You've decided to leverage welding AI, a smart move that can drastically improve weld quality, reduce scrap and provide real-time welding videos. But what does the implementation process actually look like? This guide walks you through every stage: system hardware, AI model selection, data strategy, and a realistic deployment timeline.
Our ApproachBefore an project begins, we work closely with you to define the system's purpose and performance targets. Getting these details right from the start shapes every subsequent decision, from hardware selection to model architecture. Additional capabilities can always be layers in over time. |
Define your requirements
Successful AI integration starts with a clear problem definition. Before hardware is selected, we have to answer some foundational questions:
- Understand the welding application(s). What process are we monitoring? What are the acceptable variations? What specific problems need to be detected or measured?
- Define imaging and sensor requirements. What camera type, frame rate, resolution and lighting conditions are needed to reliably capture the data required for your target model?
- Determine data collection and annotation strategy. What volume and variety of labelled images are needed? This depends on the target application and how much process variation exists.
System hardware
A complete MeltTools AI weld monitoring system consists of four core hardware components: an AI-ready camera, an embedded Industrial PC (IPC), a controller and the cabling for connection.
AI-Ready Cameras
We currently offer three cameras optimized for AI-based weld monitoring that use a GXCC2 controller. Each is suited to different process types and imaging requirements:
- MeltView® TD25 pulsed illumination camera: Ideal for GMAW, GTAW and laser based processes; delivers crisp arc imagery through synchronized pulsed near infrared lighting.
- MeltView® HT25 thermal camera: A thermal imaging solution for monitoring heat distribution and melt pool geometry.
- MeltView® UD25 high-speed pulsed illumination camera: Captures at up to 700 fps for highly dynamic processes where fast transient events must be resolved.
MeltTools will shortly offer the TS25, which is a simplified TD25 with a single cable from the computer to the camera and a C-mount version of the TS25 for coaxial laser cameras.
Industrial PC (IPC)
Our IPC comes pre-configured for data collection and AI inference, capable of running all four model types and generating real-time overlay visualizations on the live video feed. Customers who prefer to build their own systems can integrate any of the cameras above into standard AI development environments.
AI Model Types
Our AI toolkit currently offers four model types. They can operate independently or in combination to address complex, multi-layered monitoring tasks. Listed in order of increasing development complexity:
Model Type |
Complexity |
Primary Use Case |
Classification |
Lowest | Detects true/false conditions. Ex. wire presence, arc on/off, any binary pass/fail flag |
Object Detection |
Moderate | Identifies and locates specific features in the frame. Ex. silicon islands, spatter droplets, porosity |
Segmentation |
High | Segments and monitors distinct regions of the weld scene. Ex. melt pool, joint gap, wire, joint preparation, shielding cup |
Keypoint |
Highest | Tracks geometric features over time. Ex. wire tip position, joint edge location, bead profile |
Models can be combined in a processing pipeline. For example, using a classification model to confirm arc presence before activating a segmentation model to measure melt pool dimensions. This modular approach balances performance with development cost.
Data Strategy
More data does not automatically produce a better model, but it does increase cost and time. Our recommended approach builds model performance incrementally, targeting effort where it matters most:
Initial Pilot Dataset. Begin with a focused dataset (typically ~500 images per class). Train a baseline model to validate the camera configuration, annotation workflow and training pipeline before committing to large-scale data collection.
Incremental Data Expansion. Add images progressively, prioritizing the conditions and classes where the model performs least well. Monitor accuracy gains after each addition, diminishing returns signal that data quality or model architecture, not volume, is the next lever to pull.
Validation and Hyper Parameter Tuning. Use a held-out validation set and cross validation to confirm the model generalizes unseen conditions. Tune hyper parameters and, where appropriate, experiment with alternative architectures to maximize performance without over fitting.
Data Quality Is Non-NegotiableA model trained on incomplete or poorly labelled data will appear to work during testing but fail in production. Investing in clean, well-annotated, representative data up front is the single most reliable way to ensure long-term system success. |
Implementation Timeline
The following timeline represents a typical end-to-end deployment for a new AI weld monitoring system. Exact duration's will vary depending on application complexity and the number of model types required.
Weeks 1-4 |
Hardware build and system assembly |
Week 5 |
On-site installation, camera commissioning and initial data collection |
Weeks 6-7 |
Proof-of-concept model development and internal validation |
Weeks 8-13 |
Extended data collection under diverse process conditions for advanced model training |
Weeks 14-15 |
Use acceptance testing and pilot deployment in the production environment |
Ready to Get Started?
Integrating AI into your welding process is a proven path to higher quality, better consistency and richer process data, but success depends on a structured approach. Rushing data collection or skipping validation steps will compromise the system's reliability precisely when it matters most.
MeltTools works with you from initial scoping through to live deployment, ensuring every stage is executed correctly. Whether you're monitoring a single GMAW cell or building a multi-process inspection line, we'll help you define the right system and deliver it on time.
Contact UsReach out to our team or schedule a meeting to discuss your application, get a hardware recommendation and understand what an AI weld monitoring system could look like for your operation. |