# Root-Crush Protection Design of Automotive Bodies Using Clustering and Pattern Recognition
## 1 Introduction  
International environmental regulations and fuel efficiency demands in developed countries are continually putting pressure on the automotive industry. For fuel efficiency improvement, weight-reduction technologies are relatively easy to apply and have the advantage of being applicable to both existing internal combustion engine vehicles and futuristic vehicles. Weight is a core factor that determines vehicle fuel efficiency; weight reduction is also associated with various other design issues, including structural rigidity, aerodynamic design, braking system, CO\({}_{2}\) reduction [1; 2; 3]. Because of this, the automotive industry has consistently applied weight reduction to various vehicle parts. As shown in Table 1, the body-in-white (BIW) accounts for most of the weight in a vehicle along with the power train and chassis, and it has a high potential for weight reduction [1; 4; 5]. A vehicle's BIW includes parts that require excellent durability because they must be able to protect occupants by preventing deformation from being transferred to occupant space during accidents. Among vehicle collision accidents, damage caused by head-on collisions has typically led to the most severe injuries to occupants; however, such collisions have shown a decreasing trend over time by various safety technologies. However, loss of human life owing to vehicle rollover has shown an increasing trend. Furthermore, during head-on collisions, the bumper and engine parts are deformed themselves, and the collision energy is absorbed to reduce the shock and injury experienced by the occupants. However, in the case of a rollover, external impact is transferred directly to the occupant space, which leads to fatal injuries. In particular, rollover crashes are serious safety problems for light-weight vehicles [6]. For this reason, many studies have been conducted on the optimization of roof strength in regard to a simulated vehicle rollover [7; 8; 9]. Accidents, including vehicle rollovers, have high nonlinearity and it is difficult to predict the deformation behavior of parts over time after an accident. Presently,when considering completely finished vehicles, competitive products that are durable and lightweight must be produced quickly, and this requires new optimization methods that use computer-aided engineering (CAE); this is different than what has been carried out in the past.  
Data classification and clustering, pattern recognition, and machine learning are important problems in a variety of engineering and scientific disciplines, such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing [10; 11; 12; 13; 14]. Numerous artificial intelligence techniques have been used to optimize of various problems in the fields of mechanical and automobile engineering including sensing and control of unmanned vehicles [15; 16], fault diagnosis of machineries [17; 18], and manufacturing applications [19; 20].  
This study proposed a new optimization method that addressed the limitations of existing traditional methods. We created models according to design variable changes using methods of acquiring and optimizing regression models that employ these models, but combine machine-learning concepts such as clustering and pattern recognition in addition to traditional methods. This new method was compared to traditional optimization methods to prove its quality.