Nanxin Wang

Metamodeling Development for Vehicle Frontal Impact Simulation

Response surface methods or metamodels are commonly used to approximate large computationally expensive engineering systems. Five response surface methods are studied: Stepwise Regression, Moving Least Square, Kriging, Multiquadric, and Adaptive and Interactive Modeling System. A real-world frontal impact design problem is used as an example, which is a complex, highly nonlinear, transient, dynamic, large deformation finite element model. To study the accuracy of the metamodel, the optimal Latin Hypercube Sampling method is used to distribute the sampling points uniformly over the entire design space. The Root Mean Square Error (RMSE) is used as the error indicator. Convergence rate, widely used in the arena of the finite element method for evaluating new element’s performance, was exploited in this vehicle impact example.

Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design

Human appraisals are becoming increasingly important in the design of engineering systems to link engineering design attributes to customer preferences. Human appraisals are used to assess consumers’ opinions of a given product design, and are unique in that the experiment response is a function of both the product attributes and the respondents’ human attributes. The design of a human appraisal is characterized as a split-plot design, in which the respondents’ human attributes form the whole-plot factors while the product attributes form the split-plot factors. The experiments are also characterized by random block effects, in which the design configurations evaluated by a single respondent form a block. An experimental design algorithm is needed for human appraisal experiments because standard experimental designs often do not meet the needs of these experiments. In this work, an algorithmic approach to identify the optimal design for a human appraisal experiment is developed, which considers the effects of respondent fatigue and the blocked and split-plot structures of such a design. The developed algorithm seeks to identify the experimental design, which maximizes the determinant of the Fisher information matrix. The algorithm is derived assuming an ordered logit model will be used to model the rating responses. The advantages of this approach over competing approaches for minimizing the number of appraisal experiments and model-building efficiency are demonstrated using an automotive interior package human appraisal as an example. DOI: 10.1115/1.3149845

A hierarchical choice modelling approach for incorporating customer preferences in vehicle package design

The use of customer preference models to evaluate the economic impact of design changes and new product introductions has become prevalent in the literature. However, existing approaches do not sufficiently address the needs of complex design artefacts, which typically consist of many subsystems and components designed and manufactured with significant autonomy. Characteristics of complex systems, such as heterogeneity of consumer preferences throughout the system hierarchy, multiple sources of information and qualitative consumer-desired attributes, have not been adequately addressed. In this work, we propose a hierarchical choice modelling approach for complex systems to model customer preferences for attributes throughout the system hierarchy, and to subsequently predict consumer choice behaviours. A system of hierarchical models is used to link the design attributes used for engineering design to the attributes used by consumers to choose among competing products. The model framework utilises Discrete Choice Analysis at the top level to model customer choices and Ordered Logit regression at the lower levels to model ordinal survey responses as a function of product attributes. An approach for combining choice data from multiple sources based on the Nested Logit methodology is developed. The framework is demonstrated on the vehicle occupant package case study.

Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design

Choice models play a critical role in enterprise-driven design by providing a link between engineering design attributes and customer preferences. However, existing approaches do not sufficiently capture heterogeneous consumer preferences nor address the needs of complex design artifacts, which typically consist of many subsystems and components. An integrated Bayesian hierarchical choice modeling (IBHCM) approach is developed in this work, which provides an integrated solution procedure and a highly flexible choice modeling approach for complex system design. The hierarchical choice modeling framework utilizes multiple model levels corresponding to the complex system hierarchy to create a link between qualitative attributes considered by consumers when selecting a product and quantitative attributes used for engineering design. To capture heterogeneous and stochastic consumer preferences, the mixed logit choice model is used to predict consumer system-level choices, and the random-effects ordered logit model is used to model consumer evaluations of system and subsystem level design features. In the proposed approach, both systematic and random consumer heterogeneity are explicitly considered, the ability to combine multiple sources of data for model estimation and updating is provided using the Bayesian estimation methodology and an integrated estimation procedure is introduced to mitigate error propagated throughout the model hierarchy. The new modeling approach is validated using several metrics and validation techniques for behavior models. The benefits of the IBHCM method are demonstrated in the design of an automobile occupant package.

INCORPORATING CUSTOMER PREFERENCES AND MARKET TRENDS IN VEHICLE PACKAGE DESIGN

Demand models play a critical role in enterprise-driven design by expressing revenues and costs as functions of product attributes. However, existing demand modeling approaches in the design literature do not sufficiently address the unique issues that arise when complex systems are being considered. Current approaches typically consider customer preferences for only quantitative product characteristics and do not offer a methodology to incorporate customer preference-data from multiple component/subsystem-specific surveys to make product-level design trade-offs. In this paper, we propose a hierarchical choice modeling approach that addresses the special needs of complex engineering systems. The approach incorporates the use of qualitative attributes and provides a framework for pooling data from multiple sources. Heterogeneity in the market and in customer-preferences is explicitly considered in the choice model to accurately reflect choice behavior. Ordered logistic regression is introduced to model survey-ratings and is shown to be free of the deficiencies associated with competing techniques, and a Nested Logit-based approach is proposed to estimate a system-level demand model by pooling data from multiple component/subsystem-specific surveys. The design of the automotive vehicle occupant package is used to demonstrate the proposed approach and the impact of both packaging design decisions and customer demographics upon vehicle choice are investigated. The focus of this paper is on demonstrating the demand (choice) modeling aspects of the approach rather than on the vehicle package design.Copyright

