Does Arc replace LabVIEW or TestStand?
No. Arc does not replace LabVIEW or TestStand. It sits above existing test systems as an AI-native layer that helps teams connect and analyse the data those systems produce.
Tool Guide
LabVIEW and TestStand Test Data Management Guide
LabVIEW and TestStand are widely used by engineering teams building production test systems. They are flexible, powerful and well suited to complex measurement workflows.
Short answer
LabVIEW and TestStand test data management means connecting the result data generated by NI-based test systems so teams can analyse yield, failures, retests and traceability across products, stations and sites. The goal is not to replace LabVIEW or TestStand, but to make the data they produce easier to use.
Why is LabVIEW and TestStand test data hard to manage?
But managing the data generated by LabVIEW and TestStand can become difficult as products, stations and sites scale.
The issue is rarely that teams lack data. The issue is that data is often spread across TDMS files, CSV exports, XML files, SQL databases, TestStand result files, local folders and custom reporting scripts.
That makes it hard to analyse yield, failures, retests and traceability across the full production environment.
Why does LabVIEW and TestStand data become hard to manage?
Many LabVIEW and TestStand systems are built around specific products, stations or test requirements.
That is often the right decision at the start. Teams need to get tests running, validate hardware, capture measurements and support production. Over time, those systems become part of the long-term manufacturing workflow.
The challenge comes when each station or product line stores data slightly differently.
- inconsistent measurement names
- different result formats between stations
- local files stored outside a central system
- test sequence changes that are not easy to compare over time
- limited serial-level traceability
- custom scripts owned by individual engineers
- dashboards that only show part of the production picture
The result is a test environment that works operationally, but is difficult to analyse strategically.
Common LabVIEW and TestStand data sources
Production test teams may need to connect data from:
- TestStand result files
- LabVIEW-generated logs
- TDMS files
- CSV and Excel exports
- XML reports
- SQL databases
- MES exports
- repair and rework records
- RMA and customer return data
- custom Python, LabVIEW or internal scripts
Each source may contain part of the answer. The challenge is connecting those sources into a usable view.
What LabVIEW and TestStand data do teams need to analyse?
A good test data management approach should help LabVIEW and TestStand teams answer questions such as:
- Which tests fail most often?
- Which stations produce the most retests?
- Are failures linked to specific fixtures, operators or product revisions?
- Which measurements are drifting over time?
- Which false failures are creating unnecessary rework?
- Can each unit be traced through test, repair and release?
- How do results compare across sites?
These questions are difficult to answer if the data remains trapped in local files or station-specific scripts.
How can teams improve LabVIEW and TestStand test data management?
A practical approach usually starts by identifying the core data model.
Teams should standardise the key fields that make analysis possible, such as:
- serial number
- product ID
- station ID
- test sequence version
- measurement name
- test limit
- pass/fail result
- timestamp
- retest status
- repair outcome
This does not mean every test system has to be rebuilt. In many cases, the better path is to create a connected layer above existing systems.
Where can AI help with LabVIEW and TestStand test data?
AI can help engineering and quality teams query, summarise and investigate LabVIEW and TestStand data more efficiently.
For example, an engineer might ask:
- Which stations are driving the highest retest rate?
- Which failures have appeared more often since the last product revision?
- Which measurements are most commonly associated with repair actions?
- Where should we investigate first?
AI is most useful when it is grounded in structured test data and connected production context.
How Arc helps
Arc helps teams connect and analyse production test data generated across LabVIEW, TestStand and related manufacturing systems.
Arc does not replace LabVIEW or TestStand. It sits above them as an AI-native layer for manufacturing test data, helping teams move from scattered results to connected insight across yield, failures, traceability and quality.
Related resources
FAQ
What LabVIEW and TestStand data can be managed?
Teams commonly manage TestStand result files, LabVIEW logs, TDMS files, CSV and Excel exports, XML reports, SQL data, MES exports, repair records and custom script outputs.
Why is TestStand result data hard to analyse across stations?
Result data becomes hard to analyse when stations use different formats, naming conventions, sequence versions, local folders or scripts. This makes cross-station yield, failure and retest analysis difficult.
How can AI help with LabVIEW and TestStand data?
AI can help engineers query and summarise connected test data, compare stations, review recurring failures and find useful starting points for investigation when the underlying data is structured.
What fields should be standardised for LabVIEW and TestStand test data?
Useful fields include serial number, product ID, station ID, test sequence version, measurement name, test limit, pass/fail result, timestamp, retest status and repair outcome.
Review your test data workflow
If your production test data is spread across LabVIEW, TestStand, CSVs, SQL databases, spreadsheets, MES exports or repair records, Arc can help you map where the data sits and where visibility is breaking down.
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