Constraint processing has emerged as an extremely expressive and powerful paradigm in which to model, solve, and reason about many complex problems. Over the past four decades advances in both the fundamental aspects of constraint processing and practical applications of constraints have contributed to making it one of the most promising of artificial intelligence technologies.

In constraint-based design, components are modeled by variables describing their properties and subject to physical or mechanical constraints. However, some other constraints are difficult to represent, like comfort or user satisfaction. Partially defined constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using machine-learning techniques. Because constraints are actively used during solving for pruning domains, building a classifier for instances is not enough: we need a solver able to reduce variable domains. Our technique is composed of two steps: first we learn a classifier for the constraint's projections and then we transform the classifier into a propagator. We show that our technique not only has good learning performances but also yields a very efficient solver for the learned constraint.

High speed machinery has played and continues to play a vital role in the manufacture and production of consumer goods. In the design of high speed systems there are two key considerations: power transmission and motion control. Although there is considerable computer-based support for the design of systems to achieve requirements of power transmission, there is only limited support for the design of systems to deliver complex motion control. This is particularly the case where mechanism and linkage systems are considered in order to achieve large displacements and intricate paths involving reentrant and reciprocating components. One explanation for this relative lack of supportive tools is the underlying reasoning and analysis techniques implemented within many commercial and research software environments. To overcome these limitations a constraint-based approach has been employed to provide the fundamental elements of a design environment for mechanisms and machine systems. The design environment provides support for the transition from concept to embodiment stages of the design process and the subsequent stages of detailed design and optimization. In contrast to many research approaches the design environment presented in this paper has been created and developed through close collaboration with industry and through extensive application to real design scenarios. First, the underlying representations and methods are presented. The fundamental elements of the design environment are then described and its capabilities discussed with particular reference to the use of constraints in design.

The problem investigated in this research is that engineering design decision making can be complicated and made difficult by highly coupled design parameters and the vast number of design parameters. This complication often hinders the full exploration of a design solution space in order to generate optimal design solution. These hindrances result in inferior or unfit design solutions generated for a given design problem due to a lack of understanding of both the problem and the solution space. This research introduces a computational framework of a new algebraic constraint-based design approach aimed at providing a deeper understanding of the design problem and enabling the designers to gain insights to the dynamic solution space and the problem. This will enable designers to make informed decisions based on the insights derived from parameter relationships extracted. This paper also describes an enhanced understanding of an engineering design process as a constraint centered design. It argues that with more effort and appreciation of the benefits derived from this constraint-based design approach, engineering design can be advanced significantly by first generating a more quantitative product design specification and then using these quantitative statements as the basis for constraint-based rigorous design. The approach has been investigated in the context of whole product life-cycle design and multidisciplinary design, aiming to derive a generic constraint-based design approach that can cope with life-cycle design and different engineering disciplines. A prototype system has been implemented based on a constraint-based system architecture. The paper gives details of the constraint-based design process through illustrating a worked real design example. The successful application of the approach in two highly coupled engineering design problems and the evaluation undertaken by a group of experienced designers show that the approach does provide the designers with insights for better exploration, enabled by the algebraic constraint solver. The approach thus provides a significant step towards fuller scale constraint-based scientific design.

In this paper, we present the development and application of a technical feasibility model used in preliminary design to determine whether a set of desired product specifications obtained from marketing is feasible in the engineering domain. This model is developed by integrating the capabilities of a multiobjective design problem, a multicriteria design optimization tool, a Pareto frontier gap analyzer, metamodeling methods, and use of the Pareto frontier as a constraint for feasibility assessment. Although the tools are independent of the domain, their application is illustrated using two examples: a simple three-objective mathematical problem and a five-objective passenger vehicle design problem. The feasibility of the desired product specifications is determined with respect to the problem's Pareto frontier, which is considered to be the necessary constraint to satisfy.

In current commercial feature modeling systems, support for direct manipulation of features is not commonly available. This is partly due to the strong reliance of such systems on constraints, but also to the lack of speed of current constraint solvers. In this paper, an approach to the optimization of geometric constraint solving for direct manipulation of feature dimensions, orientation, and position is described. Details are provided on how this approach was successfully implemented in the Spiff feature modeling system.

Reasoning about relationships among design constraints can facilitate objective and effective decision making at various stages of engineering design. Exploiting dominance among constraints is one particularly strong approach to simplifying design problems and to focusing designers' attention on critical design issues. Three distinct approaches to constraint dominance identification have been reported in the literature. We lay down the basic principles of these approaches with simple examples, and we apply these methods to a practical linear electric actuator design problem. With the help of the design problem we demonstrate strategies to synergistically employ the dominance identification methods. Specifically, we present an approach that utilizes the transitive nature of the dominance relation. The identification of dominance provides insight into the design of linear actuators, which leads to effective decisions at the conceptual stage of the design. We show that the dominance determination methods can be synergistically employed with other constraint reasoning methods such as interval propagation methods and monotonicity analysis to achieve an optimal solution for a particular design configuration of the linear actuator. The dominance determination methods and strategies for their employment are amenable for automation and can be part of a suite of tools available to assist the designer in detailed as well as conceptual design.