In part-selection problems, parts are selected from catalogs and connected to meet the following problem requirements: functionality, specifications, and constraints. This paper formally defines the part-selection problem, enumerates a set of design properties that are useful during a search for a design solution, and provides an algorithm for solving part-selection problems based on a novel set of operators for manipulating portions of the design space.

This article is an attempt to improve the efficiency of procedures for compositional synthesis of design solutions using building blocks. These procedures have found use in a wide range of applications, and are one of the most substantial outcomes of research into automated synthesis of design solutions. Due to their combinatorial nature, these procedures are highly inefficient in solving problems, especially when the database of building blocks for synthesis or the problem size is large. Previous literature often focuses on improving only the algorithm part of a procedure, while it is both its algorithm and database which together determine the overall efficiency of the procedure. This article uses a case study to investigate and develop a set of rules for structuring and preprocessing a database of building blocks so as to improve the efficiency of synthesis of design solutions using this database.

In this work, we describe an approach to automated manufacturing feature extraction and operations planning that combines the two processes to benefit both. When viewed in planning terms, if feature extraction is the process of identifying planning goals, and operations planning is the the process of selecting and sequencing planning operators, then what we are doing can be viewed as combining the normally separate processes of finding goals and developing instantiated operators to satisfy them. Thus, we call this approach Ebgoc (Effector-Based Goal and Operator Construction), and we have implemented it in a computer program called Mediator. Effectors are the physical equipment, such as tools and machines, that are used to transform the initial materials into the desired end product (goal). We say that this approach is effector-based (rather than feature-based) because we do not recognize a fixed set of features, but instead we geometrically derive the set of shapes that can be machined with the currently available set of effectors. Thus, each shop can customize Mediator so that it identifies machinable volumes appropriate for the resource in that specific shop. This approach gives Mediator several important properties. It can 1) effectively handle feature interactions (e.g., volumetric intersections), 2) be customized to produce features appropriate to specific shop resources, 3) identify areas that the current resources cannot machine, and 4) handle nonstandard, user-defined tooling.

The evolution of computer aided design (CAD) systems and related technologies has promoted the development of software for the automatic configuration of mechanical systems. This occurred with the introduction of knowledge aided engineering (KAE) systems that enable computers to support the designer during the decision-making process. This paper presents a knowledge-based application that allows the designer to automatically compute and evaluate mass properties of racing cars. The system is constituted by two main components: the computing core, which determines the car model, and the graphic user interface, because of which the system may be used also by nonprogrammers. The computing core creates the model of the car based on a tree structure, which contains all car subsystems (e.g., suspension and chassis). Different part–subpart relationships define the tree model and link an object (e.g., suspension) to its components (e.g., wishbones and wheel). The definition of independent parameters (including design variables) and relationships definition allows the model to configure itself by evaluating all properties related to dimension, position, mass, etc. The graphic user interface allows the end user to interact with the car model by editing independent design parameters. It visualizes the main outputs of the model, which consist in numeric data (mass, center of mass of both the car and its subsystems) and graphic elements (car and subsystems 3D representation).

Environmental issues require a new manufacturing paradigm because the current mass production and mass consumption paradigm inevitably cause them. We have already proposed a new manufacturing paradigm called the “Post Mass Production Paradigm (PMPP)” that advocates sustainable production by decoupling economic growth from material and energy consumption. To realize PMPP, appropriate planning of a product life cycle (design of life cycle) is indispensable in addition to the traditional environmental conscious design methodologies. For supporting the design of a life cycle, this paper proposes a life-cycle simulation system that consists of a life-cycle simulator, an optimizer, a model editor, and knowledge bases. The simulation system evaluates product life cycles from an integrated view of environmental consciousness and economic profitability and optimizes the life cycles. A case study with the simulation system illustrates that the environmental impacts can be reduced drastically without decreasing corporate profits by appropriately combining maintenance, reuse and recycling, and by taking into consideration that optimized modular structures differ according to life-cycle options.

The application of Bayesian networks for monitoring and diagnosis of a multistage manufacturing process is described. Bayesian network “part models” were designed to represent individual parts in-process. These were combined to form a “process model,” a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the “process network.” Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given.

Considering manufacturing expertise during the early stages of design can be of great benefit. Such information can greatly improve not only the quality of a design, but it can also ensure the generation of an easily manufactured design. This, in turn, can lower the final cost of the designed product. By evaluating how easily an evolving conceptual design can be made, potential hazards can be avoided before any detailed design efforts commence. The conceptual manufacturing planning requisite to such an evaluation is the focus of this paper. A domain-independent strategy for conceptual manufacturing planning is presented. A task-structure analysis of this strategy shows its domain independence. A specific implementation of this strategy for polymer composites manufacturing planning (Socharis) is discussed. The high-level implementation details of Socharis are presented as instantiations of the conceptual manufacturing planning strategy. Finally, the validity of the planning strategy and the utility of Socharis are assessed.

