Crashworthiness is an issue that should be considered when designing a passenger vehicle to ensure the occupants' safety in a vehicle accident. Many governments and insurance companies around the world suggest conditions relating to passenger safety in designing vehicles, and regulations that include the conditions have been utilized. The suggested regulations reflect the crashworthiness of structures in order to consider passenger safety. Therefore, these conditions should be used as objective functions or constraints when optimizing a vehicle structure. However, it is difficult to apply gradient-based optimization methods to crash optimization problems because of the large non-linearities of the problems which should be considered in the time domain. The non-linearities and oscillation of the responses make it difficult to calculate the sensitivity information. Therefore, a design method regarding the crash optimization problem needs to be developed. A crash problem should consider the crashworthiness of the vehicle. That is, a design problem should be solved regarding the crash energy conveyed from the outside and injuries to the human body. In the present research, a crashworthiness design optimization method using equivalent static loads that considers the strain energy and injuries of the human body is proposed. The equivalent static loads method for non-linear static response structural optimization (ESLSO) is modified to handle responses imposed on the strain energy and the head injury criterion (HIC) responses. The proposed ESLSO is verified through three practical examples. Design optimization of a crash box and a knee bolster are carried out to maximize absorbed impact energy, and size optimization of a frontal structure of a simplified vehicle is performed to reduce head injury. In verifying the proposed method, traditional optimization methods such as the response surface method are used. The excellence and usefulness of the proposed crashworthiness optimization method are proved by successfully applying it to a crash problem and improving the crashworthiness of the vehicle.

The complexity of engineering systems is rapidly increasing because the number of components has increased and various engineering disciplines are involved. According to this trend, large-scale engineering systems are designed by multiple design teams with many designers of various disciplines. Although the design process by the design teams is a great deal similar to the design process by an individual designer, there is an important difference between them. Designing a large-scale engineering system with design teams can cause potential conflict among the subsystems because each team may design a subsystem without considering the other team’s subsystems. In this article, a collaborative design process is proposed to design a large-scale engineering system efficiently without the conflict among the subsystems using the Independence Axiom of axiomatic design. The proposed process uses a zigzagging process between the functional domain and the physical domain, and the online electric vehicle (OLEV) is designed by the proposed process. The OLEV is an electric vehicle which uses electric power transmitted wirelessly from the power source buried in the road. The functional requirements (FRs) and constraints of the OLEV are specified to clarify the design objectives and specifications. The prototype, which is designed by the design teams based on the defined FRs, is evaluated using the proposed process. It is found that the proposed process can lead design teams to design a product more efficiently without unnecessary iterations.

Anthropomorphic test devices (ATDs) are used to predict the human injury risk in a crash test. The Hybrid III crash test dummy is a standard ATD used for measuring the occupant safety in a frontal impact test. Since a real crash test using a vehicle is expensive, computer simulation using the finite element method (FEM) is widely used. Therefore, a detailed and robust finite element (FE) dummy model is required to acquire more precise occupant injury data and more accurate behaviour of a dummy during the crash. This research proposes the process of modelling and validation of an FE model of a Hybrid III crash test dummy, and a high-fidelity FE model of the Hybrid III dummy is developed on the basis of the proposed process. The proposed process consists of two phases. First, the FE model is constructed on the basis of the reverse engineering technique. Second, the material identification process is performed for validation of the constructed FE model of the Hybrid III crash test dummy. A certification test for each part of the physical dummy model and also computer simulation of the constructed FE model are performed. The material identification process is carried out by an optimization process to minimize the difference between the physical test results and the computer simulation results. The utilized optimization algorithm is the successive response surface method (SRSM).

The automobile seat must satisfy various safety regulations for the passenger's safety. In many design practices, each component for an automobile seat is independently designed by concentrating on a single regulation. However, since multiple regulations must be considered in a seat design, there may be a design confliction among the various safety regulations. Therefore, a new design methodology is required for effective automobile seat design. The axiomatic approach is employed to consider multiple regulations. The independence axiom is used to define the overall flow of the seat design. Functional requirements (FRs) are defined by safety regulations, and components of the seat are classified into groups which yield design parameters (DPs). The classification is carried out to keep the independence in the FR-DP relationship. Components in the DP group are determined by using design of experiment (DOE) orthogonal arrays. Numerical analyses are utilized to evaluate the safety levels by using a commercial software system for non-linear transient finite element analysis.