18-01-2013, 02:41 PM
QUALITY CONTROL FOR SPRINGS DURING THE PRODUCTION PROCESS BASED ON IMAGE PROCESSING TECHNOLOGIES
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ABSTRACT
The stability of the production process and whose control
technologies, is one of the major topics for the quality of
the product. Therefore fast measurement technique plays
an important role. One of the best ways, depending on
the application and the required accuracy, is an inline
measurement system. The optical quality control of
springs is one of many applications for inline
measurement. For the quality control of springs fast twodimensional
image processing technologies are well
suited. In combination with the methods of quality
assurance a powerful inline measurement is possible. In
the application presented in this paper for a production
process of springs a small quality control loop is used.
The novel approach in this application is that the ranges
of the quality control chart directly controls the spring
forming process. Therefore an upper warning limit and a
lower warning limit were defined. Also an upper action
limit and a lower action limit were to be used for the
control of the process.
INTRODUCTION
The optical quality control of springs is one of many
applications for inline measurement. Springs are used in
a wide range of technical applications. For example a
private car contains on the average more than 8000
springs. For the quality control of springs image
processing technologies are well suitable, since all
nominal dimensions are in two dimensions detectable.
Beyond that the image processing offers the advantage to
capture all measuring points with a single picture
acquisition. Machine-integrated quality techniques in the
work cycle make 100% inspection possible to quality
control. The present paper describes machine-integrated
quality techniques with image processing technologies
for the quality control of springs.
QUALITY CONTROL LOOPS FOR
PRODUCTION CONTROL
Quality assurance means to manufacture material and
immaterial products and processes in specified quality, to
control and constantly improve processes in specified
quality [1, 2, 3]. In analogy to technical control loops
therefore quality control loops with Quality Controlled
System, Quality Controller and Quality Control Element
must be developed.
Quality control loops (Fig. 1) are defined as closed
technological-organizational action sequence in a process
for the production of a quality product. With view on the
whole production process, quality control loops must be
divided into small quality control loops and large quality
control loops [1, 4].
STATISTICAL PROCESS CONTROL WITH
QUALITY CONTROL CHARTS
Statistic process control is a continuous accompanying
monitoring of the manufacturing processes by collection
of all characteristic numbers relevant for the product
quality. SPC supplies the base data for the recognition of
weak points and thus the condition for the constant
improvement of the respective processes. SPC developed
from the quality control charts technique.
Quality control charts are one of the oldest tools in the
quality management and an important aid to the quality
control. The quality control chart is a form for
graphically representing of measured values taken up by
sequential samples. They are used for the purpose of the
quality control in comparison with warning and/or
control limits [5]. The main objective is to recognize
promptly error developments for regulative intervention.
Examples of quality control charts are shewhart quality
control chart, average value quality control chart and
median quality control chart.
IMAGE PROCESSING HARDWARE
As mentioned the image processing system works as the
feedback control system. The user defines in the
application software the set points like diameter and
length. Furthermore a maximum speed of up to 600
springs per minute must be analyzed in the fastest
machine.
The image processing hardware is structured in three
parts: the charge coupled device (CCD) - camera, the
illumination with illumination controller and, in this
special case, also the communication cable plays an
important role.
The CCD - camera
Within the machine housing the camera system was
integrated. The camera is based on a scalable hardware
platform. It is divided in image acquisition board, power
supply board and the data transfer board.
Task of the image acquisition board is the timing of the
CCD sensor, exposure control, gain control and the
analogue to digital conversion (ADC). The digital output
from the ADC is presented on an 8-bit wide data bus
interface and the image synchronous signals. Beyond that
a bi-directional serial bus is used for parameterization
and control.
CONCLUSIONS
Industrial image processing in combination of the small
quality loop is a powerful method to ensure a good
spring quality in the production process. As machineintegrated
measuring instrument instantaneously
influence on the production process can be exerted, if
variations in quality arise. Quality control chats are used
thereby as quality controllers and secure beyond that a
good documentation of the measured values. In the
presented application this function is extended
additionally by operator supporting algorithms.
Automatic spring recognition and the dynamic AOI help
to teach-in new production processes fast and ensure a
continuous quality assurance. After image analysis,
measurement and evaluation of the quality control chart,
the requirement for a 100 % inspection of 600 springs per
minute can be fulfilled.