This paper presents a robust reliability analysis method for systems of multimodular redundant (MMR) controllers using the method of partitioning and parallel processing of a Markov chain (PPMC). A Markov chain is formulated to represent the N distinct states of the MMR controllers. Such a Markov chain has N2 directed edges, and each edge corresponds to a transition probability between a pair of start and end states. Because N can be easily increased substantially, the system reliability analysis may require large computational resources, such as the central processing unit usage and memory occupation. By the PPMC, a Markov chain's transition probability matrix can be partitioned and reordered, such that the system reliability can be evaluated through only the diagonal submatrices of the transition probability matrix. In addition, calculations regarding the submatrices are independent of each other and thus can be conducted in parallel to assure the efficiency. The simulation results show that, compared with the sequential method applied to an intact Markov chain, the proposed PPMC can improve the performance and produce allowable accuracy for the reliability analysis on large-scale systems of MMR controllers.

An approach to CAD and CAM modeling and to the design of CAD/CAM systems is presented. Models of the product and of the process are represented by logical assertions in a common logical language. CAD/CAM functions are represented by the application of logical inference rules, which correspond to the derivation of new information as well as to actions. This allows all the different kinds of model and specification used in design and manufacturing to be represented in a computer in a common form. It therefore allows the representation of constraints and rules connecting any aspects of design and manufacturing together.

This approach has all the advantages of formal specifiction, namely, ease of expression, communication, standardization and abstraction. At the same time, we demonstrate its practical implementation in an efficient form, and which is industry compatible, and we report practical experience with using this approach for CAD/CAM models and for intelligent CAD/CAM functions.

An MPR is a deep model of a manufacturing process. It is claimed to be easier to acquire than experiential diagnostic rules, and the acquisition of an MPR can be done either by a knowledge engineer or an intelligent interrogator program. An MPR can be used for simulating processes, for centralized or distributed model-based diagnosis of problems with processes, for designing the processes themselves, for determining the need for quality control testing and sensor checks, for determining when knowledge about the process is incomplete and additional knowledge needs to be acquired, and for compiling diagnostic rules.

Computer programs that combine traditional numeric methods with symbolic algebra and with specific knowledge of application-based techniques can provide new levels of computational support for engineering design. We illustrate this with a computer-based ‘control engineer’s assistant’. Although this program is focussed on control system design, it demonstrates techniques that should be widely applicable across many engineering disciplines. In particular, we show how, with symbolic computing, a computer-aided design system can usefully simulate engineering models early in the design process, before all (or any) system parameters have been specified numerically. Our system employs a flexible, extensible, object-oriented representation for control systems, which admits multiple mathematical models of designs and provides a framework for integrating tools that operate on diverse representations.

Case-based design (CBD) systems aim to solve a design problem by tailoring previously solved design problems to the current problem. Designers' specifications are used for indexing the knowledge base of the CBD system to retrieve an appropriate design case. Menu-based systems fail to capture designers' specifications effectively due to lack of expressiveness, while natural language systems are too immature to satisfy the goal. This paper presents the development of a graphical user interface (GUI) to implement a mechanical design specification language (MDSL) (Stelling, 1994) used to facilitate indexing in case-based mechanical design. The specification language is context-free and hence computable. It represents mechanical design knowledge in a (feature):(attribute) format suitable for indexing. An augmented transition network (ATN) parser is built using the grammar of the specification language. The parser provides syntactic as well as semantic checks. It also has capabilities to expand grammar and to adapt to a specific user domain. A graphical front end to the parser assists and guides the user through the specification language syntax in entering the design specifications. Provisions have been made to expand or edit the language grammar and vocabulary. The ATN parser was implemented in Common Lisp and the graphical user interface was written using the Gold Hill Windows Toolkit. Sample user interactions with the interface and screen dumps of the GUI are included.

A method is proposed for creative innovative design that is in concordance with the act of knowledge integration in learning; creative innovative design is defined as a guided (creative) process for arriving at an artifact that is socially valuable (practical and needed) and original (innovative). Within the context of models of reasoning, the process of design is interpreted and analyzed with a goal of extracting the stages at which it can be consciously improved by mindful control. A language is proposed for team-oriented intra- or interdisciplinary collaborative, as well as individual design that facilitates communication, mutual understanding, and makes explicit alternative nonverbal, nonquantitative thinking processes that otherwise may remain latent. The role of motivation in innovation is briefly discussed, as well as the role of artifact valuation in a societal context. Although not central to the present discussion, computer models of design are presented as an instance of design practice captured for computer-based design support. A brief discussion highlights the application and implications of the proposed method to education and research.

One of the biggest problems in pneumatic system design is over design. Thus, the results are excessive in costs of the initial investment and it requires too much energy. This article describes the development of expert system based pneumatic design system, PNEUDES (PNEUmatic Design Expert System), prototype that enables the user the optimal design of pneumatic system. Once the design requirements such as cylinder type and work load, etc. are input to the system, optimal cylinder specifications with standardized order-based size, valves types, and necessary accessories are all determined. Also the configuration information such as the connectivity among components and cylinder image data are supplied to the user. It can also help the novice of pneumatic design. The rule-based reasoning approach is used as a reasoning strategy with Intelligent Rule Element shell.

