Authors:
Patrick Trampert
;
Tobias Masiak
;
Felix Schmidt
;
Nicolas Thewes
;
Tim Kruse
;
Christian Witte
and
Georg Schneider
Affiliation:
Artificial Intelligence Lab, ZF Friedrichshafen AG, Scheer Tower II, Uni-Campus Nord, Geb. D5 2 66123 SB, Germany
Keyword(s):
Machine Learning, Artificial Intelligence, Deep Learning, Visual Quality Inspection, Defect Detection, Windowing, Multistage Learning.
Abstract:
Quality inspection based on optical systems is often limited by the ability of conventional image processing pipelines. Moreover, setting up such a system in production must be tailored towards specific tasks, which is a very tedious, time-consuming, and expensive work that is rarely transferable to different inspection problems. We present a configurable multi-stage system for Visual Quality Inspection (VQI) based on Artificial Intelligence (AI). In addition, we develop a divide-and-conquer strategy to break down complex tasks into sub-problems that are easy-to-handle with well-understood AI approaches. For data acquisition a human-machine-interface is implemented via a graphical user interface running at production side. Besides facilitated AI processing the evolved strategy leads to a knowledge digitalisation through sub-problem annotation that can be transferred to future use cases for defect detection on surfaces. We demonstrate the AI based quality inspection potential in a pro
duction use case, where we were able to reduce the false-error-rate from 16.83% to 2.80%, so that our AI workflow has already replaced the old system in a running production.
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