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RESEARCH PAPERS

Optimizing Truck Cab Layout for Driver Accommodation

[+] Author and Article Information
Matthew B. Parkinson

Engineering Design Program and Mechanical Engineering,  The Pennsylvania State University, University Park, Pennsylvania 16802parkinson@psu.edu

Matthew P. Reed

 University of Michigan Transportation Research Institute, Ann Arbor, Michigan 48109mreed@umich.edu

Michael Kokkolaras

Department of Mechanical Engineering,  University of Michigan, Ann Arbor, Michigan 48109mk@umich.edu

Panos Y. Papalambros

Department of Mechanical Engineering,  University of Michigan, Ann Arbor, Michigan 48109pyp@umich.edu

J. Mech. Des 129(11), 1110-1117 (Dec 20, 2006) (8 pages) doi:10.1115/1.2771181 History: Received August 01, 2006; Revised December 20, 2006

One important source of variability in the performance and success of products designed for use by people is the people themselves. In many cases, the acceptability of the design is affected more by the variability in the human users than by the variability attributable to the hardware from which the product is constructed. Designing for human variability as an inherent part of the product optimization process can improve the overall performance of the product. This paper presents a new approach to artifact design that applies population sampling and stochastic posture prediction in an optimization environment to achieve optimal designs that are robust to variability among users, including differences in age, physical size, strength, and cognitive capability. A case study involving the layout of the interior of a heavy truck cab is presented, focusing on simultaneous placement of the seat and steering-wheel adjustment ranges. Trade-offs between adjustability (an indicator of cost), driver accommodation, and safety are explored under this paradigm.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

A typical cab “driver packaging” problem involves the design of the interior environment so that a large population of drivers is accommodated. The AHP is a fiducial point to which other cab components are referenced. This figure shows a hypothetical seat adjustment range and the preferred seat location of a sample driver.

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Figure 2

Adjustability in both the steering wheel and the seat increases accommodation. The adjustment ranges depicted are larger than typical values to improve the clarity of the illustration.

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Figure 3

Schematic of optimization methodology, showing submodels and information flow

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Figure 4

Parameters defining the size of the cab are considered fixed inputs to the models utilized by the optimization algorithms

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Figure 5

Demonstration of censoring. This driver would prefer to sit further rearward and with the steering-wheel higher than the current placement of the components allows. Instead, the driver is positioned at the adjustability limits nearest the desired location and the posture is adjusted accordingly.

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Figure 6

Preferred and censored seat locations for the 1000 drivers after a virtual fit. The adjustment limits of the seat are also shown. Drivers whose preferred position is more than 10mm from their actual location were considered disaccommodated.

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Figure 7

The outline of the cab and the adjustment ranges for the seat and steering-wheel in the different design scenarios are shown. Bringing the seat and steering wheel forward and up allowed cab length to be decreased without sacrificing population accommodation. Simultaneously restricting cab height dramatically reduced accommodation, however, as the seat and wheel were forced down.

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Figure 8

Angles used in the calculation of the downvision metric, Dn,dv. They are calculated for each driver and the smaller of the two is used to determine compliance with the requirement.

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Figure 9

The actual and preferred∕unattainable seat and steering-wheel locations for the 1000 drivers in the sample population are superimposed on the adjustability limits for those components. The eye locations are also shown, with lines indicating those which do not meet head interference and safety∕regulatory constraints.

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