Quantified Relations A Class of Predicate Logic Design Constraints Among

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Copyright © 1996 by ASME
variation affect the design, manufacture, and operation of engineered devices or systems. Variations are uncertainties in the value of variables describing quantities. They arise from many sources, including: manufacturing variations, changes in environmental conditions, operator adjustments, or even uncertainty in the decisions of other engineers. As a result, single values are not always sufficient to describe quantities in design processes. Consider a centerless grinding operation producing precision, quarter-inch dowel pins. Due to minute part-to-part variations, any pin's diameter may be up to 0.0002 inch oversize, so 0.2500 does not fully describe the diameter; an accurate description requires a more complex representation. Probability distributions, such as normal curves, are often used to describe variations resulting from these types of stochastic processes. Probability of occurrence, however, is not applicable to some types of design uncertainty, such as the selection of a part number from a catalog. Fuzzy set representations have also been used to encode imprecision in design processes (Wood and Antonsson, 1989). Another approach represents uncertainty in design with arbitrary sets, including closed intervals of real numbers, of possible values for each variable (Habib and Ward, 1991). Here, a problem arises; conventional notions of design constraints are limited to constraints among single values, typified by equalities and inequalities. To apply a set-based representation to engineering design processes affected by variations, this notion must be extended to constraints among sets of values. This paper presents quantified relations (QR's), a formalism using first order predicate logic (PL) quantifiers to represent a class of constraints among sets of design, manufacturing, operating, and other variations. These quantifiers ( ∀ and ∃ ) provide a precise, compact, domain independent notation to describe a wide variety of useful design constraints. The formalism is not limited to a particular kind of set, but can describe constraints among discrete sets, continuous intervals, disjoint intervals, etc. Another advantage of the formalism is its ability to express causal relationships between design variables. Propagation algorithms can then make correct and useful inferences about sets of design variations. The remainder of this section defines the nomenclature for the paper, details two failures of conventional constraint propagation methods, and compares this approach with previous work. Section 2 discusses causal relationships between design variables and introduces a tabular representation for them. Section 3 defines the class of PL expressions for quantified relations and illustrates their use in representing engineering design constraints. Section 4 presents a method for propagating closed intervals through PL constraints involving continuous equations. Section 5 then applies the method to the two examples of Section 1.3, making correct inferences in both problems. Section 6 reviews the contributions of the paper and describes several directions for future research.
X∀ , X∃ g a (x)
A
Vectors of sets associated with variables in x ∀ and x ∃ respectively g(a , x) = 0 solved for a; a = g a (x) Maximum of a closed interval ( [a1 , a 2 ]= a 2 ) Minimum of a closed interval ( [a1 , a 2 ]= a1 ) Variables for which g(x) is monotonically
QUANTIFIED RELATIONS: A CLASS OF PREDICATE LOGIC DESIGN CONSTRAINTS AMONG SETS OF MANUFACTURING, OPERATING, AND OTHER VARIATIONS
William W. Finch MEAM Dept. University of Michigan Ann Arbor, Michigan 48109-2125, USA Tel: (313) 248-4807 Fax: (313) 621-8381 wfinch@ Allen C. Ward MEAM Dept. University of Michigan Ann Arbor, Michigan 48109-2125, USA Tel: (313) 936-0353 Fax: (313) 747-3170 award@
ABSTRACT This paper addresses a class of engineering design problems in which multiple sources of variations affect a product's design, manufacture, and performance. Examples of these sources include uncertainty in nominal dimensions, variations in manufacture, changing environmental or operating conditions, and operator adjustments. Quantified relations (QR's) are defined as a class of predicate logic expressions representing constraints between sets of design variations. Within QR's, each variable's quantifier and the order of quantification express a physical system's causal relationships. This paper also presents an algorithm which propagates intervals through QR's involving continuous, monotonic equations. Causal relationships between variables in engineering systems are discussed, and a tabular representation for them is presented. This work aims to broaden the application of automated constraint satisfaction algorithms, shortening design cycles for this class of problem by reducing modeling, and possibly computing effort. It seems to subsume Ward's prior work on the Label Interval Calculus, extending the approach to a wider range of engineering design problems. NOMENCLATURE a , b, c, Variables for real numbers, or set elements A, B, C, x, X x∀ , x∃ Corresponding variables for closed intervals, or sets Vectors of variables for elements and sets Vectors of universally quantified variables and existentially