Catch defects
at production speed.
Machine vision systems for inline quality inspection, defect detection, and dimensional measurement — engineered as a complete inspection station, not a camera hung above the line.
Repeatable measurement,
not subjective judgment.
Manual inspection doesn't scale. At 200 parts per minute, a human inspector is making a subjective judgment every 300 milliseconds — and they're consistent for about 20 minutes before fatigue sets in. After that, defects pass. Good parts get rejected. Quality becomes a function of who's on the line and how far into their shift they are.
Machine vision replaces subjective judgment with repeatable, quantifiable measurement. Every part inspected. Every measurement recorded. Every defect classified. At full production speed, without breaks, without fatigue, and without the Monday-morning quality variance that manual inspection guarantees.
We handle camera selection, lighting design, algorithm development, reject handling, and PLC integration — engineered as a complete inline inspection station.
Subjective judgment every 300 milliseconds. Consistent for 20 minutes, then fatigue sets in.
- —Quality varies by shift and inspector
- —No measurement data, no traceability
- —Defects escape at full production speed
Every part inspected. Every measurement recorded. Every defect classified. At full production speed.
- 100% inspection, repeatable results
- Measurement data logged with traceability
- Defects rejected automatically at line speed
Six engineering disciplines,
one complete inspection system.
Inspection System Design
Complete vision system design starting from your quality requirements — what defects you need to catch, what dimensional tolerances you need to verify, at what line speed, on what surface finish, and with what confidence level. Pass/fail criteria, measurement uncertainty, and false reject targets defined during design — not during commissioning when the production manager asks why the system is throwing out good parts.
Camera and Optics Selection
Camera selection based on resolution requirements, field of view, pixel density at the feature of interest, exposure time, and interface bandwidth. Area scan, line scan, and 3D profiling cameras selected for the application — not the catalog. Telecentric optics for dimensional measurement where perspective distortion would introduce error. Industrial-grade cameras and optics rated for your operating environment — temperature range, vibration, washdown, dust.
Lighting Design
Lighting is the single most important factor in vision system performance — and the most frequently underestimated. We design lighting geometries that maximize defect contrast: bright field for surface contamination, dark field for scratches and texture defects, backlighting for dimensional profiles, structured light for 3D surface measurement, and dome lighting for complex geometries. Lighting is prototyped and validated on actual parts before the system design is finalized.
Algorithm Development
Image processing and analysis software tailored to your inspection task. Pattern matching, edge detection, blob analysis, color analysis, OCR/OCV, barcode and data matrix reading, and machine learning-based classification. We don't default to deep learning because it's trendy — rule-based vision works for 80% of industrial inspection tasks and is easier to validate, troubleshoot, and maintain. We use ML when the defect requires it and rule-based when it doesn't.
Reject Handling and Integration
The vision system integrated into the production line with mechanical reject mechanisms — air blasts, diverter gates, pusher arms, robotic pick-off — that remove defective parts at speed without disrupting flow. PLC integration for real-time pass/fail communication, encoder-based part tracking for accurate reject timing, and SCADA connectivity for inspection statistics, reject trending, and SPC charting. Every inspection result logged with timestamp, image, measurement values, and pass/fail status.
Dimensional Measurement and Gauging
Non-contact dimensional measurement for inline or at-line verification of critical dimensions — widths, lengths, diameters, gaps, concentricity, flatness, and profile. Measurement results compared against tolerances with SPC capability — Cp, Cpk, X-bar and R trending. Sub-pixel edge detection for measurement precision beyond native pixel resolution. GR&R studies to quantify measurement system capability.
Every industry
where quality matters at speed.
If a defect can escape to a customer, a dimension can drift out of tolerance, or a label can be wrong — vision can catch it.
Packaging and Labeling
Label presence, position, and print quality verification. Date code and lot number OCR. Seal integrity, fill level, and package completeness inspection.
Automotive Components
Surface defect detection on machined, stamped, and molded parts. Dimensional verification of critical features. Assembly verification for component presence and orientation.
Food and Beverage
Foreign object detection, fill level measurement, cap and closure inspection, label verification, and color consistency. Designed for washdown environments and food safety compliance.
Pharmaceutical
Tablet and capsule inspection, blister pack verification, label and serialization verification. Systems designed for GMP environments with validation documentation.
Metal and Plastic Parts
Surface defect detection (scratches, dents, porosity, flash), dimensional measurement, and sorting by grade or quality level.
