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106 TRƯỜNG ĐẠI HỌC SƯ PHẠM KỸ THUẬT - ĐẠI HỌC ĐÀ NẴNG

AI-BASED PROCESS OPTIMAL
CONTROL SYSTEM FOR QUALITY TRACKING

AND PREDICTION

1*
2
Song-Hee Park , Young-Se Lee , Young-Hyung Kim
1
1 Department of Electronic Engineering, Kumoh National Instityte of Technolog, Gumi,Korea; psh0203@kumoh.ac.kr
2 AJUSTEEL, Gumi, Korea; young@ajusteel.co.kr
*Young-Hyung Kim (email: kic126@kumoh.ac.kr)
Abstract: The AI-based process optimal control system methods for a smart factory applying an AI-based process
is a control system technology that combines next- optimization control system.
generation new technologies such as autonomous and semi- Keywords: Quality Management System; Predictive
autonomous control algorithms, intelligent HMI, sensors, management; Artificial intelligence; Control system;
and networks. In this paper, we discuss efficient operation Quality tracking.
plans and quality tracking and predictive management
1. INTRODUCTION system can be built in the facility as shown below, and
There are many ways to run a factory efficiently. the following effects can be achieved.
If you apply an AI appearance inspection algorithm 2.1. Build an intelligent manufacturing environment
that applies deep learning to the quality inspection for self-diagnosis and improvement
process, the equipment can detect product defects on It detects the causal process that causes defects by
its own without a human inspection, and if you build
a process monitoring system in the equipment, it can reflecting the sequential manufacturing process
notify the user in advance when it detects an situation, visualizes it, and presents explainable
abnormality in the equipment. Accidents and defects process management guidelines. Through
can be prevented. Efficient management is possible visualization, equipment can express the relationship
because the process flow can be viewed in real time. between parameters collected in real time through a
In this paper, we discuss ways to build a smart factory network, and through this, key equipment parameters
that can increase productivity by applying the optimal for diagnosing equipment status can be determined. In
system to the quality process to remotely manage the the inspection process, a deep learning-based defect
process, reduce defect rates, and increase operating activation map is developed to identify the cause of
efficiency. If a smart factory is introduced, it can product defects, and the defect judgment text is
overcome the limitations of manual work, increase the generalized to provide insight to field workers. By
efficiency of the production system, and can be checking this, field workers recognize the presence or
applied to most factory processes, so it can be absence of abnormalities.
expected to not only secure product quality but also 2.2. Build a responsive prediction system
operate efficiently overall. applicable to various environments

2. AI-BASED PROCESS OPTIMAL We develop a monitoring method based on a
CONTROL SYSTEM process progress prediction model that can detect
Most manufacturing processes that mass produce stagnation and abnormalities in the sequential
small varieties mainly use sequential processes such as manufacturing process early. It is equipped with a
a conveyor system. This process has the advantage of deep learning model to enable early detection of the
being able to operate efficiently because the labor is time and cause of failure of equipment that performs
simple and production management is easy. However, the process. In the event of a breakdown or
due to repeated simple labor, worker fatigue is high abnormality, it notifies the user or stops the operation
and if a problem occurs in the front process, the back of the equipment to prevent defects, improving the
process cannot proceed. As a result, production yield of the process and improving the equipment's
stagnation and abnormal phenomena occur, which has performance. Improves reliability and productivity.
the disadvantage of causing serial defects. If 2.3. Optimization-based manufacturing process
production is delayed and defects occur, the yield of control to improve manufacturing efficiency
the process drops, leading to a large loss. To solve this Infer and verify control parameters based on
problem, a self-diagnosis and responsive prediction
historical data for process optimization. In order to
ISBN: 978-604-80-9779-0