optical inspection in tool industry and...

Optical Inspection in Tool Industry

and Manufacturing

Dr. Daniel Garten1, Dipl.-Phys. Heinz-Wolfgang Lahmann1

and Dr. Katharina Anding2

1) GFE – Society for Production Engineering and

Development

Näherstiller Straße 10, D-98574 Schmalkalden

[email protected]

URL: http://www.gfe-net.de

2) Ilmenau University of Technology

Department for Quality Assurance and

Industrial Image Processing

Gustav-Kirchhoff-Platz 2, D-98693 Ilmenau

[email protected]

URL: http://www.tu-ilmenau.de/qualitaetssicherung

26th March 2014 • Dr.-Ing. Daniel Garten • © GFE Schmalkalden e.V. 2 STEINBEISS SpectroNet Collaboration Forum

Outline

1 3-D Inspection of grinding tools

2 2-D Inspection of grinding tools

3 Intelligent methods for 2-D surface inspection

4 Optical detection of production defects at pet-bottles


Problems of optical inspection in tool industry and manufacturing

- complex objects with a high range of variation

- high variation of surface and optical reflectance characteristics

- rough surrounding conditions (dust, vibrations, foreign light)

- increasing requirements regarding testing time, accuracy and repeatability

- short cycle time

- often 100-%-monitoring required

- . . .

robust image acquisition and image processing algorithms are needed


Shape-from-Focus with colour information System OMG3

3-D-model

3-D Inspection of grinding tools

Method to measure the highly inhomogenious grinding surface


3D point cloud for estimating of wear determining parameters

Image acquisition of equally spaced

surface views

Calculation of the contrast value for

every pixel to evaluate the local

sharpness

Find the maxima of the focus function

for every pixel to calculate the height

of the surface


Algorithm overview



Grain edges distribution

N

n

SnS zN

K1

*1

galvanic bond

metal carrier

galvanic bond

metal carrier

Grain protrusion

N

n

BnB zN

K1

*1

Parameters to characterize wear on grinding tools



Grinding surface

Aim: Count the abrasive grain and determine its area in the image field of the camera

Found abrasive grain with its calculated area

Gain: 1. Estimate the accuracy of the sieving process,

2. Evaluate homogenity of the spatial grain distribution



Algorithm overview

1. Calibrate the image acquisition system with a micro-structure of known geometry

(in this case a reticle)

2. Choose the red colour channel (different colour models where investigated

regarding its contrast between abrasive grains and bond level)

3. Smooth the histogram of the red channel till only two maxima appear

4. Apply region-growing to detect the connected regions of the grain

5. Fill holes in the detected regions with morphological operations

6. Count the regions and calculate its mean area and area deviation

Peak bond level

Peak abrasive grain


Intelligent methods for 2-D surface inspection

Detection of surface defects by 3-D-measurement

Example: cone-shaped counterbores aquired with Alicona Infinite Focus SL, after form removal

good surface quality, furrows

only in machining direction bad surface quality, chatter marks

Is it possible to determine surface defects only from

fast and cheap acquirable 2-D texture and color features?


Intelligent methods for 2-D surface inspection

(currently under development)

Methods for surface inspection based on 2-D texture analysis and machine learning


Optical detection of production defects at pet-bottles

Aim: Detect water-rings and bubbles at pet-bottles

Idea for solution:

- use a transmitted light setup with an horizontal line structure

- at waterrings or bubbles optical effects cause an distortion of these lines in the image



Algorithm overview

1. Segment the horizontal black lines

2. Segment the region of the bottle

3. Detect the middle line of every segmented region of the black lines with a thinnig

operation

4. Detect endpoints and junctions

5. Count all junctions and endpoints within the region of the bottle, exclude those near

the border, near the bottom and those at the neck (winding)

6. Decision: number junctions/endpoints > 5 => not OK

number junctions/endpoints < 5 => OK

Defect region in detail


Thank you for your attention!

Contact:

Dr. Daniel Garten, Dipl.-Phys. Heinz-Wolfgang Lahmann

GFE – Society for Production Engineering and Development

Department for measuring technique and test bench building

Näherstiller Straße 10, D-98574 Schmalkalden

[email protected]

Phone: 03683/690086

URL: http://www.gfe-net.de

optical inspection in tool industry and...

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