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Transactions on Engineering and Computing Sciences - Vol. 13, No. 02
Publication Date: April 25, 2025
DOI:10.14738/tecs.1302.18245.
Kim, B. S., Choi, J., & Le, S. Y. (2025). A Cognitive Analysis and Life Prediction Through AI Algorithm of Control Arm Using
Manufacturing and Vehicle Driving Data. Transactions on Engineering and Computing Sciences, 13(02). 10-18.
Services for Science and Education – United Kingdom
A Cognitive Analysis and Life Prediction Through AI Algorithm of
Control Arm Using Manufacturing and Vehicle Driving Data
Byeong Sam Kim
Departement of Automotive Engineering,
Hoseo University, Asan city, Korea
Jinuk Choi
R&D Center, Induswell co. Ltd, CheonAncity, Korea
Sang Yeoul Le
R&D Center, R&D Center, Hwangsung co. Ltd
ABSTRACT
This study aims to enhance vehicle safety by predicting the life perdition of control
arms, critical suspension components. Traditional inspection methods have
limitations in accurately predicting failures, leading to unexpected accidents
occurring both before and after the vehicle's expected lifespan. The increasing
complexity of control arm manufacturing, coupled with the growing volume of
vehicle driving data and heightened competition, necessitates a more sophisticated
approach to quality and safety. This system implements autonomy and intelligence
of the production system by utilizing intelligent production system, big data, and
artificial intelligence technologies, and supports optimal decision-making in real
time. Data collection: There collected various sensor data from the production site,
system data, MES system data, etc. In data refinement, data analysis and algorithm
extraction of the Control Arm are performed, and the collected data is refined and
preprocessed to be processed into a form suitable for analysis. Database
construction: We build a relational database or NoSQL database to systematically
manage data. This study represents a crucial step towards a more proactive and
data-driven approach to vehicle safety and manufacturing. By integrating AI and big
data technologies, the automotive industry can move towards a future
characterized by minimized accidents and optimized production processes. Finally,
we derived the results of predicting and optimizing the remaining useful life
prediction of the remaining product.
Keywords: Control arm, cognitive analysis detection technique, AI data analysis,
intelligent production system, life perdition, smart factory.
INTRODUCTION
The control arm, one of the automobile parts, is one of the core components of the suspension
system and acts as a direct connection point between the front wheel assembly and the vehicle
frame. It is a component used in the suspension of the control arm, and it connects the body and
the wheels to absorb the shock of the road surface. The control arm may look simple in
appearance, but it is one of the core components that plays an important role in the overall
stability and driving performance of the vehicle. The control arm, which can be seen in almost
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Kim, B. S., Choi, J., & Le, S. Y. (2025). A Cognitive Analysis and Life Prediction Through AI Algorithm of Control Arm Using Manufacturing and Vehicle
Driving Data. Transactions on Engineering and Computing Sciences, 13(02). 10-18.
URL: http://dx.doi.org/10.14738/tecs.1302.18245
all road-driving suspension systems, is located on the front axle of each of the two front wheels.
In the manufacturing process, the suspension control arm is made of cast iron, and the same
material was used in this study. The cast iron control arm is a component that provides
strength, rigidity, and damage resistance.
Fig 1: Installing the control arm system
The present invention relates to the control arm that constitutes the suspension of an
automobile, and in particular, to a manufacturing method of a control arm manufactured by
extrusion using a material such as aluminum, and to an extruded material and a control arm
manufactured by this manufacturing method. In the manufacturing process of the present
control arm, the control arm is manufactured by an extrusion process of extruding an aluminum
billet through an extrusion die equipped with an extrusion hole along the horizontal cross- sectional shape of a control arm to form an extruded material extending in a direction matching
the vertical height direction of a control arm to be manufactured, a cutting process of cutting
the extruded material in a direction perpendicular to the vertical height direction of the control
arm to be manufactured to form a plurality of primary intermediate materials, a processing
process of forming a joining portion for joining parts to the primary intermediate materials to
form a secondary intermediate material, and an assembly process of separately manufacturing
a spring seat cup on which a seat of a coil spring to be coupled to the control arm is to be placed,
and assembling the spring seat cup and the seat to the secondary intermediate material.
