ConductVision Enterprise
ConductVision checks hand-assembly and rework benches for gloves and goggles and counts the boards waiting between steps for electronics production and quality teams.
Electronics manufacturing demands inspection accuracy at the micron level across components that are too small and too numerous for reliable human evaluation. Solder joint quality, component placement accuracy, wire bond integrity, and surface mount defects must be verified at speeds that match automated assembly equipment. ConductVision's technology can provide high-resolution visual inspection models trained specifically for electronics manufacturing environments, including PCB assembly, semiconductor packaging, and final product verification. Cleanroom monitoring and ESD compliance tracking can provide additional operational oversight for sensitive production environments.
Outcomes
Operators at rework and hand-solder benches take gloves off for fine work and forget to put them back.
Skin oils from bare hands stay on boards and connectors through the next process steps.
Conformal coating, laser marking and cleaning stations need goggles, and operators move between stations all shift.
One supervisor cannot see which stations lose eye protection on a night shift.
Boards wait on carts and racks between reflow, inspection and test, and no one logs for how long.
Planners cannot see which step holds the boards back until orders run late.
Surface mount components are shrinking to 01005 packages (0.4 x 0.2 mm) that are invisible to unaided human inspection. Solder defects like cold joints, bridging, and insufficient wetting have distinct visual signatures but occur across thousands of joints per board. False reject rates from rule-based AOI systems waste significant operator time on phantom defects.
ConductVision can deploy deep learning inspection models that achieve higher defect detection rates with significantly lower false reject rates compared to traditional AOI rule sets. Models can adapt to new component types through transfer learning, reducing new product introduction inspection setup from weeks to days.
Endpoints
Each endpoint is defined before the pilot starts, with what is counted, its unit and how the results are broken down.
| Endpoint | How it is counted | Unit | Broken down by |
|---|---|---|---|
| PrimaryBench glove rate | Share of person-minutes at hand-assembly and rework benches with gloves worn while a board is in view. | Unit:% | By:Bench, shift |
| SecondaryBare-hand events | Moments a person handles a board without gloves past the grace period, per 100 bench-hours. | Unit:events per 100 bench-hours | By:Bench, shift |
| Eye protection rate | Share of person-minutes at goggle-required stations with goggles worn. | Unit:% | By:Station, shift |
| Boards in staging | Circuit boards counted on carts and racks in each staging area, sampled every 15 minutes. | Unit:boards (hourly mean) | By:Staging area, hour |
Each pilot measures ConductVision on your own footage against a sample checked by hand, before anyone relies on its numbers.
Glove alert precision
Share of bare-hand alerts that a reviewer confirms in a hand-checked sample of clips.
% · by bench
Board count agreement
Difference between the ConductVision board count and a reviewer's hand count on sampled stills.
boards per still (mean absolute difference) · by staging area
Gap recall
Share of glove and goggle gaps a reviewer finds in sampled footage that ConductVision also flagged.
% · by station, item
Capabilities
Boxes each unit on a production line, with defect classes trained on your parts.
Counts and tracks people in each camera view, by zone and hour.
Watches water, wildlife, vessels and smoke at rivers, wetlands and shorelines.
Tracks posture, waiting and movement from pose keypoints in public spaces.
Use cases
High-magnification cameras can evaluate solder joint quality on SMT and through-hole assemblies, detecting cold joints, bridging, tombstoning, and insufficient wetting. Deep learning models can distinguish true defects from acceptable process variation, reducing false rejects.
Post-placement cameras can verify component presence, correct orientation, polarity markings, and positional accuracy against design data. Missing or misaligned components can be flagged before reflow, preventing costly post-reflow rework.
Semiconductor packaging inspection can verify wire bond loop height, tail length, ball placement, and die attach void coverage. Defects can be classified and tracked to specific bonder heads or tool settings for process correction.
Cameras can monitor cleanroom gowning compliance, particle-generating activities, and door seal integrity. Personnel traffic patterns can be analyzed to identify contamination risk behaviors and optimize cleanroom layout and protocols.
Detection models can verify wrist strap usage, ESD smock wearing, and proper grounding practices at workstations. Compliance events can be logged for quality system auditing and correlated with field failure data for root-cause analysis.
Related
Watches stations, robots and crews across the factory floor.
Counts vehicles and tracks crews and robots in plants and yards.
Checks gloves, masks and goggles, and counts units on filling lines.
Pilot
Tell us about your sites and cameras, and a ConductVision engineer replies with a scoping plan built around the endpoints above.
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