AI for Robotic Welding Path Planning & Quality…

AI for Robotic Welding Path Planning & Quality Assurance

Optimize weld paths, parameters, and QC with AI to reduce defects, spatter, and cycle time in robotic welding operations.

Poor weld paths and parameters cause porosity, undercut, and rework. AI-driven planning and monitoring improve penetration, reduce spatter, and accelerate commissioning.

Travel speed and weaving patterns tuned for penetration.

Adaptive control for heat buildup and distortion.

Arc/acoustic/vision monitoring for defect signatures.

live demo · related simulation● LIVE

Robot kinematics, collisions, reachability; fixture states.

CAD to path conversion; joint library; fixture metadata.

Process telemetry and synchronized sensors (arc/vision/acoustic).

Frequently asked questions

What does the Operator UI display, including live quality scores and alarms?

The Operator UI provides real-time performance metrics for your simulation runs, showing key indicators like quality scores alongside alerts if issues arise. This allows you to quickly identify and address problems during the simulation process.

How can I generate reports and traces for quality assurance and customer documentation?

mysimulator.uk allows you to create detailed reports and trace logs of your simulations, which are invaluable for documenting the results and demonstrating the fidelity of the model to stakeholders or clients. These records capture all relevant data points during the simulation execution.

Can I import CAD/joint libraries to generate baseline paths and simulate collisions?

Yes, you can import CAD models and joint libraries directly into mysimulator.uk; this enables the system to automatically generate initial path predictions and accurately simulate potential collisions between objects within your virtual environment. This greatly simplifies the setup for complex physical interactions.

How do I collect synchronized telemetry and quality labels for training defect/parameter models?

mysimulator.uk facilitates the collection of synchronized telemetry data – sensor readings and other performance metrics – alongside corresponding quality labels, which you can then use to train machine learning models that identify defects or optimize simulation parameters based on observed behavior.

▶ Try it live

Everything above runs in your browser — open Inverse Kinematics (FABRIK) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Inverse Kinematics (FABRIK) simulation