Human designers often adopt strategies from previous similar cases to guide their search in new design tasks. We have developed an approach for automated design strategy capture and reuse. That approach has been implemented in DDIS, a prototype structural design system that uses a blackboard framework to integrate case-based and domain-based reasoning. Plans, goals, and critical constraints from user-selected previous cases are combined with case-independent reasoning to solve underconstrained parametric structural design problems. This article presents a detailed example of design strategy recording and reuse in base plate design for electrical transmission poles.

Many of the most fundamental decisions about a product are made during the system architecture design process. However, how system architecture is designed in practice is not well understood. This paper draws on several research studies related to system architecture design to develop a categorization of system architecture design processes to support the adaptation design methodologies and tools to specific situations. The paper reviews different definitions of system architecture and comments on the relevance of the different perspectives taken in the literature on system architecture to different types of system architecture. The research highlights the need for further empirical research on system architecture design processes as well as on tools to support the engineers creating the system architecture.

Today, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together.

To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering entities (SA), the ordinary differential equations for describing the rate of affected entities with respect to the total ones and the duration for resolving all the changes for every moment are presented by combining the calculations of change impacts with different split and joint junctions. Considering the difficulty of finding analytical solutions to the differential equations, algorithms for sequential and concurrent simulations of change propagations across different development stages, and random and GA (Genetic Algorithm)-based optimal selections of feasible propagation paths are developed to obtain numerical solutions for single and multiple change requests. Simulation results show that change ripples and blossoms can be observed in both sequential and concurrent change propagations, and these propagation patterns are not sensitive to the initial change effect and the threshold value for propagations, while critical change propagation paths and the number of initiated changes have important effects on both concurrent and sequential change propagation process. It is also demonstrated that concurrent propagation strategy is advantageous for processing single or few of initiated changes since it can shorten product redevelopment time, sequential propagation strategy has an advantage of robustness for handling multiple initiated change requests.

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system can simultaneously classify, detect, and segment fillers in SEM images, making it suitable for morphology analysis of fillers and automatic quality inspection. We also propose a novel SEM image simulation procedure to overcome the data scarcity for training a deep CNN architecture. The proposed filler detection system is trained on the simulated images. It is shown that the trained network can detect and segment fillers with higher accuracy even in the overlapping and obscure situations. The performance and robustness of the proposed system are evaluated using both simulated and real microscopic images.

Since the inception of research on machine learning (ML), these techniques have been associated with the task of automated knowledge generation or knowledge reorganization. This association still prevails, as seen in this issue. When the use of ML programs began to attract researchers in engineering design, different existing tools were used to test their utility and gradually, variations of these tools and methods have sprung up. In many cases, the use of these tools was based on availability and not necessarily applicability. When we began working on ML in design, we attempted to follow a different path (Reich, 1991a; Reich & Fenves, 1992) that led to the design of Bridger (Reich & Fenves, 1995), a system for learning bridge synthesis knowledge. Subsequent experiences and further reflection led us to conclude that the process of using ML in design requires careful and systematic treatment for identifying appropriate ML programs for executing the learning tasks we wish to perform (Reich, 1991b, 1993a). Another observation was that the task of creating or reorganizing knowledge for real design tasks is outside the scope of present ML programs. Establishing the practical importance of ML techniques had to start by addressing engineering problems that could benefit from present ML programs.

Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions. We present a rule-based technique for intelligently computing gradients in the presence of such pathologies in the simulators, and show how this gradient computation method can be used as part of a gradient-based numerical optimization system. We tested the resulting system in the domain of conceptual design of supersonic transport aircraft, and found that using rule-based gradients can decrease the cost of design space search by one or more orders of magnitude.

The authors have been involved in ontological modeling of function for over 15 years. As an instance of the revisionary approach discussed in Vermaas's position paper, we have proposed an ontological definition of function and a modeling framework based on it, which has been deployed in industry. In addition, as an instance of the overarching approach, we have proposed a reference ontology of function that explains some kinds, definitions, and practical expressions of functions. In this paper, we explain our methodology in an overarching approach based on perspectives for capturing functions. When one captures a function of an artifact, one focuses on a specific aspect of the artifact from a specific perspective. In this paper, we conceptualize such perspectives behind the reference ontology. In addition, based on our experiences in deployment in an industrial setting, we report some solutions, such as ontological modeling guidelines, for overcoming some of the difficulties faced in the practical functional modeling approach described in Eckert's position paper. Our findings suggest that such solutions will help engineers to describe consistent functional models compliant with a single definition of function.

An efficient single-layer dynamic semisupervised feedforward neural network clustering method with one epoch training, data dimensionality reduction, and controlling noise data abilities is discussed to overcome the problems of high training time, low accuracy, and high memory complexity of clustering. Dynamically after the entrance of each new online input datum, the code book of nonrandom weights and other important information about online data as essentially important information are updated and stored in the memory. Consequently, the exclusive threshold of the data is calculated based on the essentially important information, and the data is clustered. Then, the network of clusters is updated. After learning, the model assigns a class label to the unlabeled data by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and density of each cluster are updated. The accuracy of the proposed model is measured through the number of clusters, the quantity of correctly classified nodes, and F-measure. Briefly, in order to predict the survival time, the F-measure is 100% of the Iris, Musk2, Arcene, and Yeast data sets and 99.96% of the Spambase data set from the University of California at Irvine Machine Learning Repository; and the superior F-measure results in between 98.14% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center. We show that the proposed method is applicable in different areas, such as the prediction of the hydrate formation temperature with high accuracy.

