Design Of Knowledge-based System Research Articles

Recent test experiences with applying multiple unshrouded impellers in a single-shaft compressor

The use of unshrouded impellers to increase the pressure ratio and volume reduction achieved in low mole weight, single-shaft compressor applications is discussed. The aerodynamic and mechanical implications of using multiple unshrouded impellers in series is also addressed. Test results will show that with proper management of impeller tip gaps good aerodynamic performance can be achieved with three unshrouded impellers in a single process section. The development effort also leveraged a knowledge-based impeller design system to provide new impeller designs that were optimized as a function of Mach number to provide a 30% axially shorter section without compromising aerodynamic performance. The shorter axial stages in the first section pushed the rotor natural frequency 20% higher, enabling faster rotor tip speeds. Test results from a single-stage test rig and from a full-size prototype testing are provided and generally show good agreement.

Knowledge-based system for aircraft design

This article, based on the analysis and the theoretical approaches of the subject area "Design of aircraft at the sketch stage" proposed a method of aircraft design, which allows mathematically sufficiently effective implementation of the process of aircraft construction, on the example of aircraft using an ontological approach. The approach allows us to identify ways to formalize the two main situations that occur in the design of aircraft: sequential complication and refinement (descending design) and consistent simplification (ascending design). The proposed technology allows to significantly reducing the total design time of the aircraft through the use of several levels of model decomposition.
 The application of the ontological approach facilitates the design process, allows solving the main tasks of modern design at different stages of aircraft creation, to coordinate the parallel work of designers on the project.
 The main advantages of using the ontology design of the developed aircraft are:
 1) effective compact presentation of the knowledge system of the subject area "Design of aircraft" on the basis of modern information technologies (specification and conceptualization);
 2) search for information in the system of knowledge of the obtained ontology (obtaining reference and educational information);
 3) formulation and solution of the necessary applied tasks within the given subject area (tasks of weight design, centering of the aircraft);
 4) development of the system and acquisition of new knowledge or streamlining of existing ones, verification of their consistency, correction of the aircraft design tree.

Integrated framework of knowledge-based decision support system for user-centered residential design

A user-oriented residential environment is considered essential to providing occupants with a comfortable living experience while meeting their demands. However, design assessment in the early stage of residential design is a complex problem due to multifactor coupling, the diversity in user needs, and the incompleteness of design knowledge. This study proposes an integrated framework of knowledge-based decision support system (KBDSS) to mitigate such problems. In this framework, the user segmentation is performed in accordance with people’s particular requirements priority and, thus, enables design decisions that create better satisfaction among a wider audience. Meanwhile, the quality function deployment approach, decision support system, and fuzzy set theory are used to translate user requirements into quantifiable design specifications, thereby making the design a multicriteria decision-making problem. To illustrate and validate the proposed framework, a case study of a KBDSS for a multiunit residential building kitchen design is demonstrated to explore a user-centered residential design with such decision support. This proposed framework provides naive design practitioners with specific knowledge about decision problems and assists them in consistently making user-centered design decisions. The research results represent a further step toward a systematic methodology of user-centered built environment design.

Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process

The requirements of future aeroengines challenge turbomachinery designs to be quieter, greener, and more efficient; furthermore, they must be developed at considerably reduced cycles and costs. Traditional turbomachinery design systems involve the iterative design–evaluation–improvement process, leading to high computational costs and considerable reliance on expert experience. In this paper, a knowledge-based turbomachinery aerodynamic design system consisting of a design of experiment framework and a two-stage modeling is proposed. In the first phase of the two-stage modeling, a deep neural network (DNN)-enhanced multi-output Gaussian process model is constructed. This model determines the optimal loading distribution from reference designs that are optimized for the desired performance. In the second stage, a DNN model is developed to predict the turbine profile geometry for a given loading distribution. This formulation enables the automatic determination of the optimal turbine geometry without an iterative process. Furthermore, the knowledge in the design system grows with the increasing database. The results indicate that the L2 relative errors for predicting the optimal pressure coefficient distribution and turbine profile geometry are less than 0.25% and 1.7%, respectively. A design case of a low-pressure turbine is used to validate the entire design system. The results reveal that the turbine geometry generated by the proposed design system outperforms that of the benchmark case in terms of the design and off-design aerodynamic performance. The foregoing observations are finally confirmed through experiments.

PROTOTYPE DEVELOPMENT OF KNOWLEDGE-BASED SYSTEM FOR LOW-VOLUME RURAL ROADS IN SARAWAK

The value of rural roads cannot be overstated in terms of a society’s economic and social development. Road construction has the potential to alter natural terrain, cause widespread environmental damage and result in significant land-use and cultural changes. Roads must be constructed in accordance with appropriate design specifications in order to balance the needs, costs and environmental impacts of road users. There are several reasons for scarcity in technical skills and technological development of Low-Volume Rural Roads (LVRRs). Universities, colleges, polytechnics and technical schools do not provide LVRR-focused education. Low-tech LVRRs skills are often acquired through years of experience and are lost due to retirement and downsizing. This article discusses the development of a conceptual knowledge-based system for low-volume rural road design. The prototype system has been developed for demonstration purposes and is not ready for implementation. This prototype system simulates a portion of a pavement expert’s or specialist’s technical reasoning skills, which it can then use to help with road design and improve the efficiency and precision of engineers tasked with evaluating all potential designs. As a result, the system increases the engineers’ efficiency in evaluating, assessing and modifying information that will aid decision makers throughout the project, thereby increasing the effectiveness of making the right decision at the right time.

Uncharted Aspects of Human Intelligence in Knowledge-Based “Intelligent” Systems

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems (KBIS) design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes.

