Machine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

The present paper studies the process of information generation during design and focuses on the relationship between the information importance and the required effort for its generation. Multiple associative relationships among design entities (handled as design descriptors) are used to represent the design knowledge. The characteristics of the dependent and the primary descriptors are examined and their distinct roles in the design process are discussed. Term definitions concerning the information importance and the design effort are also introduced. The descriptors are used to form a matrix. A number of operations on this matrix results in its transformation, with the final matrix reflecting the quantitative relationship between the information importance and the design effort. From the aforementioned matrix, a unique sorted list for the primary design descriptors is produced. Following this list during descriptor instantiation ensures the production of design information of maximum importance with the least effort in the early design stages. The design of a belt conveyor is used as a basis for a better understanding of the theoretical analysis and for a demonstration of the use of the suggested descriptor list.

The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

Although there has been considerable computer-aided conceptual design research, most of the proposed approaches are domain specific and can merely achieve conceptual design of energy flows-processing systems. Therefore, this research is devoted to the development of a general (i.e., domain-independent) and knowledge-based methodology that can search in a wide multidisciplinary solution space for suitable solution principles for desired material-flow processing functions without designers' biases toward familiar solution principles. It first proposes an ontology-based approach for representing desired material-flow processing functions in a formal and unambiguous manner. Then a rule-based approach is proposed to represent the functional knowledge of a known solution principle in a general and flexible manner. Thereafter, a simulation-based retrieval approach is developed, which can search for suitable solution principles for desired material-flow processing functions. The proposed approaches have been implemented as a computer-aided conceptual design system for test. The conceptual design of a coin-sorting device demonstrates that our functional representation methodology can make the proposed computer-aided conceptual design system to effectively and precisely retrieve suitable solution principles for a desired material-flow processing function.

In human-centered design, user research drives design decisions by providing an understanding of end users. In practice, different people, teams, or even companies manage each step of the design process, making communication of user research results a critical activity. Based on an empirical study of current methods used by experts, this paper presents strategies for effectively communicating user research findings across organizational or corporate boundaries. To build researcher–client relationships, understand both user and client needs, and overcome institutional inertia, this paper proposes viewing user research clients as users of user research outcomes. This reframing of the crafting of communication across boundaries as a parallel internal human-centered design process we refer to as a double ethnography.

Although, the knowledge management (KM)-culture research has helped to validate the importance of cultural values for companies' KM initiatives and provided insights into some important values, it still lacks frameworks and analysis outlining how specific types of cultural values might relate to Knowledge Management system (KMS) adoption and subsequent outcomes. In this paper, we provide a three-dimensional framework to help managers articulate how culture affects their unit's ability to create, transfer, and apply knowledge through KMS use. To illustrate the application of the framework, we also present an exploratory case study we have performed in an international organization in the area of development assistance and capacity development.

Public perception of engineering recognizes its importance to national and international competitiveness, economy, quality of life, security, and other fundamental areas of impact; but uncertainty about engineering among the general public remains. Federal funding trends for education underscore many of the concerns regarding teaching and learning in science, technology, engineering, and mathematics subjects in primary through grade 12 (P-12) education. Conflicting perspectives on the essential attributes that comprise the engineering design process results in a lack of coherent criteria against which teachers and administrators can measure the validity of a resource, or assess its strengths and weaknesses, or grasp incongruities among competing process models. The literature suggests two basic approaches for representing engineering design: a phase-based, life cycle-oriented approach; and an activity-based, cognitive approach. Although these approaches serve various teaching and functional goals in undergraduate and graduate engineering education, as well as in practice, they tend to exacerbate the gaps in P-12 engineering efforts, where appropriate learning objectives that connect meaningfully to engineering are poorly articulated or understood. In this article, we examine some fundamental problems that must be resolved if preengineering is to enter the P-12 curriculum with meaningful standards and is to be connected through learning outcomes, shared understanding of engineering design, and other vestiges to vertically link P-12 engineering with higher education and the practice of engineering. We also examine historical aspects, various pedagogies, and current issues pertaining to undergraduate and graduate engineering programs. As a case study, we hope to shed light on various kinds of interventions and outreach efforts to inform these efforts or at least provide some insight into major factors that shape and define the environment and cultures of the two institutions (including epistemic perspectives, institutional objectives, and political constraints) that are very different and can compromise collaborative efforts between the institutions of P-12 and higher education.