Electronics and Semiconductor
PCB inspection, solder joint analysis, component placement verification, and wafer surface inspection. High-resolution, high-speed inspection for precision manufacturing.
From inspection spec
to validated system.
Define the inspection.
What are you inspecting for? What's the defect library? What's the production speed? What's the acceptable false reject rate? We define the inspection specification with your quality team so success criteria are quantified before design begins.
Feasibility and prototyping.
Sample parts, lighting trials, and algorithm prototyping to prove the inspection is achievable before committing to a full system build. We test on your worst-case parts — the ones that are borderline, the ones with surface variation, the ones that challenge human inspectors. If it works on those, it works on everything.
System design and build.
Mechanical mounting, camera and lighting hardware, enclosure design, algorithm development, PLC integration, and reject mechanism design. System assembled and tested in our shop on sample parts before shipping.
Installation and validation.
On-site installation, alignment, calibration, and production validation. We run the system against a known set of good and defective parts to verify detection rates and false reject rates meet the specification. Performance data documented.
Ongoing optimization.
New defect types appear. Product designs change. Surface finishes vary across material lots. We provide ongoing algorithm updates, lighting adjustments, and camera recalibration to keep the system performing as production evolves.
Platforms we work with.
Platform selection is driven by your inspection requirements, not our partnerships. The right tool for the job.
Lighting first,
algorithms second.
Lighting first, algorithms second.
Most vision projects fail because the integrator picked a camera and started writing algorithms before designing the lighting. We start with the lighting geometry because it determines whether the defect is even visible to the camera. The best algorithm in the world can't detect a scratch that doesn't produce contrast in the image. We prototype lighting on your actual parts before finalizing any hardware or software decisions.
Rule-based when it works, ML when it doesn't.
We don't default to deep learning because it's fashionable. Rule-based vision is faster to deploy, easier to validate, simpler to maintain, and more predictable in production for 80% of industrial inspection tasks. We use deep learning for the 20% of applications where traditional methods genuinely can't distinguish good from bad — complex surface defects, natural-material variation, cosmetic assessment.
Integrated into the control system, not a standalone box.
Our vision systems communicate directly with PLCs over EtherNet/IP or PROFINET, feed inspection data into SCADA and historians, and trigger reject mechanisms as part of the coordinated production line. Inspection results are logged with production context — product code, lot number, timestamp, line speed — so quality data is traceable and useful for SPC.
Validated performance, not a demo.
We run formal validation using known-good and known-defective parts. Detection rate, false reject rate, and escape rate are measured against the specification we defined during design. For regulated environments, validation follows documented protocols with acceptance criteria. You get confidence in the system's real-world performance, not just a successful demo on ideal parts.
Straight answers.
It depends on the resolution required, the number of cameras, and the complexity of the algorithm. Simple presence/absence checks can run at thousands of parts per minute. High-resolution surface inspection typically operates at 50–500 parts per minute depending on part size and defect sensitivity. We define the speed requirement during specification and design the system to meet it.
For many inspection tasks, yes. For subjective assessments like 'cosmetic acceptability' where the standard varies by customer expectation, vision may augment human inspectors rather than replace them — handling the high-volume screening while humans make final calls on borderline parts. We'll be direct about which inspections can be fully automated and which benefit from a hybrid approach.
Natural variation in material (wood grain, food products, cast surfaces) is where most vision systems struggle. We address it with appropriate lighting design, robust algorithm development, and — where needed — deep learning models trained on your specific variation. The key is capturing enough representative samples during development to build a system that handles the real range, not just the ideal parts.
Calculate the cost of your current defect escapes (customer returns, rework, scrap, containment actions) and compare it to the system investment. For most applications, vision systems pay for themselves in 6–18 months through reduced scrap, eliminated rework, and avoided customer quality complaints. We can help you build the business case with your actual quality cost data.
We run a formal validation using a set of known-good and known-defective parts. Detection rate, false reject rate, and escape rate are measured against the specification we defined during design. This isn't a quick demo — it's a statistical validation on a representative sample that gives you confidence in the system's real-world performance. For regulated environments, validation follows documented protocols with acceptance criteria.
Product changes are expected. We design systems with configurable inspection recipes — camera settings, algorithm parameters, and pass/fail thresholds stored per product and loaded automatically via barcode scan or production schedule. Adding a new product typically requires capturing sample images, configuring the inspection parameters, and validating performance — a process we can support remotely or on-site.
Ready to automate inspection?
Whether it's inline defect detection, dimensional verification, or replacing a manual inspection station — tell us what you need to catch and at what speed.