This paper is intended to be the opening salvo of the workshop, Computing Futures in Engineering Design (Dym, 1997). Thus, I want to take this privileged moment to ask you to think with me about the role of design in engineering. In particular, I want to reflect upon how design is articulated and how design is taught; about the role of design in engineering education and in the practice of engineering; and about the role that could be played locally and, perhaps, nationally by a center devoted to design education. Because I teach here at Harvey Mudd College (HMC), and because most of you are visitors, I will place my remarks in our context by telling you about what we do here and how that doing has shaped my thinking.

The act of design is a complex of actions and abilities that is evolving and often highly individual. Given the context of human–computer interaction, and a commitment to the model of design space exploration, we identify two axes that help position efforts to realize this model: the spectrum of strengths and needs that stretches from the machine to the human, and the time scale of events in design. Considering a section of each reveals a landscape that prefers certain activities and gives rise to particular emphases. This paper places the other authors in this Special Issue upon this map, and argues the value of typed feature structures and information orderings to the endeavor of realizing design space explorers.

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.

The use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method builds on natural language processing to determine change candidates from textual data and uses dependency modeling to reveal direct and indirect change propagation paths arising from the change candidates. The method was applied in a case example where 3665 YouTube comments on a diesel engine were analyzed. Based on the results, two engine components were recommended for design change with six others predicted as likely to be affected through change propagation. The findings suggest that the method can be used to aid decision quality in product planning through a better understanding of the change impact associated with the opportunities identified.

Spatial grammars are rule-based, generative systems for the specification of formal languages. Set and shape grammar formulations of spatial grammars enable the definition of spatial design languages and the creation of alternative designs. The original formalism includes labels that provide the possibility to restrict the application of rules or to incorporate additional, nongeometric information in grammar rules. Labels have been used in various ways. This paper investigates the different uses of labels in existing spatial grammars, both paper based and computational, and introduces a new concept of three-dimensional (3-D) labels for spatial grammars. The approach consolidates the different label types in one integrated concept. The main use of 3-D labels is that they can simplify the matching of the left-hand side of rules in parametric grammars. A prototype implementation is used to illustrate the approach through a mechanical engineering example of generating robot arm concepts. This approach more readily enables the use of complex solid geometry in the definition and application of parametric rules. Thus, the flexible generation of complex, meaningful design solutions for mechanical engineering applications can be achieved using parametric spatial grammars combined with 3-D labels.

Documented engineering design rationale has the potential to become a key source of information about past designs. Ease of comprehension of design rationale might play a crucial role in ensuring that the full potential of documented information is realized and that the effort and time necessary to capture design rationale pay off. This research proposes an empirical methodology for evaluating how structuring design rationale and supplying it with visual nontextual cues influences reading and interpretation. The study compares reading and interpretation of technical documentation presented in different formats to engineering graduate trainees in the aerospace industry.

Embedding emergent parts in shape grammars is computationally challenging. The first challenge is the representation of shapes, which needs to enable reinterpretation of parts regardless of the creation history of the shapes. The second challenge is the relevant part searching algorithm that provides an extensive exploration of the design space–time efficiently. In this work, we propose a novel method to solve both problems; we treat shapes as they are and use a parallel particle swarm optimization-based algorithm to compute emergent parts. The execution time of the proposed method is improved substantially by dividing the search space into small parts and carrying out searches in each part concurrently using a graphics processing unit. The experiments show that the proposed implementation detects emergent parts accurately and time efficiently.

Creative design has been characterized in computational terms as “that design activity which occurs when a new variable is introduced into the design” (Gero, 1994). This is opposed to “routine design,” where “knowledge about variables, objectives expressed in terms of those variables, constraints expressed in terms of those variables, and the processes needed to find values for those variables, are all known a priori.” A third alternative, “innovative design,” occurs when no new variables are introduced, but when one or more variables are used with values outside the usual scope. In computational terms, routine design can also be seen as “search,” (sometimes also called “exploitation”) in so far as a certain, predefined search space is searched for a design solution. For creative design, where the search proceeds outside the boundaries of a predefined search space, the term “exploration” can be used.

