Smart Production How Data Becomes Decisions

Automated weld inspection offers a key solution here: it not only digitizes inspection, but also makes process quality measurable and developable. Thanks to integrated AI algorithms, VIRO WSI from VITRONIC is one of the innovation drivers for this change. VIRO WSI is the integrated solution that not only collects data, but also makes it usable as a basis for decisions. The path to smart, resilient manufacturing begins with quality assurance – data-based, digitized, and increasingly AI-supported.

Documentation as a Strategic Tool for Continuous Improvement

In digitalized manufacturing, mandatory documentation becomes a productivity factor. Every weld seam inspected by VIRO WSI is automatically recorded, classified, and archived: seamlessly, standardized, and traceable. Through integration into Weldloop, this data can also be analyzed using AI, so that not only is evidence generated, but concrete optimization potential also becomes visible.

All inspections are documented in an auditable manner. The data can be statistically evaluated across plants: at the component level, seam level, or by defect type. This creates a consistent quality picture across lines, plants, and time periods. The documentation is thus more than just proof. It forms the basis for continuous process optimization and for the transition from reactive to preventive quality assurance.

Why Visual Inspection Alone Is Not Enough

Visual inspections of weld seams provide only limited guidance: they reveal that a defect has occurred, but not why, and certainly not how it can be avoided in the future. At the same time, automated inspection processes generate tens of thousands of data records every day – per weld seam, per component, per shift. This data contains valuable information about systematic weaknesses in the process: excessive energy input, uneven wire feed, incorrect torch positioning. But without context, structure, and visualization, it remains unused. Data is generated, but no insights are gained.

This is where VIRO WSI comes in, in conjunction with the Weldloop data platform. The goal is not mere documentation, but the complete transformation of inspection data into process knowledge. Only those who understand deviations and classify them correctly can take targeted action and thus bridge the gap between the error pattern and the control decision.

Integration of Inspection and Process Data for Data-Driven Optimization

The basis for data-driven process optimization is an architecture that combines inspection data and process parameters in a consistent system. This is exactly what the combination of VIRO WSI and Weldloop achieves. VIRO WSI takes care of the inline recording of the weld seam – optically, three-dimensionally, and fully automatically. The sensor technology detects even the smallest deviations from 0.1 mm. This not only results in a inspection decision, but also a complete digital twin of each seam with all relevant characteristics. Weldloop supplements this inspection data with process parameters: current curve, wire feed, seam position, arc stability, etc

The result is a uniform database that uses machine learning to identify correlations that are barely visible to the human eye. This creates a database that can be used by both local inspection centers and central quality assurance systems. Process sequences and error patterns are available in the same system—connected, analyzable, and traceable.

Detect, Analyze and Eliminate Errors

Inspection data only gains value when it leads to the right conclusions. An example: If burn-throughs are regularly detected in a series, linking with process parameters reveals recurring current spikes at those exact positions. Weldloop marks these deviations over time, compares them with defined limits, and visually displays the tolerance violations. The result: a clear recommendation to adjust the power source or torch settings.

Even before errors impact reject rates, critical seam positions, their frequency, affected components, and their distribution across shifts and time periods can be evaluated with minimal time spent. This reduces rework, saves material costs, and strengthens competitiveness—especially with high volumes such as in automotive manufacturing. Tools like heat maps, Pareto diagrams, or segment analyses identify where action is most needed and where measures will have the greatest impact.

The reverse is also possible: line managers can analyze specific defect classes and trace under what process conditions they occur. Thus, an isolated inspection decision becomes a complete analysis route: from detection to cause to targeted optimization measures. This saves time and resources while shifting quality assurance from retrospective control to proactive process management.

Industry of the Future: Future Viability Through Modular, AI-Supported Systems

The digital automation of weld seam inspection is just the beginning. The ability to not only check quality, but also to continuously ensure and proactively control it, is crucial for the future viability of industrial manufacturing. With AI-supported evaluation, VIRO WSI and Weldloop form the basis for this.

The vision: manufacturing that optimizes itself based on real-time data. Error patterns are not only analyzed, but anticipated. Process deviations are not corrected retrospectively, but prevented at an early stage before they become relevant to the product. The necessary mechanisms are already in place: powerful sensor technology, complete data acquisition, modular interface architecture, and AI-based evaluation systems.

Future software releases will gradually expand these capabilities, for example with functions for predictive quality forecasting or automated feedback into the welding process control system. The system remains updatable, backward compatible, and open for integration into existing lines and architectures.