Class-centered data models, such as the object-oriented data model, are inadequate for supporting engineering design product models because of their lack of support for object evolution, schema evolution, and semantic and user-defined relationships. Description logic overcomes these limitations by providing constructs for intentional description of classes, relationships, and objects. By combining description logic with object-oriented modelling concepts, design product schemas and data can be uniformly represented and modified throughout the design process.

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.

Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

The goal of this paper is to develop and test a gamified design thinking framework, including its pedagogical elements, for supporting various learning objectives for school students. By synthesizing the elements and principles of design, learning and games, the authors propose a framework for a learning tool for school students to fulfil a number of learning objectives; the framework includes a design thinking process called “IISC Design Thinking” and its gamified version called “IISC DBox”. The effectiveness of the framework as a learning tool has been evaluated by conducting workshops that involved 77 school students. The results suggest that the gamification used had a positive effect on the design outcomes, fulfilment of learning objectives, and learners' achievements, indicating the potential of the framework for offering an effective, gamified tool for promoting design thinking in school education. In addition to presenting results from empirical studies for fulfilment of the objectives, this paper also proposes an approach that can be used for identifying appropriate learning objectives, selecting appropriate game elements to fulfil these objectives, and integrating appropriate game elements with design and learning elements. The paper also proposes a general approach for assessing the effectiveness of a gamified version for attaining a given set of learning objectives. The methodology used in this paper thus can be used as a reference for developing and evaluating a gamified version of design thinking course suitable not only for school education but also for other domains (e.g., engineering, management) with minimal changes.

An engineering service project can be highly interactive, collaborative, and distributed. The implementation of such projects needs to generate, utilize, and share large amounts of data and heterogeneous digital objects. The information overload prevents the effective reuse of project data and knowledge, and makes the understanding of project characteristics difficult. Toward solving these issues, this paper emphasized the using of data mining and machine learning techniques to improve the project characteristic understanding process. The work presented in this paper proposed an automatic model and some analytical approaches for learning and predicting the characteristics of engineering service projects. To evaluate the model and demonstrate its functionalities, an industrial data set from the aerospace sector is considered as a the case study. This work shows that the proposed model could enable the project members to gain comprehensive understanding of project characteristics from a multidimensional perspective, and it has the potential to support them in implementing evidence-based design and decision making.

Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.

Integrated design synthesizes combinations of options into alternatives that take advantage of interactions to maximize multidisciplinary value. As resources become further constrained, options become more numerous, and goals become increasingly complex, it is more critical and more challenging for design teams to find these integrated solutions. Theory proposes the integration of transformation, flow, and value views as necessary to support such integrated design. This paper develops requirements for these views that encourage flexible yet systematic integrated conceptual design processes. It then illustrates how these requirements are only partially satisfied by current design management systems, provides motivating case studies, and introduces a new framework, multiattribute interaction design (MAID), to fill this void by systematically guiding design teams to explicitly consider the potential interactions of options and the resulting value of design solutions. The paper defines the terms relevant to design space exploration and interactions. It then defines the MAID method and specifies metrics and a process for its validation. Initial laboratory charettes carry out first validations, illustrating evidence for how MAID can help integrate transformation, flow, and value views and lead teams of students to discover and record more interactions in a relatively short amount of time. The paper then lists future work required to further develop and validate MAID.

This paper defines, develops algorithms for, and illustrates the utility in design of a class of mathematical operations. These accept as inputs a system of linear constraint equations, Ax = b, an interval matrix of values for the coefficients, A, and an interval vector of values for either x or b. They return a set of values for the “domain” of the other vector, in the sense that all combinations of the output vector values set and values for A, when inserted into the constraint equation, correspond to values for the input vector that lie within the input interval. These operations have been mostly overlooked by the interval matrix arithmetic community, but are mathematically interesting and useful in the design, for example, of structures.

This paper shows the way Leon Battista Alberti treatise De Re Aedificatoria column systematization shape grammar was built, and how the longitudinal elevation of the nave of the Sant'Andrea church was designed, evidencing some steps involved in the grammar construction process, its derivation, and the partial outputs gained from the grammar evaluation. The goal of this grammar is to infer Alberti's treatise rules through a computational approach evidencing its algorithmic nature, illustrating it, and understanding the De Re Aedificatoria rules that may be applied in the generation of some parts of buildings such as the Sant'Andrea church, showing a complementary method to archaeological, bibliographical, historical ones, among others, that have been used to understand the built environment. The rules provided by the grammar may be applied to an initial insertion point to produce a complete column and to a pre-existing plan or section element of a building generating its facade. It was proved, evaluating the grammar with a simple linear regression method, to what extent the treatise influenced the design of the facade in the study.

This paper presents a formal analysis of design process dynamics. Such a formal analysis is a prerequisite to come to a formal theory of design and for the development of automated support for the dynamics of design processes. The analysis was geared toward the identification of dynamic design properties at different levels of aggregation. This approach is specifically suitable for component-based design processes. A complicating factor for supporting the design process is that not only the generic properties of design must be specified, but also the language chosen should be rich enough to allow specification of complex properties of the system under design. This requires a language rich enough to operate at these different levels. The Temporal Trace Language used in this paper is suitable for that. The paper shows that the analysis at the level of a design process as a whole and at subprocesses thereof is precise enough to allow for automatic simulation. Simulation allows the modeler to manipulate the specifications of the system under design to better understand the interlevel relationships in his design. The approach is illustrated by an example.