7/27/2019 NI-CaseStudy-cs-11336.pdf
1/71/7 www.ni.c
Incandescent Glass Inspection with Machine Vision at the Molding
Stage with NI LabVIEW
Author(s):
Sbastien Parent -AV&R Vision & Robotics
Christian Marchand - AV&R Vision and Robotics Inc.
Jean-Franois Bergeron - AV&R Vision and Robotics Inc.
Glass is the main material used in the fabrication of lighting
components. In the fabrication of reflectors and lenses, the
process requires glass, melted at a high temperature, to give
the final product shape. The lamp must be functional but also
exempt from aesthetic defects that could influence the confidence
of the end user in the product quality. In this case
study, we present a network of four inspection processors linked
to a centralized statistical analysis database performing the
quality inspection of reflectors and lenses after the
melted glass pressing.
System Architecture
When inspecting the products, our configuration divides the
products into two major families: lenses and reflectors. For each
of these families, the fabrication process is the same.
We send an incandescent glass puck to a mold to be pressed to
give the final shape to the glass piece. During the glass
preparation process and the pressing process, problems can
occur. The defects created could include foreign components
embedded in the glass (like rocks or gas bubbles), wrong part
dimensions, cracks, or locally missing material at the
outer diameter of the parts. Some of these defects, like missing
material at the circumference, can create leaks between the lens
and the reflector when they are sealed together. The
customers primary need was to avoid these kinds of defects at
the presses.
We at AV&R selected a PC-based system architecture. The
advantage of such a solution is the flexibility for future
improvements anticipated by the customer, based on the results
from the actual inspection system. We achieved this flexibility
using the National Instruments software library and toolkits. Our
entire system network consists of four inspection
computers connected via Ethernet to a fifth computer, which acts
as a central statistical analysis engine and database.
We used Prosilica GC1280 GigE Vision cameras for image
acquisition. We selected the Ethernet protocol because of its
robustness even when a highly electronic noise was emitted
by the powerful glass induction oven. The enabled communication
between the software and the cameras, and we created the image
inspection routineNI-IMAQdx driver softwareusing the NI vision
library. We manage the inspection results for the following two
purposes: 1) we send the pass/fail result using an to theNI
PCI-6514 digital I/O interface
customer-line programmable logic controller (PLC) for rejection
of defective parts, and 2) we send the inspection data via Ethernet
to the statistical database computer for monitoring.
In machine vision applications, we usually need to control all
factors, such as acquisition devices and lighting, to ensure
maximum performance. In this application, the product to
inspect generates its own light because the glass is
incandescent. As long as the glass temperature is controlled enough
to stay in the cameras dynamic range, normalization of the
image ensures enough product intensity stability to avoid
external light sources in this rough environment. But these
high-temperature conditions (as high as 160 F in summer)
necessitate protection of the acquisition equipment. We must
protect the camera with an enclosure and cool it down using a
Venturi device working with compressed air. A security
feedback device generates an alarm if the cooling system is not
working properly, avoiding any deterioration of the equipment.
Flexibility
In this application, users must consider a large quantity of
different products. Some differences between products could be as
major as the product type (reflector versus lens) or the
pattern embedded in the glass. We developed a solution that is
configurable for any type of product with any kind of pattern. The
challenge is in the generic capability of the learning
procedure to cover all product types.
Using the vast capabilities of the in the design of high-level
graphical interfaces, we developed a complex interface for
productNI LabVIEW graphical programming environment
configuration, taking into account all common features. Hence,
our system must detect and localize circular features, which are
involved in all products. We developed referencing
capabilities to anchor sequenced inspections and established
regions with different detection and acceptance criteria based on
the found features. We also had to design the learninginterface to
be a small, configurable machine vision system in and of
itself.
For the defects in the embedded patterns, we developed our
strategy to extract the defects from the normal pattern. Frequency
analysis using 2D fast Fourier transform (FFT) tools
demonstrates the capability to discriminate the information of
the defect from the background signature.
Data Monitoring
We send the inspection results to a rejection station and a
statistical processing tool to monitor the inspection performance
and evaluate the trends in system efficiency and
shrinkage. We then have some feedback in a dedicated computer,
linked via Ethernet to the four inspection stations at the presses.
We developed this functionality with the LabVIEW
. The database uses MySQL. We designed the statistical analysis
interface to show the systems efficiency and defect detail for a
user-defined timeDatabase Connectivity Toolkit
period. This functionality gives information to the user for
correlation between production events and system capability.
With this architecture, our configuration rejects the bad parts
and the inspection system ensures that all the parts continuing in
the fabrication process are compliant with the
customer quality criteria. Performing such inspection and
quality sorting directly at the press stage ensures that the
customer does not continue adding value to a bad product.
Another advantage of the inspection at this process stage is to
give some live feedback to the operators during the process tuning.
Finally, the central statistical analysis shows a
general snapshot of the overall production performance.
"Using the vast capabilities of the NI LabVIEW graphical
programming
environment in the design of high-level graphical interfaces,
we
developed a complex interface for product configuration, taking
into
account all common features. "- Sbastien Parent,AV&R Vision
& Robotics
The Challenge:
Developing a machine vision system for the lighting industry to
inspect formed, incandescent glass for production quality from
a
centralized computer for various product types (lenses and
reflectors) with the same algorithms on four different production
lines.
The Solution:
Implementing a system using a high-level learning procedure on
each production line that has a computer with cameras, and
sending the results of each line via Ethernet to a central
computer for monitoring.
A PC-Based Architecture of the Inspection
System with Four Inspection Stations and One
Statistical Analysis Station
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