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

Style is a word that people think they understand. Most people recognize artifacts like buildings and clothes as being exemplars of particular styles, and they know words like Rococo and Art Deco as names for styles. They can recognize stylistic similarities not only in one sort of artifact but also across wide ranges of different things, such as buildings, furniture, artworks, clothes, music, and even manners. However, “style” is a slippery notion: the word has been used in a variety of senses since the ancient Greeks first thought about the differences in how people wrote or painted, and it is still used to refer to different things.

Metaphors are powerful tools for design, enabling designers to encapsulate sets of properties and relations as short verbal descriptions. This paper aims to clarify how simple spatial configurations may emerge from concise metaphoric descriptions at the conceptual design phase. To this aim, we propose a framework for a metaphor-based design process. As a basis for the framework, we introduce the concept of “complementary visual potential” – a property which ties the spatial configuration of the objects in the composition with their metaphoric roles. The framework is developed by studying the practice of metaphor-based spatial configuration design in Japanese rock gardens. Accordingly, it is implemented and tested in this context by attempting to generate alternative designs for an existing rock composition in the famous garden of Ryōan-ji. This is followed by a discussion of its possible implications and potential for generalization to other areas of design.

This paper introduces a method to improve the design procedure of axiomatic design theory (AD) with Extenics. A comprehensive review of the AD indicates that the powerful principle of AD has been widely studied and applied to many areas, however, inexperienced practitioners of the AD theory still find it difficult to follow or apply the principles in their design which inadvertently often leads to misunderstanding and skepticism. The lack of definitive descriptions for all the elements and specific approaches to guiding the mapping process restricts the development and application of AD theory. This paper improves the design procedure of AD with Extenics. The elements in AD domain are expressed by basic-elements of Extenics, and the formulations are generated. The mapping process based on AD and Extenics is developed. The improved design procedure provides designers with a theoretical foundation based on the logical and rational thought process, meanwhile the solution space can be expanded and innovative designs are inspired. Based on the proposed design procedure, a computer-aided system is developed, which makes the complex and fuzzy design activity clear and easy to follow by filling in the blanks in a step-by-step manner. An example of a novel corn harvester header design scheme is considered to illustrate the validity of the improved design procedure.

Low-carbon process planning is the basis for the implementation of low-carbon manufacturing technology. And it is of profound significance to improve process executability, reduce environmental pollution, decrease manufacturing cost, and improve product quality. In this paper, based on the perceptual data of parts machining process, considering the diversity of process planning schemes and factors affecting the green manufacturing, a multi-level evaluation criteria system is established from the aspects of processing time, manufacturing cost and processing quality, resource utilization, and environmental protection. An integrated evaluation method of low-carbon process planning schemes based on digital twins is constructed. Each index value is normalized by the polarized data processing method, its membership is determined by the fuzzy statistical method, and the combination weight of each index is determined by the hierarchical entropy weight method to realize the organic combination of theoretical analysis, practical experience, evaluation index, and process factors. The comprehensive evaluation of multi-process planning schemes is realized according to the improved fuzzy operation rules, and the best process planning solution is finally determined. Finally, taking the low-carbon process planning of an automobile part as an example, the feasibility and effectiveness of this method are verified by the evaluation of three alternative process planning schemes. The results show that the method adopted in this paper is more in line with the actual production and can provide enterprises with the optimal processing scheme with economic and environmental benefits, which may be helpful for more data-driven manufacturing process optimization in the future.

In an era where artificial intelligence (AI) permeates every facet of our lives, the imperative to steer AI development toward enhancing human wellbeing has never been more critical. However, the development of such positive AI poses substantial challenges due to the current lack of mature methods for addressing the complexities that designing AI for wellbeing poses. This article presents and evaluates the positive AI design method aimed at addressing this gap. The method provides a human-centered process for translating wellbeing aspirations into concrete interventions. First, we explain the method’s key steps: (1) contextualizing, (2) operationalizing, (3) designing, and (4) implementing supported by (5) continuous measurement for iterative feedback cycles. We then present a multi-case study where novice designers applied the method, revealing strengths and weaknesses related to efficacy and usability. Next, an expert evaluation study assessed the quality of the case studies’ outcomes, rating them moderately high for feasibility, desirability, and plausibility of achieving intended wellbeing benefits. Together, these studies provide preliminary validation of the method’s ability to improve AI design, while identifying opportunities for enhancement. Building on these insights, we propose adaptations for future iterations of the method, such as the inclusion of wellbeing-related heuristics, suggesting promising avenues for future work. This human-centered approach shows promise for realizing a vision of “AI for wellbeing” that does not just avoid harm, but actively promotes human flourishing.