Automated Assistance to Defect Detection
DiotaInspect
A set of tools for automated assistance to detection
and characterization of non-conformities.
Overview
For inspection tasks including numerous points to checks and with limited traceability, operations can be very time consuming, with potential high costs and delay due to late defect detection and poor visibility on the on-going process.
As a module in DiotaPlayer, DiotaInspect performs the analysis of the data captured on the field to establish a status on the elements to control. As a collection of algorithmic operators, DiotaInspect combines a set of automatic control features to detect and characterize non-conformities.
At the end of the check, a deviation report is made immediately accessible to the controllers, illustrating for each checked item its condition and the data used to rule on its compliance.
Main features
Various control techniques: geometric, colorimetric, recognition/reading, comparative analyses
Adapted to multi-configurations and various inspection cells: cobot, manual inspection with tablet, mobile workstation…
Detection of anomalies based on the digital model
Large control class catalog: brackets, harnesses, clamps, screw heads, cables, caps, etc.
Module integrated in DiotaPlayer or in the Digital-Based Robotic suite
Compatible with various types of cameras and sensors like the Sensor One
How it works
DiotaInspect workflow
Preparation of the job card: control classes, labels, tracking model
In-process field data collection: visualization of the step-by-step instructions to manually capture data in DiotaPlayer or automatic collection of data through digital-based robotic solution
Processing of data with Diota algorithms in DiotaInspect to assess status of elements to control
Generation and export of inspection report for further analysis
Applications
Different applications and benefits depending on your context, manual or robotic.
Human context, with Digital-Assisted Operator solution
Automated assistance to defect detection for DiotaPlayer in manual process:- For production, to rapidly detect possible non-conformities early in the assembly process and prevent late reworking, ensuring “first-time-right”
- For inspection, to save time and allow alert escalation to detect potential non-conformities in case of multiplicity of control points and types
Robotic control context, with Digital-Based Robotics solution
Automated control of configuration during Digital-Based Robotic operations:- For automated inspection and documentation on complex products subject to variable configurations
- For fully automated simulation and robot trajectory planning, process execution supervision, data acquisitions, analysis and arbitration
Control classes
For inspection processes, the solution includes a set of control features that automatically search for a wide diversity of defects using a variety of methods.
Geometric controls
- Presence/absence of brackets
- Positioning of brackets
- Interferences
- Inversions
Colorimetric controls
- Detection of objects
- Presence/absence of objects (washers, lids, caps, obturators…)
- Markings
- Paint areas
Recognition- reading
- Texts (OCR)
- Series numbers, signal plates datamatrix
- Bar codes
- Symbols, markings
Comparative analysis
- Comparison by image recognition
- Training through reference data sets
- Machine learning
- Deep learning
- Image correlation
