The Explorer parametric design assistant, an interactive tool that provides intelligent support for searching concurrent-engineering trade-spaces under multiple, conflicting objectives, is described. The system provides a convenient means for specifying multiple, cross-disciplinary constraints in terms of tables, formulas, and logical sentences. Based on these data, the system performs interactive constraint checking, computes feasible designs, and provides graphical analysis facilities, allowing users to compare designs based on multiple criteria. As a first application, Explorer has been used as a printed circuit board (PCB) construction design assistant. In initial tests, Explorer has helped users to find design configurations not previously considered that yield comparable performance and cost while offering better manufacturability and reliability. The capabilities and use of the Explorer system are described in detail, the underlying technologies are outlined, and an evaluation of the prototype system is presented.

Design has been extensively studied by artificial intelligence researchers for many years. These studies have resulted in a large number of design tools that perform interesting tasks. Understanding the capabilities of these tools is, however, very difficult, which seriously impedes progress in the field. A better understanding of different tools can be achieved by analyzing the knowledge use of existing tools. Such an analysis of six configuration design tools is presented. This results in a model of configuration design that shows significant similarity in the tasks performed by these tools.

The hub location problems involve locating facilities and designing hub networks to minimize the total cost of transportation (as a function of distance) between hubs, establishing facilities and demand management. In this paper, we consider the capacitated cluster hub location problem because of its wide range of applications in real-world cases, especially in transportation and telecommunication networks. In this regard, a mathematical model is presented to address this problem under capacity constraints imposed on hubs and transportation lines. Then, a new hybrid algorithm based on simulated annealing and ant colony optimization is proposed to solve the presented problem. Finally, the computational experiments demonstrate that the proposed heuristic algorithm is both effective and efficient.

A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

This article describes the technical principles and representation of a constraint-based configuration method for work processes. Methods developed for the configuration of modular systems comprising components have traditionally adopted a representation where the properties and compatibility requirements are expressed as constraints associated with individual components. However, this representation does not accurately capture constraints on paths and subprocesses and is therefore unsuitable for process configuration. This article extends established constraint-based configuration methods with a constraint language for specifying properties of execution paths in work processes. A framework for semiautomated process customization is presented. It integrates the extended constraint language with a metamodel of the work processes in an organization and allows to adapt generic work processes to fit the requirements of specific development projects. Heuristic search methods are applied to build valid process configurations by incrementally resolving constraint violations. The declarative framework facilitates the adaptation of abstract work processes as well as the validation and repair of existing processes. The approach was developed in the context of a real-world system of complex design and development processes where it was shown that significant process improvements and reduction in effort required to edit process models can be achieved.

With the rise of the “Maker Movement” and the entrepreneurial university, academic makerspaces became widespread. These facilities provide tools and machines that enable making and tinkering; and while the offerings, organizational and operational models, and outreach of the academic makerspaces can vary widely across institutions, their common value proposition is enabling innovation, entrepreneurship, and hands-on project-based learning and these studies are largely qualitative and exploratory by nature. Through a case study, this paper presents an in-depth analysis and insights on the users and usage of an academic makerspace. Using the data generated by and collected from the users of an academic makerspace, we evaluate the effects of having access to the makerspace on users' teaching and learning experiences, and their satisfaction with the offerings. Our results show that attracting courses and educators to the facilities played a strong role in growing the user base, courses and teaching activities introduced new teaching and learning activities to adopt the offerings, group and project work is positively impacted, and the users are very satisfied with the facilities and having the access to its offerings. The analysis also showed that the demand for the offerings can be challenging to manage during certain periods, most of the users come from three departments (mechanical, electrical, civil engineering), and the diversity of the users could improve with the introduction of new offerings, such as a wet lab for bio/chemistry experiments and a food lab to tinker with food processing and preparation.

The recognition and identification of parts are important processes in modern manufacturing systems. Although machine vision systems have played an important role in these tasks, there are still challenges in performing these tasks in which parts may be in motion and subjected to noise. Using a flexible vibratory bowl feeder system as a test bed to simulate motion of parts subjected to noise, scanned signatures of part features are acquired using fiber optic sensors and a data acquisition system. Because neural networks have been shown to exhibit good pattern recognition capability, ARTMAP, a neural network that learns patterns under supervision, was incorporated into the feeder system. The pattern recognition capability of the feeder system is dependent on a set of parameters that characterized ARTMAP, the sampling rate of the data acquisition system, and the mean speed of the vibrating parts. The parameters that characterized ARTMAP are the size of an input vector, the vigilance, threshold value of the nonlinear noise suppression function, and the learning rate. Through extensive training and testing of the ARTMAP within the feeder system, it was shown that high success rates of recognition of parts features in motion under noisy conditions can be obtained provided these parameters of ARTMAP are appropriately selected.

Decision making is a significant activity within industry and although much attention has been paid to the manner in which goals impact on how decision making is executed, there has been less focus on the impact decision making resources can have. This article describes an experiment that sought to provide greater insight into the impact that resources can have on how decision making is executed. Investigated variables included the experience levels of decision makers and the quality and availability of information resources. The experiment provided insights into the variety of impacts that resources can have upon decision making, manifested through the evolution of the approaches, methods, and processes used within it. The findings illustrated that there could be an impact on the decision-making process but not on the method or approach, the method and process but not the approach, or the approach, method, and process. In addition, resources were observed to have multiple impacts, which can emerge in different timescales. Given these findings, research is suggested into the development of resource-impact models that would describe the relationships existing between the decision-making activity and resources, together with the development of techniques for reasoning using these models. This would enhance the development of systems that could offer improved levels of decision support through managing the impact of resources on decision making.

