HFS89: An Expert "Critiquer" for Propulsion Gear Design

Appeared in the the Proceedings of the 1989 Conference of the Human Factors Society, October, 1989.

An Expert "Critiquer" For Propulsion Gear Design: A Case Study In Intelligent Decision Support

Ellen K. McKinley
Westinghouse Science & Technology Center
Pittsburgh, PA

Michael L. Mauldin
Carnegie Mellon University
Pittsburgh, PA

Emilie M. Roth
Westinghouse Science & Technology Center
Pittsburgh, PA


This paper describes an "intelligent" designer's aid that was developed to support the design of marine propulsion gears. Key elements of the system include: conversion of gear design formulas from a procedural to a declarative form to facilitate inspection: a direct-manipulation interface; and encoding of "expert" design constraint knowledge. The case study demonstrates that delivering "expert knowledge" is often only a small element of an "intelligent" support system, and provides a concrete illustration of the importance of a cognitive task analysis in defining the elements of an effective support system. This design solution should have applicability to other engineering design tasks.


Advances in computer science and artificial intelligence (AI) are providing powerful new tools that expand the potential to support complex cognitive tasks: diagnosis, troubleshooting, planning, design. While these tools provide new opportunity for support, they also create new challenges: how to deploy the machine power afforded by these new technologies to support human activity (Roth, Bennett & Woods, 1987; Woods & Roth 1988). In this paper we describe a case study in the design of intelligent decision support systems: an intelligent system to support the design of marine propulsion gears. The case study demonstrates how encoding and delivering "expert knowledge" is often only a small element of a total support system, and provides a concrete illustration of the importance of a cognitive task analysis in defining the elements of an effective intelligent decision support systems.

Propulsion gear design encompasses many features of engineering design problems in general. While there is a large experiential base on how to design gears, it remains a creative task with no closed form solution. In this respect, it differs greatly from the types of procedural tasks (e.g., maintenance diagnosis) that have received the most attention in the applied expert system literature (e.g., Richardson, 1985). The gear design process involves consideration and balancing of multiple constraints based on multiple points of view on the problem (e.g., stress, noise, manufacturability, maintenance). The relevant knowledge of constraints resides in different individuals or groups. For example, gear designers do not have ready information on manufacturing capabilities and constraints that impact on the economic viability of alternative gear designs. As a result, the design process often involves multiple iterations across groups.

A need was felt to build a designer's aid that could encode and deliver expert knowledge of the multiple design constraints and design strategies for meeting them in order to reduce the need for across group iterations. The need was perceived to be all the more pressing because several individuals with expert knowledge of the manufacturing process and its impact on gear design were near retirement age. The perceived goal was to build an "expert system" that would capture their unique manufacturing knowledge for delivery to the primary gear designers. This challenge was how to utilize advanced computational techniques to support the creative design process rather than build la system that would automate the design task (Woods & Roth, 1988).


A thorough cognitive task analysis of the cognitive activities involved in the gear design process was conducted (Woods & Roth, 1988). This included analysis of the kinds of manufacturing knowledge that impacts gear design, analysis of kinds of contributions made by the experts in gear manufacture to gear design, and analysis of the sources problems and bottlenecks in the gear design process in general. The analysis revealed that experts ingear manufacture contributed significantly to gear design. There were three sources of contribution, two of which could be captured in a design aid. First, gear manufacturers possessed knowledge of manufacturing constraints (e.g., capabilities and limits of available tooling machinery) that impacted on the feasibility and economic viability of gear designs. Second these experts had developed computer programs (written in BASIC and FORTRAN) that defined the exact parameters of the gears for manufacturing purposes. Translation of this software into a non-opaque format for use by the primary gear designers turned out to be a key element for effective support of the gear designers. Third, gear manufacturing experts contributed to creative design solutions for unique design problems. This third, creative contribution, is what marks true human expertise and falls beyond the bounds of what can be captured by state-of-the-art Al systems.

Analysis of the gear design process also revealed that one of the major sources of problem was the opacity of the existing codes for computing gear parameters. Because gear design is not a closed form problem, there are often several alternative formulas which may be used to compute particular gear parameters (different ones may apply under different circumstances, and/or may be advocated by different engineering organizations). The opacity of the software made it difficult to identify what formula was being used in any particular case. This made it difficult to trust any given program, reconcile inconsistency in answers among programs, and incorporate new formulas into the programs. In addition, the design software they were using was run in batch mode, thus delaying feedback, and inhibiting rapid iteration in design.

A generic software system was developed to meet these requirements. Key elements in the design solution were:

This system has been delivered to the gear design engineers and has been well received. The intelligent design environment allows the engineer to rapidly try out alternative designs, see (and be able to modify) what formulas are employed, and get immediate feedback on the implications of a design change (both in terms of a graphic display and a check of whether any design constraints are violated). The system reduces the time spent in the mundane part of the design process and allows the engineer more time for developing creative design solutions.

Figure 1: Basic design screen partially completed

Figure 2: Basic design screen showing constraint violation

Figure 3: Basic design screen and graphical display


Several conclusions with applicability beyond gear design may be drawn from this study. First, the study provides a concrete illustration that intelligent decision support need not be synonymous with "delivering expertise in a box". In this case, encoding of expert design rules was only a small element of the total intelligent decision support system. The primary key to performance enhancement was the conversion of engineering formulas from a procedural format to a declarative format that allowed them to be more readily inspected and manipulated. Second, the direct manipulation system that was built demonstrates unique application of advanced computational techniques with application beyond the initial gear design problem.


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Roth, E.M., Bennett, K., Woods, D. D., ``Human Interaction with an `Intelligent Machine,''' International Journal of Man Machine Studies, 1987, Vol. 27 , pp. 479-525.

Roth, E.M. Woods, D. D., ``Aiding Human Performance: I. Cognitive Analysis,'' Le Travail Humain, 1988, Vol. 51, pp. 39-64.

Schneiderman, B. ``The Future of Interactive Systems and the Emergence of Direct Manipulation,'' Behavior and Information Technology, 1, 1982, 237-256.

Woods, D.D., Roth E.M., ``Cognitive Engineering: Human Problem Solving with Tools,'' Human Factors, Vol. 30, No. 4, 1988, pp. 415-430.

Last updated 14-Sep-94