Understanding and modelling heterogeneity of human preferences for engineering design

In todays competitive market, it is essential for companies to provide products which not only achieve high performance, but also appeal to the tastes of consumers. Therefore, a key element of design is an understanding of consumer preferences for product features. In this work, the random-effects ordered logit model is proposed as the modelling framework to capture the impact of both product and human attributes on consumers’ ratings of qualitative system and sub-system attributes. To support the modelling, a series of methodologies are developed to both understand and model the influence of consumer heterogeneity upon product preferences. To illustrate the methodologies, a human appraisal experiment for understanding preferences for automobile occupant package design is analysed. An issue with analysing human appraisal experiments is that the effect of respondent heterogeneity must be understood to separate the influence of design factors from that of human factors. Hierarchical Bayes estimation and cluster analysis are used to gain an understanding of respondent rating styles, which are subsequently modelled explicitly in the ordered logit model. Smoothing spline regression is used to determine the functional form of the ordered logit model. The proposed ordered logit model is validated using a vehicle design case study.

Design and Verification of a New Computer Controlled Seating Buck

Appraising vehicle package design concepts using seating bucks — physical prototypes representing vehicle package, is an integral part of the vehicle package design process. Building such bucks is costly and may impose substantial burden on the vehicle design cycle time. Further, static seating bucks lack the flexibility to accommodate design iterations during the gradual progression of a vehicle program. A “Computer controlled seating buck”, as described in this paper, is a quick and inexpensive alternative to the traditional seating bucks with the desired degree of fidelity. It is particularly useful to perform package and ergonomic studies in the early stages of a vehicle program, long before the data is available to build a traditional seating buck. Such a seating buck has been developed to accommodate Ford vehicle package design needs. This paper presents the functional requirements, the high level conceptual design of how these requirements are realized, and the methods to verify, improve and sustain the dimensional accuracy and capability of the new computer controlled seating buck.Copyright

A Parametric Approach to Vehicle Seating Buck Design

Vehicle package development is an important part of the entire vehicle design. It consists of determining the occupant’s spatial environment, the vehicle’s mechanical spatial configuration and the overall exterior/interior dimensions while meeting the engineering requirements, including packaging, structure, manufacturing, etc. Developing and verifying the occupant compartment configuration is usually conducted by using a seating buck. To build a seating buck, vehicle interior surfaces are generated in CAD using vehicle exterior surfaces, package layouts and master sections. During early program stages, this information is scattered, incomplete and constantly changing, which makes the seating buck creation challenging and the package design decision-making more difficult. A new method has been developed to quickly generate the seating buck surfaces from scattered information. It has shown to significantly reduce the time conventionally required for the seating buck surface modeling. This paper documents the method and process and summarizes the potential of the method and its impact on vehicle package design.Copyright

Predicting Subjective Responses From Human Motion: Application to Vehicle Ingress Assessment

The ease of entering a car is one of the important ergonomic factors that car manufacturers consider during the process of car design. This has motivated many researchers to investigate factors that affect discomfort during ingress. The patterns of motion during ingress may be related to discomfort, but the analysis of motion is challenging. In this paper, a modeling framework is proposed to use the motions of body landmarks to predict subjectively reported discomfort during ingress. Foot trajectories are used to identify a set of trials with a consistent right-leg-first strategy. The trajectories from 20 landmarks on the limbs and torso are parameterized using B-spline basis functions. Two group selection methods, group nonnegative garrote (GNNG) and stepwise group selection (SGS), are used to filter and identify the trajectories that are important for prediction. Finally, a classification and prediction model is built using support vector machine (SVM). The performance of the proposed framework is then evaluated against simpler, more common prediction models.Copyright

Enhancing Vehicle Ingress/Egress Ergonomics with Digital Human Models

The ease of getting in and out of a vehicle (or ingress/egress) is one of the most important ergonomic issues for automotive manufacturers. It represents the first physical contact of a customer with a vehicle. A pleasant sensation while interfacing with the vehicle plays a vital role in vehicle purchasing decisions. Understanding and being able to assess vehicle ingress/egress performance early in a design process is therefore critical to a successful vehicle design. Conventional method relies on clinic research with physical bucks, which is a time consuming and pure subjective process. A new hardware-in-the-loop motion analysis system has been developed, which uses the latest motion capture, biomechanical and digital human modelling technologies to capture and analyse human motions as the driver or passenger interacts with a vehicle. The design assessment is provided in both subjective ratings and, for the first time, the physical data (e.g., swept volumes in CAD). The use of the system avoids costly seating buck builds and reduces engineering time and cost associated with both buck build and conducting the tests. Most importantly, it enables engineers to assess a vehicle’s ingress/egress performances early in its design process, which will lead to better vehicle packages and better customer satisfaction.