Machining feature-based system for supporting step-compliant milling process

STEP standards aims at setting up a standard description method for product data and providing a neutral exchanging mechanism that is independent of all the information processing systems for product information model. STEP Part 21 is the first implementation method from EXPRESS language and implemented successfully in CAD data. However, this text file consists of purely geometrical and topological data is hardly to be applied in machining process planning which requires machining features enriched data. The aim of this research is developing a new methodology to translate the EXPRESS language model of CAD STEP data into a new product data representation and enriched in machining features which is more beneficial to machining process planning. In this research, a target Database Management System (DBMS) was proposed for developing this system by using its fourth-generation tools that allow rapid development of applications through the provision of nonprocedural query language, reports generators, form generators, graphics generators, and application generators. The use of fourth-generation tools can improve productivity significantly and produce program that are easier to maintain. From this research, a new product data representation in a compact new table format is generated. Then this new product data representation has gone through a series of data enrichment process, such as normal face direction generation, edge convexity/concavity determination and machining features with transition feature recognition. Lastly, this new enriched product data representation is verified by generating to a new STEP standard data format which is according to ISO1030-224 standard format and providing an important part of solution for supporting STEP-compliant process planning and applications in milling process

Translation-based Constraint Answer Set Solving

We solve constraint satisfaction problems through translation to answer set programming (ASP). Our reformulations have the property that unit-propagation in the ASP solver achieves well defined local consistency properties like arc, bound and range consistency. Experiments demonstrate the computational value of this approach.Comment: Self-archived version for IJCAI'11 Best Paper Track submissio

SAT Modulo Monotonic Theories

We define the concept of a monotonic theory and show how to build efficient SMT (SAT Modulo Theory) solvers, including effective theory propagation and clause learning, for such theories. We present examples showing that monotonic theories arise from many common problems, e.g., graph properties such as reachability, shortest paths, connected components, minimum spanning tree, and max-flow/min-cut, and then demonstrate our framework by building SMT solvers for each of these theories. We apply these solvers to procedural content generation problems, demonstrating major speed-ups over state-of-the-art approaches based on SAT or Answer Set Programming, and easily solving several instances that were previously impractical to solve

Propagators and Solvers for the Algebra of Modular Systems

To appear in the proceedings of LPAR 21. Solving complex problems can involve non-trivial combinations of distinct knowledge bases and problem solvers. The Algebra of Modular Systems is a knowledge representation framework that provides a method for formally specifying such systems in purely semantic terms. Formally, an expression of the algebra defines a class of structures. Many expressive formalism used in practice solve the model expansion task, where a structure is given on the input and an expansion of this structure in the defined class of structures is searched (this practice overcomes the common undecidability problem for expressive logics). In this paper, we construct a solver for the model expansion task for a complex modular systems from an expression in the algebra and black-box propagators or solvers for the primitive modules. To this end, we define a general notion of propagators equipped with an explanation mechanism, an extension of the alge- bra to propagators, and a lazy conflict-driven learning algorithm. The result is a framework for seamlessly combining solving technology from different domains to produce a solver for a combined system.Comment: To appear in the proceedings of LPAR 2