Discrete sizing, cross-sectional shape, topology optimization, and material selection of a framed automotive body

This study proposed a discrete structural optimization method for a framed automotive body. Up to four types of discrete design variables are considered simultaneously, that is, the sizing, cross-sectional shape, topology, and material variables. Firstly, to solve the nonconvex and nonlinear optimization problem, the original non-dominated sorting genetic algorithm, the third version (NSGA-III), is adapted. An improved extreme points identification scheme and a new mutation operator are proposed to stabilize the normalization of the population and accommodate the manufacturing constraints, respectively. Two test problems demonstrate that the modified NSGA-III can handle continuous and discontinuous multiple objective optimization. Subsequently, the classical 10-bar truss is used to illustrate the proposed method. A weight reduction of 4.5 kg is achieved as compared to previous optimal designs in the literature. Finally, a framed automotive body is optimized for maximizing the first order natural frequency and minimizing the total mass, the maximum stresses and the maximum displacements in different load cases and the manufacturing cost. The results obtained by different optimization procedures are presented and discussed. The results demonstrate the feasibility and effectiveness of the proposed method. A weight reduction of 17.59% is achieved while other structural performances satisfy the design requirements.

Material Selection Decision-Making Method for Multi-material Lightweight Automotive Body Driven by Performance

This work proposes a material selection decision-making method for multi-material lightweight body driven by performance to achieve that the right materials are used for the correct positions of the automotive body. The internal relationship between performance and mass, cross-sectional shape, wall thickness parameters, and material properties of a thin-walled structure is studied. The lightweight material indices driven by performance are then established. The lightweight material indices and material price are taken as the decision-making criteria for the material selection of automotive body components. A hybrid weighting method integrated with the analytic hierarchy process, fuzzy analytic hierarchy process, and quality function deployment is proposed. The difficulty of quantitatively evaluating the performance requirements of different components of the body is solved using the proposed weighting method combined with the numerical analytical results of the component performance under multiple operating conditions of the automotive body. Then, the weight of the decision-making criteria for material selection is calculated. Grey relational analysis is used to make multicriteria decision-making on a variety of candidate materials to select the best material for body components. After the lightweight material selection of the front longitudinal beam of the automotive body, the frontal collision safety performance of the body is effectively improved, and the mass of the front longitudinal beam is reduced by 45%. Material selection result of the front longitudinal beam indicates that the proposed material selection decision-making method can effectively achieve the fast material selection of components in different positions of the body.

Verification and validation of an automated robot inspection cell for automotive body-in-white: a use case for the VALU3S ECSEL project

Verification and validation (V&V) of systems, and system of systems, in an industrial context has never been as important as today. The recent developments in automated cyber-physical systems, digital twin environments, and Industry 4.0 applications require effective and comprehensive V&V mechanisms. Verification and Validation of Automated Systems' Safety and Security (VALU3S), a Horizon 2020 Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL-JU) project started in May 2020, aims to create and evaluate a multi-domain V&V framework that facilitates evaluation of automated systems from component level to system level, with the aim of reducing the time and effort needed to evaluate these systems. VALU3S focuses on V&V for the requirements of safety, cybersecurity, and privacy (SCP). This paper mainly focuses on the elaboration of one of the 13 use cases of VALU3S to identify the SCP issues in an automated robot inspection cell that is being actively used for the quality control assessment of automotive body-in-white. The joint study here embarks on a collaborative approach that puts the V&V methods and workflows for the robotic arms safety trajectory planning and execution, fault injection techniques, cyber-physical security vulnerability assessment, anomaly detection, and SCP countermeasures required for remote control and inspection. The paper also presents cross-links with ECSEL-JU goals and the current advancements in the market and scientific and technological state-of-play.