Organizational research has shown that effectively structuring the resources (human, informational, computational) available to an organization can significantly improve its collective computational capacity. Central to this improved capacity is the manner in which the organization's member agents are related. This study is an initial investigation into the role and potential of interagent ties in computational teaming. A computational team-based model, designed to more fully integrate agent ties, is created and presented. It is applied to a bulk manufacturing process-planning problem and its performance compared against a previously tested agent-based algorithm without these agent relationships. The performance of the new agent method showed significant improvement over the previous method: improving solution quality 280% and increasing solution identification per unit time an entire order of magnitude. A statistical examination of the new algorithm confirms that agent interdependencies are the strongest and most consistent performance effects leading to the observed improvements. This study illustrates that the interagent ties associated with team collaboration can be a highly effective method of improving computational design performance, and the results are promising indications that the application of organization constructs within a computational context may significantly improve computational problem solving.

Although it is known that quantum computers can solve certain computational problems exponentially faster than classical computers, only a small number of quantum algorithms have been developed so far. Designing such algorithms is complicated by the rather nonintuitive character of quantum physics. In this paper we present a genetic programming system that uses some new techniques to develop and improve quantum algorithms. We have used this system to develop two formerly unknown quantum algorithms. We also address a potential deficiency of the quantum decision tree model used to prove lower bounds on the query complexity of the parity problem.

This paper discusses the issues related to retrieval of cases in a case-based design system. The limitations of the conventional approaches are brought out, and this is followed by a description of a new approach to indexing and retrieval. This approach aims at capturing control knowledge required for retrieval from individual retrieval examples. Since this approach is based on past retrieval examples, we call it RBEX (Retrieval Based on EXamples). The proposed approach is implemented in a prototype system called CADREM. CADREM uses design methods used in solving past design problems to solve new problems. This is closer to the derivational analogy approach rather than the more common transformational analogy. CADREM has been tried in the domain of conceptual structural design of buildings. Sample outputs from CADREM are also included.

The design community has growing familiarity with the concept of affordances and with the utility of this concept in many areas of design. Less emphasis has been placed on natural processes by which people acquire knowledge about affordances. Consequently, little is known about how design might be optimized to enable users to detect the actions that are available in a given human–machine system. We review scientific research about what people do to obtain information about affordances. We discuss implications of this research for design.

At its heart, design is a decision-making process. These decisions, and the reasons for making them, comprise the design rationale (DR) for the designed artifact. If available, DR provides a comprehensive record of the reasoning behind the decisions made during the design. Unfortunately, while this information is potentially quite valuable, it is usually not explicitly captured. Instead, it is often buried in other design and development artifacts. In this paper, we study how to identify rationale from text documents, specifically software bug reports and design discussion transcripts. The method we examined is statistical text mining where a model is built to use document features to classify sentences. Choosing which features are most likely to be good predictors is important. We studied two evolutionary algorithms to optimize feature selection – ant colony optimization and genetic algorithms. We found that for many types of rationale, models built with an optimized feature set outperformed those built using all the document features.

Exploration with a generative formalism must necessarily account for the nature of interaction between humans and the design space explorer. Established accounts of design interaction are made complicated by two propositions in Woodbury and Burrow's Keynote on design space exploration. First, the emphasis on the primacy of the design space as an ordered collection of partial designs (version, alternatives, extensions). Few studies exist in the design interaction literature on working with multiple threads simultaneously. Second, the need to situate, aid, and amplify human design intentions using computational tools. Although specific research and practice tools on amplification (sketching, generation, variation) have had success, there is a lack of generic, flexible, interoperable, and extensible representation to support amplification. This paper addresses the above, working with design threads and computer-assisted design amplification through a theoretical model of dialogue based on Grice's model of rational conversation. Using the concept of mixed initiative, the paper presents a visual notation for representing dialogue between designer and design space formalism through abstract examples of exploration tasks and dialogue integration.

In designing a state space of possible designs is implied by the representation used and the computational processes that operate on that representation. GAs are a means of effectively searching that state space which is defined by the length of the genotype's bit string. Of particular interest in design computing are processes that enlarge that state space to change the set of possible designs. This paper presents one such process based on the generalization of the genetic crossover operation. A crossover operation of genetic algorithms is reinterpreted as a random sampling of interpolating phenotypes, produced by a particular case of phenotypic interpolation. Its generalization is constructed by using a more general version of interpolation and/or by adding extrapolation to interpolation. This generalized crossover has a potential to move the current population outside of the original state space. An adaptive strategy for state space enlargement, which is based on this generalization, is designed. This strategy can be used for computational support of creative designing. An example is given.