As a benefit of modularization of complex systems, original equipment manufacturers (OEMs) can choose suppliers in a less constricted way when faced with new or evolving requirements. However, new suppliers usually add uncertainties to the system development. Because suppliers are tightly integrated into the design process in modular design and therefore greatly influence the outcome of the OEM's products, the uncertainty along with requirements satisfaction of the suppliers and their modules should be controlled starting from potential supplier identification. In addition, to better satisfy new requirements, the potential supplier identification should be combined with architecture generation to enable the new technology integration. In this paper, we propose the Architecture & Supplier Identification Tool, which generates all possible architectures and corresponding suppliers based on new requirements through matrix mapping and propagation. Using the Architecture & Supplier Identification Tool, the overall uncertainty and requirements satisfaction of generated architectures can be estimated and controlled. The proposed method aims at providing decision support for early design of complex systems, thereby helping OEMs have an integrated view of suppliers and system architectures in requirements satisfaction and overall uncertainty.

Abstraction and generalization of layout design cases generate new knowledge that is more widely applicable to use than specific design cases. The abstraction and generalization of design cases into hierarchical levels of abstractions provide the designer with the flexibility to apply any level of abstract and generalized knowledge for a new layout design problem. Existing case-based layout learning (CBLL) systems abstract and generalize cases into single levels of abstractions, but not into a hierarchy. In this paper, we propose a new approach, termed customized viewpoint—spatial (CV–S), which supports the generalization and abstraction of spatial layouts into hierarchies along with a supporting system, SPIDA (SPatial Intelligent Design Assistant).

This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a "predictive CAD system" because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components for assemblies using 3D solid models. In contrast, this research work focuses on feature-based predictive CAD system using B-rep models. This paper investigates the performance of predictive models that could enable the creation of such an intelligent CAD system by assessing three different methods to support inference: sequential, machine learning, or probabilistic methods using N-Grams, Neural Networks (NNs), and Bayesian Networks (BNs) as representative of these methods. After defining the functional properties that characterize a predictive design system, a generic development methodology is presented. The methodology is used to carry out a systematic assessment of the relative performance of three methods each used to predict the diameter value of the next hole and boss feature type being added during the design of a hydraulic valve body. Evaluating predictive performance providing five recommendations ($k = 5$) for hole or boss features as a new design was developed, recall@k increased from around 30% to 50% and precision@k from around 50% to 70% as one to three features were added. The results indicate that the BN and NN models perform better than those using N-Grams. The practical impact of this contribution is assessed using a prototype (implemented as an extension to a commercial CAD system) by engineers whose comments defined an agenda for ongoing research in this area.

Protective packaging buffer design is a complex process which involves manipulating both analytical and graphical data, and expert knowledge. The design activities of this process can be represented as a series of procedural tasks, and it is therefore suitable for design automation efforts. Currently, the design activities of the protective packaging buffers are predominantly manual and done on a trial and error basis. In this paper, PROPACK, a knowledge-based CAD system which has been developed to automate this design process will be discussed. A description of how the knowledge and information in PROPACK are structured and stored in the database will also be made. Preliminary tests have found that PROPACK is able to generate possible design solutions that either require external or simple internal cushioning features used for protection. These results have found to compare quite favourably with the BASF worked examples.

The linking of research in machine learning with research in knowledge-based design is such that each of the two areas benefit from the consideration of the other. The use of machine learning in design addresses the perceived need to support the capture and representation of design knowledge, because handcrafting a representation is a difficult and time-consuming task. In addition, design provides a task with which to investigate the usefulness of existing machine learning techniques, and, perhaps, to discover new ones.

The function of a process planning system is to determine the methods by which a product is to be manufactured economically and competitively. In a modern manufacturing environment, a process planning system consists of highly trained people and complex software. The plans prepared by a process planning system are not always executed as planned. It is useful if the system can discover why plans fail, when they do fail. In order to learn why plans fail, the system must analyse a number of plans, both successful and unsuccessful, to find patterns in the failures of plans. This type of analysis is difficult for people, who are much better at analysing single events than multiple events.

The aim of the project described here is to design and implement a computer program which will help human planners in a process planning system to understand why plans fail. To achieve this aim, a program called IMAFO (Intelligent MAnufacturing FOreman) has been developed. IMAFO uses decision tree induction to analyse examples of both successful and unsuccessful plans.

The difficulties presented by this application are discussed and solutions are presented. Problems addressed include finding an appropriate set of attributes for describing the plans, using data efficiently, consolidating input from distinct sources, and presenting decision trees in an understandable form. Potential applications and directions for future research are considered.

This paper describes the development of models to simulate the process of Concept Design Evaluation. The models are an amalgam of a number of statistically based methods and approaches taken from the probability, reliability, and quality domains. They assume that designers use decomposition of design to undertake evaluation at design characteristic level with the total design evaluation being achieved, in some way, via recomposition. The models described in this paper attempt to describe how designers may perform recomposition and hence total design evaluation. It is argued that the ability to model this human activity is important for the future development of knowledge-based design tools.

Development of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.

The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

Interval arithmetic has been extensively applied to systems of linear equations by the interval matrix arithmetic community. This paper demonstrates through simple examples that some of this work can be viewed as particular instantiations of an abstract “design operation,” the RANGE operation of the Labeled Interval Calculus formalism for inference about sets of possibilities in design. These particular operations promise to solve a variety of design problems that lay beyond the reach of the original Labeled Interval Calculus. However, the abstract view also leads to a new operation, apparently overlooked by the matrix mathematics community, that should also be useful in design; the paper provides an algorithm for computing it.

Erection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly.