Development of Vehicle's Body for Ride Comfort by Actual Driving Simulation

Tomoya Aou･Takayoshi Shigihara (Toyota Motor)

In terms of the ride comfort of a car, recently, a vibration phenomenon in the high frequency range, which is expressed as "texture", has been pointed out. In the high frequency range, there are many elastic resonances of the body.
In this paper, we analyzed the vibration mode of the body during actual operation using an actual driving simulation. The fundamental weaknesses of the body structure were clarified, leading to structural changes including production process changes.Improvements in "texture" were confirmed in the actual vehicle.

Multidisciplinary Design Method for Off-road Vehicles using Differential Evolution Based Adaptive Sampling

Hiroaki Kawamura･Kazuki Tsuda･Tomotaka Sugai･Kohei Shintani･Koji Nishikawa (Toyota Motor)

The suspension characteristics that enhance ride vomfort may conflict with the abillity ot absorb the substantial inpt loads from the road surface, especially when traversing rough terrains in off-road vehicles, such as overcoming rocks and other obstacles. To achieve optimal performace across various indices, we have traditionally relied on trial-and-error methods to find the optimal combinations of design variables. In this paper, we introduce a novel data-driven set-based design approach, employing the differential evolution method．

Development of Automatic Hood Mastic Sealer Arrangement Technology Considering Manufacturing Constraints and Performance Requirement

Noriko Ohtsuka･Hiroaki Onodera･Mashio Taniguchi･Masatake Kimura (Toyota Motor)

Currently, to meet tension stiffness target of the hood, CAE is used repeatedly to determine suitable mastic sealer arrangement. Several complex constraints and combination of the constraints needed for manufacturing require numerous cases of simulation. In this research, we developed a design technology that can rapidly generate multiple arrangement patterns using machine learning. Using this technology, it is expected to reduce simulation costs which will improve the efficiency of the hood development process.

Development of Prediction Method for FDS (Flow Drilling Screw) Joint Rupture Phenomenon using Machine Learning

Kojiro Chujo･Hiroshi Sato･Yoshiteru Ishibashi･Hisashi Ihara･Hisahide Matsuoka (Nissan Motor)

FDS joint is increasingly being used because it can be used to joint aluminum materials to each other and to joint different materials such as aluminum and steel materials.
In this report, we report a case study in which a machine learning model was created based on the result of test piece for the purpose of reproducing the collision phenomenon in CAE, and the accuracy was verified for estimating the breaking load and failure mode.

Development of CAE Models of Arc Welds Including Aluminum Casting

Yuri Ichikawa･Kazushi Urakawa･Eita Niisato･Hiroaki Kawamura･Toshiyuki Isono (Toyota Motor)

Aluminum castings are used for the body frame, and aluminum arc welding is being considered as a joining method. Although it is important to understand the fatigue strength of the weld joint in fatigue strength design, there is no CAE model for arc welding that joins the casting base metal and plate base metal. In this study, a new model using a multi-point constraint function was developed, and it is shown that fatigue strength evaluation is now possible.