over-reliance on operator intuition, especially in high-mix environments

• Limited integration between maintenance routines and production analytics

These issues increase both operational risk and maintenance cost.

At SMARC, we are developing AI-powered preventive maintenance frameworks that combine lightweight data acquisition, signal interpretation, and behavioral pattern recognition to detect early signs of machine degradation. Our focus is on scalable intelligence, making predictive insights accessible without overhauling existing infrastructure.

We are currently validating models across multiple domains, from machining to molding environments, always guided by the principles of cost-efficiency, robustness, and industrial interpretability.

If you are exploring smart maintenance strategies or seeking to reduce downtime through intelligent insights, please don’t hesitate to reach out.