This study demonstrates how subtle signals taken from the early stages within a construction process can be used to diagnose potential problems within that process. For this study, the construction process is modeled as a quasi-Markov chain. A set of six different scenarios representing various common problems (e.g., small budget, complex project) is created and simulated by suitably defining the transition probabilities between nodes in the Markov chain. A Monte Carlo approach is used to parameterize a Bayesian estimator. By observing the time taken to pass the review gateway (as measured by number of hops between activity nodes), the system is able to determine with good accuracy the problem scenario that the construction process is suffering from.

For every problem mentioned by crew members in an aircraft log book, an associated repair action note is entered in the same log book by a maintenance technician after the problem has been handled. These hand-written repair notes, subsequently transcribed into a database, give an account of the actions undertaken by the technicians to fix the problems. Written in a free-text format with peculiar linguistic characteristics, including many arbitrary abbreviations and missing auxiliaries, they contain valuable information that can be used for decision support methods such as case-based reasoning. We use natural language techniques in our information extraction system to analyze the structure and contents of these notes in order to determine the pieces of equipment involved in a repair and what was done to them. Lexical information and domain knowledge are extracted from an electronic version of the illustrated parts catalog for the particular airplane, and are used at different stages of the process, from the morpholexical analysis to the evaluation of the semantic expression generated by the syntactical analyzer.

Innovation and creativity are a mandatory for companies who wish to stay competitive. In order to promote an inventive dynamic, it implies to set up tools, habits, and an adapted environment to foster creativity. Creativity is the wealth of companies that should be valorized. To promote creativity, companies implement creativity workshops that gather people with various roles and expertise exchange and create knowledge to solve collectively open-ended engineering problems. However, group dynamics or facilitation can make the wrong decision and make the creative problem-solving unfruitful. The aim of our research project is to create a digital system to manage and valorize knowledge during creativity workshops. To design this system, we need to formalize the knowledge domain of creative workshops. The ontologies are used for decades to structure and manage information and knowledge in different domains. However, methodologies to design these ontologies are either hardly reproducible or not oriented to extract knowledge from organization. This article describes a methodology based on an organizational modeling to build ontologies. We will illustrate our approach by designing an ontology that models knowledge of creativity workshops.

The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.

During the 1980s, a community of artificial intelligence researchers became interested in formalizing problem solving methods (PSMs) as part of an effort called “second-generation expert systems.” We provide an example of how we are applying second-generation expert systems concepts in an agent-based system for space flight operations, the orbital communications adapter mirroring system (OCAMS), which was developed in the Brahms multiagent framework. Brahms modeling language provides an ontology for simulating work practices, including groups, agents, activities, communications, movements, and geographic areas. Activities are a behavioral unit of analysis to be contrasted with tasks, a functional unit of analysis. Problem solving occurs in the context of activities in the service of tasks; appropriate PSMs depend on the context: which people/roles are participating, what tools are available, how the results will be evaluated, and so forth. A work practice simulation facilitates designing workflow tools that appropriately interact with the physical and organizational context in which work occurs. OCAMS was developed using a simulation-to-implementation methodology, in which a prototype workflow tool was embedded in a Brahms simulation of how people would use the tool. The reusable components in a workflow system like OCAMS include entire “problem solvers” (e.g., a planning subsystem), interoperability frameworks, and agents that inspect and change the world. Thus, a tool kit for building workflow tools requires more than a library of PSMs, which play a relatively small role in the overall multiagent, systems-integration architecture. Our research concern has shifted to situations that may arise that are outside the OCAMS' capability. In practical decision making, people must reflect on the validity of their models. As programs becoming actors in the workplace, we need to develop systems that help people to understand the limitations of the models that drive the automated operations, which means in part detecting when the formalizations in the system are inadequate.

Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

Configuration of large-scale applications in an engineering context requires a modeling environment that allows the design engineer to draft the configuration problem in a natural way and efficient methods that can process the modeled setting and scale with the number of components. Existing configuration methods in artificial intelligence typically perform quite well in certain subareas but are hard to use for general-purpose modeling without mathematical or logics background (the so-called knowledge acquisition bottleneck) and/or have scalability issues. As a remedy to this important issue both in theory and in practical applications, we use a standard modeling environment like the Unified Modeling Language that has been proposed by the configuration community as a suitable object-oriented formalism for configuration problems. We provide a translation of key concepts of class diagrams to inequalities and identify relevant configuration aspects and show how they are treated as an integer linear program. Solving an integer linear program can be done efficiently, and integer linear programming scales well to large configurations consisting of several thousands components and interactions. We conduct an empirical study in the context of package management for operating systems and for the Linux kernel configuration. We evaluate our methodology by a benchmark and obtain convincing results in support for using integer linear programming for configuration applications of realistic size and complexity.

Product configuration is a major industrial application domain for constraint satisfaction techniques. Conditional constraint satisfaction problems (CCSPs) and feature models (FMs) have been developed to represent configuration problems in a natural way. CCSPs are like constraint satisfaction problems (CSPs), but they also include potential variables, which might or might not exist in any given solution, as well as classical variables, which are required to take a value in every solution. CCSPs model, for example, options on a car, for which the style of sunroof (a variable) only makes sense if the car has a sunroof at all. FMs are directed acyclic graphs of features with constraints on edges. FMs model, for example, cell phone features, where utility functions are required, but the particular utility function “games” is optional, but requires Java support. We show that existing techniques from formal methods and answer set programming can be used to naturally model CCSPs and FMs. We demonstrate configurators in both approaches. An advantage of these approaches is that the model builder does not have to reformulate the CCSP or FM into a classic CSP, converting potential variables into classical variables by adding a “does not exist” value and modifying the problem constraints. Our configurators automatically reason about the model itself, enumerating all solutions and discovering several kinds of model flaws.

Perspectives on research methodology in the field of “AI and design” are discussed. This perspective is based on a view of a “science of design” focusing on methods of design and on characteristics of design tasks that affect what methods are relevant for a given task. Two methodological issues are discussed: the need to try applying a design method on multiple tasks and domains, and the need to work with collaborators who are experts in the task domain of each research system you build.

To obtain optimal design efficiently in the initial design stage of a ship, a hybrid system is developed by integrating the optimization algorithm and knowledge-based system. The hybrid system can manipulate numeric and symbolic data simultaneously. To increase search efficiency in a design space, the optimization algorithm (optimizer) is implemented by coupling a genetic algorithm (GA) and search method. The optimizer determines a candidate region around the optimum point by using the GA, then searches the optimum point by the search method concentrating in this region, thus reducing calculation time and increasing search efficiency. To generate input data for the optimizer, a rule-based system is developed. Some domain knowledge for ship optimization in the initial design stage is retrieved from a database of existing ship and design experts. The obtained knowledge is stored in the knowledge base. The optimizer incorporates a knowledge-based system with heuristic and analytic knowledge, thereby narrowing the feasible space of the design variables. Therefore, search speed and the capability of finding an optimum point will be increased in comparison with conventional approach. The developed system is applied principally to particulars of optimization of ships with multicriteria. Through application ship design, it shows that the hybrid system can be a useful tool for optimum design.

An ongoing research effort is consolidating material and process knowledge in a critiquing system dealing with fuzzy criteria to aid designers in evaluating the incorporation of composite materials into their design. The extent of knowledge required to perform the task of evaluating composite processes and materials is often beyond the expertise of many design engineers as they lack understanding of the nature of composite material manufacturing. The system under development is known as the Composites Design and Manufacturing Critiquing System (CDMCS). The CDMCS critiques a submitted design through interaction with the user. An account of the strengths and weaknesses of the design is supplied to the user through the facilities. The current focus of the system is on process selection, but the system is generic so that other aspects of composite material manufacturing may be included. The system is implemented in Macintosh™ Common LISP. This article describes the features of the system that have been implemented. The system is currently being extended to cover more than the primary process component of the domain.