Machine Vision Feasibility Checklist
Industrial inspection planning for food, FMCG, pharmaceutical and packaging manufacturers.
Prepare the samples, defect definitions and line conditions that make a machine vision project easier to validate, integrate and scale. A good feasibility phase turns uncertainty into concrete inspection choices before camera, lighting and software decisions are locked in.
What feasibility should prove
| Outcome | What it means for the project |
|---|---|
| Detectability | The smallest relevant defect can be made visible with the right camera angle, optics and lighting. |
| Repeatability | Normal product variation still leads to stable decisions at production speed. |
| Integration fit | Camera, trigger, software, reject signal and HMI can work inside the real machine environment. |
| Validation evidence | Samples and pass/fail criteria are strong enough to prove performance before rollout. |
The eight-step checklist
| Step | What to prepare | Why it matters |
|---|---|---|
| 1. Inspection objective | Defect, measurement, traceability or process goal | Prevents technology-first decisions |
| 2. Representative samples | Good, bad, borderline and batch variation | Reveals real-world visual spread |
| 3. Defect definitions | Appearance, location, severity and action | Aligns quality and automation teams |
| 4. Line conditions | Speed, trigger, mounting, vibration and reject timing | Keeps the design realistic |
| 5. Optical setup | Field of view, lens, lighting and camera angle | Makes defects visible before software decides |
| 6. Result workflow | Pass/fail, logging, alarms and traceability | Turns inspection into usable production data |
| 7. Validation criteria | Detection rate, false rejects, speed and uptime | Gives the project measurable proof points |
| 8. System architecture | Rule-based, AI-driven or hybrid inspection | Matches the method to the inspection problem |
Bring these inputs to the first discussion
| Input | Prepare this before feasibility |
|---|---|
| Samples | Good parts, defective parts, edge cases and realistic product variation from the line. |
| Line facts | Cycle time, product orientation, mounting space, trigger options and reject response time. |
| Quality rules | What must be rejected, what may pass, and what should be logged for traceability. |
Choose the architecture from the evidence
Use rule-based vision when the inspection can be defined by stable measurements, geometry, contrast or code readability.
Use AI-driven inspection when defect appearance is variable, subtle or hard to describe with fixed logic.
Use a hybrid architecture when deterministic measurement and flexible visual classification need to work together on the same line.
Relevant Sedeco solution areas include quality inspection, process monitoring, measurement and positioning, traceability and OCR, AI-driven inspection, and high-speed vision.
Ready to turn the checklist into an inspection concept?
Sedeco Imaging can review samples, defect definitions and line constraints to advise whether rule-based vision, AI inspection or a hybrid setup is the strongest route.
Contact Sedeco Imaging to discuss feasibility.