Concurrent Engineering needs a series of measures (or measurement criteria) that are distinct to each process, and a set of metrics to check (and validate) the outcome when two or more of the life-cycle processes are overlapped or required to be executed in parallel. Because product realization involves concurrent processes that occur across multiple disciplines and organizations, appropriate measures and the methods of qualifying metrics are essential. Inevitably, such concurrent processes generate design conflicts among multiple life-cycle concerns. Individual assurances of satisfying life-cycle design criterion (one at a time) do not capture the most important aspect of Concurrent Engineering—the system perspective—meaning achieving a well-balanced trade-off among the different life-cycle design measures. While satisfying life-cycle design measures in a serial manner only those, which are not in conflict, are occasionally met. The paper first describes a set of life-cycle measures and metrics and explains how those could be used for gaining operational excellence. Second, this paper provides an insight into the mechanisms (such as knowledge-based systems, rule-based simulation, and rule-based optimization) to ensure an effective trade-off across different life-cycle measures, customer requirements, and their inclusion into a product design, development, and delivery (PD3) process.

A case-based design functionality is a natural and intuitive addition to a design tool that can augment human capabilities and help designers remember and retrieve appropriate cases. SEED-Config, a design environment for conceptual building design, was developed to incorporate a case-based reasoning functionality to provide designers with initial potential solutions. The case representation in SEED-Config is the BENT information model, which records design knowledge, supports the hierarchical decomposition of design cases, offers multiple views, and encapsulates the outcome of the design in addition to the problem specification and the design solution. The case library was implemented in an object-oriented database management system to accumulate cases automatically and to provide efficient query facilities. The case retrieval aspect of SEED-Config offers three different methods to find the most useful cases stored in the case library: task-based, lineage-based, and customized. Case retrieval responds to the exploratory nature of the design process and supports versatile case retrieval by providing multiple paths to each case. The case adaptation aspect, which adjusts the selected case to the new problem to provide a complete solution, uses an adaptation method called derivational replay. The case-based design capabilities are completely integrated within the design environment from which the cases originate.

Production systems have traditionally been used in engineering to aid the designer in generating designs in domains not significantly influenced by geometry. However, in geometric-based domains such as mechanical engineering, their popularity has been limited. This work proposes the use of shape grammars as the framework for engineering expert systems. Shape grammars are shown to have all the generation and analysis capabilities of traditional production systems, while being able to represent knowledge about both the functionality and the form of a product. Additionally, the parametric nature of shape grammars, their ability to deal with physical form rather than abstract elements, and their ability to recognize emergent shapes give them significant advantages over traditional production systems for geometry-based engineering design.

Since the operation of technical systems can be explained using physical laws, why then might we not use them explicitly in designing these systems? The characteristic initial binding variable, with which appropriate physical laws are sought, first needs to be extracted from the function of the future technical system. If there are several appropriate physical laws (i.e., operators), we evaluate them using the Analytic Hierarchy Process (AHP) method. The most suitable is then selected with regard to the chosen criteria based on design requirements. When one physical law is not sufficient for the design of a technical system, several laws are linked together using binding variables to form a conceptual chain (i.e., macro-operator). Such a chain does not only contain supporting physical laws; physical laws indirectly introduce basic models of shape, their basic topology, geometry, and basic material properties into the chain. A prototype computer-aided design system is based on the prescriptive conceptual design model presented below.

The use of agent-based systems may offer significant advantages over centralized hierarchical manufacturing systems, including distribution of control/processing and data as well as adaptable automated or semiautomated problem solving. However, simply applying any agent-based paradigm to manufacturing domains may not be enough to address the various demands of these domains. Manufacturing domains require agent-based problem solving to be flexible and tolerant of faulty information (e.g., due to faulty sensors), equipments, and communication links. These issues are addressed by research on Sensible Agents, capable of 1) Dynamic Adaptive Autonomy and 2) explicit Perspective Modeling. Dynamic Adaptive Autonomy allows Sensible Agents to change problem-solving roles during system operation to address dynamic factory floor conditions, while Perspective Models internal to a respective Sensible Agent serve to maintain knowledge about the agent itself, other agents, and the environment. The application of the Sensible Agent paradigm to a shop-floor control is demonstrated.