Test engineering
- Measurement definition and test-method design
- Fixture and handling design for repeatable presentation
- Instrumentation, sensors, and test electronics selection
- Sequenced test execution with limits management
Engineering solution
Test Systems & Machine Vision
We engineer automated test and inspection systems: end-of-line testers, functional test stands, leak and electrical test stations, and machine-vision inspection for defects, measurement, and verification. Scope covers the measurement approach, fixtures, electronics and controls, software with pass/fail logic, and traceable results storage.
Why this solution exists
A test system embodies a quality decision: what 'good' means, how it is measured, and what happens to the data. Weak test engineering shows up as escapes to customers, false rejects that bleed yield, and results nobody can trace when a claim arrives.
We design from the measurement backward — gauge capability, fixturing repeatability, lighting and optics for vision — then build the automation and software around a defensible measurement. Results land in a database with the traceability your customers or auditors expect.
This is a strong fit when
- Manufacturers adding end-of-line or in-process testing
- Products that need documented test results per serial number
- Visual inspection tasks straining human consistency
- Replacing subjective checks with measurable criteria
Scope
What the work includes
Vision and data
- Camera, optics, and lighting engineering — the part that makes or breaks vision
- Rule-based and, where justified, learning-based inspection tools
- Pass/fail logic, reject handling, and machine integration
- Serial-number-linked result storage, reporting, and export
Required inputs and deliverables
What we need from you
- Product samples spanning good, marginal, and defective conditions
- Specification limits and current inspection criteria
- Cycle-time and volume expectations
- Traceability requirements from customers or standards
What you receive
- Test specification and method documentation
- Built and programmed test or inspection station
- Capability evidence appropriate to the measurement
- Results database or integration with your systems
- FAT records, manuals, and training
Project process
How the engagement runs
- STEP 1
Define the measurement
Agree what is measured, the limits, and the evidence needed to trust the method.
- STEP 2
Prove feasibility
Bench-test the measurement or vision approach on real sample sets before full build.
- STEP 3
Engineer the station
Design fixtures, electrical, controls, and software around the proven method.
- STEP 4
Build & validate
Assemble, program, and demonstrate repeatability on production samples.
- STEP 5
Deploy
Install, integrate reject and data flows, and train the team.
Measurement approach by defect type
| What you need to detect | Approach that usually fits | Where vision is the wrong tool |
|---|---|---|
| Surface defects — scratches, contamination | Vision with controlled, purpose-designed lighting | — |
| Presence, absence or count | Vision, or a single sensor | A photo-eye is often cheaper and more reliable |
| Dimensions to tight tolerance | Contact gauging or laser measurement | Vision resolution rarely holds the tolerance claimed for it |
| Internal defects | X-ray or ultrasonic inspection | Vision cannot see inside the part |
| Colour and print verification | Vision with calibrated colour and fixed lighting | — |
| Torque, force or leakage | An instrumented test fixture | Vision measures appearance, not physics |
Risks and limitations we state up front
- Vision feasibility depends on physics — contrast, optics, presentation; we bench-prove applications before committing to full builds.
- Sample sets that omit real-world defect variety produce testers that pass FAT and fail in production; we insist on representative samples.
- Learning-based inspection needs governance: labeled data, retraining rules, and clear limits are part of the design, not afterthoughts.
What drives cost and schedule
We do not publish fixed prices: these are the factors that determine effort. A scoped proposal follows the technical fit review.
- Number and difficulty of measurements or inspections
- Cycle time and automation level (manual load versus fully automatic)
- Traceability and database integration depth
- Sample availability and validation rigor required
What this engagement is not
Lighting and fixturing decide whether an inspection system works; the camera rarely does. Where a defect cannot be made reliably visible, that is reported before a system is built rather than discovered during acceptance testing.
Last reviewed by Geek Engineering senior engineering review.
Test Systems & Machine Vision: common questions
Rule-based vision or AI-based vision — which do we need?
Rule-based tools are preferred when features are geometrically definable: measurements, presence checks, readable codes. Learning-based tools earn their place on variable, organic, or cosmetic defects that defeat fixed rules. The bench feasibility phase demonstrates which approach holds up on your parts, with evidence.
Can the tester store results by serial number?
Yes — result traceability is a standard part of the scope: each unit's measurements, limits, outcome, and timestamps stored to a database, with export or direct integration to your quality or MES systems as required.
How do you handle false rejects?
False rejects are a measurement-capability problem. We address them during validation — with repeatability studies and threshold analysis on marginal samples — rather than by quietly widening limits in production. Where a marginal zone genuinely exists, the disposition path is designed explicitly.
Can you retrofit inspection into an existing line?
Usually, yes. The constraints are space, presentation, and cycle time. A retrofit study confirms mounting, lighting isolation, and integration points with the existing controls before hardware is ordered.
Continue your project research
Discuss this solution with a senior engineer
Describe your equipment, objectives, timeline, and constraints. We will confirm whether this solution fits — and say so plainly if it does not.
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