A generalized approach to fast interpretation of objects and their features has so far eluded researchers. In manufacturing, this interpretation can be approached from the vision point of view or from the CAD data perspective. Presently, CAD systems are widely used in several aspects of manufacturing production. It is therefore more efficient to use CAD data for object reasoning in manufacturing, especially when systems will eventually be data driven. Components can be modelled on a CAD system using various modelling techniques and the representation of their geometric information is still CAD system dependent. However, the advent of the Initial Graphics Exchange Specification (IGES) now makes it possible to represent CAD data in a neutral and standard manner.

This paper describes a scheme for recognizing and representing features for CAD data extracted using the IGES interface. The concepts developed are based on graph-based feature representation, where features are represented by a set of faces as well as their topological adjacency.

Strategies for classifying features and methods of decomposing a complicated feature into several simpler features for recognition purposes are discussed.

Achieving the dimensional integrity for a complex structural assembly is a demanding task due to the manufacturing variations of parts and the tolerance relationship between them. Although assigning tight tolerances to all parts would solve the problem, an economical solution is taking advantage of small motions that joints allow, such that critical dimensions are adjusted during assembly processes. This paper presents a systematic method that decomposes product geometry at an early stage of design, selects joint types, and generates subassembly partitioning to achieve the adjustment of the critical dimensions during assembly processes. A genetic algorithm generates candidate assemblies based on a joint library specific for an application domain. Each candidate assembly is evaluated by an internal optimization routine that computes the subassembly partitioning for optimal in-process adjustability, by finding a series of minimum cuts on weighted graphs. A case study on a three-dimensional automotive space frame with the accompanying joint library is presented.

Shape grammars come in a variety of forms. Algebras of shapes have been defined for spatial elements of different kinds, as well as for shapes augmented with varying attributes, allowing for grammar forms to be expressed in terms of a direct product of basic algebras. This algebraic approach is extended here to the algebraic derivation of combinations of basic shape algebras with attribute algebras. This algebraic abstraction at the same time serves as a procedural abstraction, giving insights into the modular implementation of a general shape grammar interpreter for different grammar forms. In addition, we consider practical limitations on algebraic compositions of basic shape algebras with attribute algebras and identify a complication with respect to solving the matching problem for parallel and compound shape grammars, suggesting a way to address this complication.

Prior research has identified set-based design as a method that accounts for the high level of uncertainty that is associated with the design of innovative products or systems. Rather than precisely specifying a system architecture in the early design stages, set-based design builds on designing a system and its architecture in an evolutionary way. The literature on set-based design has studied how a system's design evolves by moving from a number of optional design ideas to the final system through gradually eliminating unfeasible design ideas and continually developing design ideas for which engineers increasingly establish feasibility. However, little is known about how firms set up the design process and the organization to successfully create new products with set-based design. Our research contributes to closing this gap. First, we study how firms determine the number (i.e., portfolio) of design ideas to pursue, an important step of the early design process. Second, we study how firms organize for set-based design by assigning teams to develop design ideas and eventually design a system's architecture. Our research uses an exploratory case study approach, investigating five cases in three different firms. First, we find that the early design process is characterized by the absence of formal idea evaluation and selection. Instead, firms start to pursue all initially created design ideas, evaluating and selecting them in an evolutionary manner as the design project progresses. Second, we identify two organizational approaches associated with set-based design: assign one team to pursue all ideas or assign one team per design idea.

This paper presents a conceptual and computational framework to support experts in the design and manufacturing of high quality products. The framework is based on the development of specific knowledge artifacts characterized by tools for the management of functional, procedural, and experiential knowledge. As a case study, the GUITAR HERO project is presented. The project aims at building a knowledge-based system to support experts of a handicraft enterprise involved in the design and manufacturing of electric guitars characterized by an aluminum body. The domain of the project is extremely innovative, because electric guitars are typically manufactured with different kinds of wood rather than metals or other materials. To this aim, an ontological representation of the electric guitar has been implemented exploiting NavEditOW, a computational framework for the codification, navigation, and querying of ontologies over the Internet, based on the OWL language.