Intelligent Design Prediction Aided by Non-Uniform Parametric Study and Machine Learning in Feature Based Product Development

Automotive body structure design is critical to achieve lightweight and crash worthiness based on engineers’ experience. In the current design process, it frequently occurs that designers use a previous generation design to evolve the latest designs to meet certain targets. However, in this process the possibility of adapting design ideas from other models is unlikely. The uniqueness of each design and presence of non-uniform parameters further makes it difficult to compare two or more designs and extract useful feature information. There is a need for a method that will fill the missing gap in assisting designers with better design options. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study with machine learning in order to make valuable suggestions to the designer. The proposed method uses data sets produced from experiment design to reduce the number of parameters, perform parameter correlation studies and run finite element analysis (FEA), for a given set of loads. The response data generated from this FEA is then used in a machine learning algorithm to make predictions on the ideal features to be used in the design. The method can be applied to any component that has a feature-based parametric design.

A Study on the Effect of Process and Material Variables on the Hot Stamping Formability of Automotive Body Parts

Hot stamping is a method capable of manufacturing high-strength automotive body parts by inducing a martensitic phase transformation through forming and die quenching after heating a metal sheet into a high temperature austenite phase. However, it is not easy to solve various formability problems occurring in the hot stamping process due to the complexity of the process and material behavior during high temperature forming. In this study, fracture-related forming limits and martensite phase ratio were selected as criteria for evaluating hot stamping formability. First, a hot stamping test was performed on a T-type part that simplified the B-pillar, an automotive body part, and the fracture behavior according to the temperature and thickness of the sheet blank was investigated. Additionally, forming analysis was performed on the hot stamping process of mass-produced B-pillar parts by varying the temperature of the sheet blank, the thickness of the sheet blank, the die-blank friction coefficient, and the strain-rate sensitivity of material among various process and material variables. Based on the analysis results, the effect of each process and material variable on the hot stamping formability of B-pillar parts was quantitatively analyzed. By utilizing the results of this study, it will be possible to solve the formability problem that occurs in the mass-production hot stamping process for automotive body parts and improve the quality of parts in the future.

Assessment of Heat-Affected Zone Softening of Hot-Press-Formed Steel over 2.0 GPa Tensile Strength with Bead-On-Plate Laser Welding

High-strength hot-press-formed (HPF) steels with a fully martensitic microstructure are being widely used in the fabrication of automotive body structure, and 2.0 GPa-strength HPF steel has recently been commercially launched. However, heat-affected zone (HAZ) softening is unavoidable in welding martensitic steel. In this study, the HAZ softening characteristic of 2.0 GPa HPF steel was investigated by applying a high-brightness laser welding process, wherein the heat input was controlled by varying the welding speed. Microstructural evaluation and hardness test results showed that the base metal with a fully martensitic microstructure was changed to the same type of fully martensitic microstructure in the weld metal, while relatively soft microstructures of tempered martensite and ferrite phase were partially formed in the intercritical HAZ (ICHAZ) and subcritical HAZ (SCHAZ) areas. In the tensile test, the joint strength was 10–20% lower than that of the base metal, and the fracture initiation was estimated at the ICHAZ/SCHAZ boundary, where the lowest hardness was confirmed by the nanoindentation technique.

Assessment of Competency Based on Dicotomic Scoring to Diagnose Students 'Skills in Vocational Learning in Vocational Schools

Assessment of competency in schools is carried out by teachers using conventional tests, consists of multiple choice tests, essay tests, and skills tests using open scoring competency tests based on the teacher's principle of "being in charge" in scoring. Therefore, a assessment of competency tool is needed that measures the real competence of students. The research objective was to diagnose students' skills in automotive body vocational learning through a dichotomy scoring competency test. The research method used is pre experimental design (non designs) one shot case study. The research was conducted in January-March 2020. The research location was in a state vocational high school in Bandung. The research sample used simple random sampling of 32 students. Data collection used a dichotomy scoring skill test. Data analysis using percentages. The results showed that: (1) 84% of the students were not competent in the competence of automotive bodies in making mini cars when using the dichotomy scoring competency assessment; (2) only 16% of students were able to complete the competency test by producing work products that match dimensions and on time.