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Weld Spot Analytics

Weld Spot Analytics

Our Mission

Weld Spot Analytics (WSA) is a software solution that helps welding engineers in taking faster, more accurate decisions, and increase weld quality while avoiding inefficiencies and reducing wastes.

 
 

Many are the challenges affecting welding operations: controllers provide their data only at a cell level, which makes it difficult to cross-correlate data from other cells in order, for example, to detect the quality trend of a part as it moves along the line. Destructive tests represent the only reliable method to identify the ground truth on whether a spot was welded according to quality specifications or not. Unfortunately this process is highly inefficient and there is no tool that can help welding engineers in deciding which part has to be sent for testing.


Until now. At its core, Weld Spot Analytics software provides easy access to all welding controllers on the shop floor. No more wasting time sifting through complicated interfaces, all the most useful information can be reached with just a couple of clicks away. Finally, some of the latest machine learning algorithms will analyze the data and help welding engineers in quickly finding their answers.


 
 

For more information, please reach out to the WSA team.

WSA Dashboard Preview

 
 
 
WSA Dashboard 1 Magnifier
WSA Dashboard 2 Magnifier
WSA Dashboard 3 Magnifier
 
 

WSA behind the scenes

 
 
 
Weld Spot Analytics is the combination of 5 main components:
1. A cloud deployed architecture specifically designed to represent welding data and capable to support machine learning tasks
2. A smart edge module that provides secure connection to the welding controllers on the shop floor and also machine learning inference close to the source of data
3. A beautiful browser-based user interface, capable of displaying a variety of data with a great look-and-feel
4. A simple but rich user experience, we believe in providing a solution that does not require a user manual
5. An artificial intelligence engine that will allow to use the latest and greatest machine learning algorithms to detect anomalies, customizing them to the end user’s specific use cases