CONTROL ARM SYSTEM
Control Arm Manufacturing Proces
By installing it on the control arm, which is a suspension component, we attempted to predict
the remaining lifespan due to vehicle component defects and fundamentally prevent vehicle
accidents based on this. In the past, only simple inspections and tests were conducted, but now
we need a guarantee for accelerated life accidents that occur before and after the vehicle's
durability life. To this end, we individually installed complex sensors on important vehicle parts
to prevent accidents during driving in advance through non-contact sensors that cannot
physically interact with people, such as autonomous driving.
Fig 2: Control arm adjustment device and measurement position of each sensor
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Transactions on Engineering and Computing Sciences (TECS) Vol 13, Issue 02, April - 2025
Services for Science and Education – United Kingdom
Processing of Manufacturing Generated Data
In this study, we attempted to complete the component level by conducting real-time
measurement tests on three types of data (sensor/road/vehicle) through T, a suspension parts
manufacturer, and control arm parts equipped with sensors, and obtaining measurement data.
The acquired measurement data was filtered using the PRUL algorithm technology and
completed vehicle level development through field tests at the actual road test site of the Korea
Automobile Research Institute, and composite data for five life prediction sensors were
constructed. At this time, this study was conducted using the PRUL (Prognostics of Remaining
Useful Life) sensor and system on-device. The generated data was processed. As a comparative
study of this study, the prospects after the completion of the development of the technology are
quite encouraging, such as improving vehicle safety, securing safety solutions, and extending
the life of related parts by measuring the status of vehicle driving safety parts that have a
serious impact on driver safety and vehicle operation in real time during normal driving of the
vehicle, diagnosing failures related to the durability of the parts, and transmitting and
controlling preemptive prediction information. In particular, in the past, it was limited to simple
predictions based on inspection processes and accelerated life tests, or collecting and notifying
information after failures occurred, and in the case of further improvements, it was limited to
transmitting information on maintenance consumables such as tires/wheels/oil/brakes. In this
way, after the completion of the development of the technology, the initiative in information on
vehicle maintenance management was acquired, and based on this, there were similar contents
such as identifying responsibility and efficiently managing optimal maintenance of driving
parts other than consumables.
Autonomous Driving Safety Technology System for Automobiles
A comparative study examining the future technology system aspect of autonomous vehicles
shows that risks have been continuously raised, including the fatal accident caused by Tesla
malfunction in the US, 11 minor accidents involving Google's self-driving car in 6 years, and a
traffic accident on the first day of fully autonomous driving in the US. In order to ensure safety,
a driving platform based on multi-safety technology that can minimize the risk of accidents
even if a problem occurs in the vehicle is essential. For Level 0-2 vehicles, a fail-safe system that
detects and notifies a failure and relies on manual driver action is required, and for Level 3 and
higher, a fail-operational system based on multi-safety design that overcomes the failure itself
before driver intervention is required when a failure is detected. In particular, the functional
safety standard for automotive electrical components (ISO26262) recommends multi-mode
design, the International Forum for Harmonization of Automotive Standards (UNECE/WP. 29)
is reviewing strict safety requirement standards for autonomous vehicles, and the US National
Highway Traffic Safety Administration (NHTSA) announced architecture recommendations for
functional safety of major ADAS systems, and in particular, the steering system recommended
redundancy of power, sensors, and actuators. Accordingly, GM introduced multiple motors,
multiple controllers, and multiple power supplies for the steering and braking actuators in the
Cruise AV. Furthermore, the Safety Of The Intended Functionality (SOTIF) standard (ISO/PAS
21448), unlike functional safety (ISO26262), considers cases where the intended design itself
is insufficient to secure safety and is inappropriate rather than malfunctions, failures, and
defects.