Integrated Framework of Knowledge-Based Decision Support System for User-Centered Residential Design

Integrated Framework of Knowledge-Based Decision Support System for User-Centered Residential Design

Knowledge Service Technology for Supporting Intelligent Product Design

In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.

A new method for the design of knowledge-based engineering systems for manufacturing

A new method for the design of knowledge-based engineering systems for manufacturing

An Ontology-Based Framework for Decision Support in Assembly Variant Design

Abstract The designer generates a variant product by applying several design suggestions that fulfilled a variety of customer requirements. These design suggestions rely on multiple domains of expert knowledge, which are unstructured and implicit. Moreover, these design suggestions have an impact on assembly joint information (liaison), which makes the variant design a complex problem. To effectively support the designers, this work presents a knowledge-based decision support system for assembly variant design using ontology. First, a knowledge base is built by the development of an ontology to formally represent the taxonomy, properties, and causal relationships of/among core concepts involved in the variant design. Second, a five-step sequential procedure is established to facilitate the utilization of this knowledge base for decision-making in variant design. The procedure takes the extracted liaison information from the CAD model of an existing product as the input and further used for generating a set of variant design decisions as the output through Semantic Web Rule Language (SWRL) rule-based reasoning. The inferred outputs by the process of reasoning are the design suggestions, the variant design type required for each design suggestion, and its effect on joint information. Based on the evaluation of the ontology, the precision, recall, and F-measure obtained are 79.3%, 82.1%, and 80.67%, respectively. Finally, the efficacy of the knowledge-based decision support system is evaluated using case studies from the aerospace and automotive domain.

Unified modeling for digital twin of a knowledge-based system design

• Integrating digital twin (DT) with model-based systems engineering (MBSE) for the system design complexity analysis and prediction. • A system design digital twin in 5 dimensions (SDDT 5D ) framework is introduced, which is able to map the actual system design process into the corresponding virtual space . • The core concepts and relationships of SDDT 5D are customized by a SysML profile that keeps every view of the system digital model unified. • The system model reduces ambiguity through unifying system requirements, functions, and structures and behaviors. While Model-Based Systems Engineering (MBSE) improves the ambiguity problem of the conventional document-based way, it brings management complexity. Faced with the complexity, one of the core issues that companies care about is how to effectively evaluate, predict, and manage it in the early system design stage. The inaccuracy of contemporary complexity measurement approaches still exits due to the inconsistency between the actual design process in physical space and the theoretical simulation in virtual space. Digital Twin (DT) provides a promising way to alleviate the problem by bridging the physical space and virtual space. Aiming to integrate DT with MBSE for the system design complexity analysis and prediction, based on previous work, an integration framework named System Design Digital Twin in 5 Dimensions was introduced from a knowledge perspective. The framework provides services for design complexity measurement, effort estimation, and change propagation prediction. Then, to represent the system design digital twin in a unified way, a modeling profile is constructed through SysML stereotypes. The modeling profile includes System design digital model in virtual space profile, system services profile, relationships profile and digital twin data profile. Finally, the system design of a cube-satellite space mission demonstrates the proposed unfiled modeling approach.

Design of knowledge-based systems for automated deployment of building management services

Despite its high potential, the building's sector lags behind in reducing its energy demand. Tremendous savings can be achieved by deploying building management services during operation, however, the manual deployment of these services needs to be undertaken by experts and it is a tedious, time and cost consuming task. It requires detailed expert knowledge to match the diverse requirements of services with the present constellation of envelope, equipment and automation system in a target building. To enable the widespread deployment of these services, this knowledge-intensive task needs to be automated. Knowledge-based methods solve this task, however, their widespread adoption is hampered and solutions proposed in the past do not stick to basic principles of state of the art knowledge engineering methods. To fill this gap we present a novel methodological approach for the design of knowledge-based systems for the automated deployment of building management services. The approach covers the essential steps and best practices: (1) representation of terminological knowledge of a building and its systems based on well-established knowledge engineering methods; (2) representation and capturing of assertional knowledge on a real building portfolio based on open standards; and (3) use of the acquired knowledge for the automated deployment of building management services to increase the energy efficiency of buildings during operation. We validate the methodological approach by deploying it in a real-world large-scale European pilot on a diverse portfolio of buildings and a novel set of building management services. In addition, a novel ontology, which reuses and extends existing ontologies is presented.

An interactive knowledge-based recommender system for fashion product design in the big data environment

In this paper, we originally propose an interactive, knowledge-based design recommender system (IKDRS) for relevant personalised fashion product design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the designer’s professional knowledge and perception in order to find the best existing design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and designer’s perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire design knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and design factors by using fuzzy techniques. From these models, we set up an ontology-based design knowledge base, including key data and relevant relation models. This knowledge base can be updated in a big data environment by progressively learning from new design cases. On this basis, we propose an interactive, personalised design recommender system. This system works through a newly proposed design process: consumers’ emotional requirement identification – design schemes generation – recommender – 3D virtual prototype display and evaluation – design factors adjustment. This process can be performed repeatedly until the designer is satisfied. The proposed system has been validated through a number of successful real design cases.

A knowledge-based material selection system for interactive pressure vessel design

Continual introduction of new materials and improvements in existing materials increase the variety of materials that can be used for pressure vessel components. Among wide variety of materials, the most suitable one must be selected for a component by matching its functional requirements with various available materials’ specifications. This study proposes an interactive knowledge-based decision support system for selecting the most suitable material for a given pressure vessel component and its working environment. The developed decision support system, namely Pressure Vessel SELection (PVSEL), consists of two separate phases. In the first elimination phase of PVSEL, the user obtains a feasible set of alternative materials by answering various questions and providing lower-limit values at materials’ critical specifications. PVSEL, then, uses a ranking phase which uses ELECTRE, TOPSIS and VIKOR methods to rank the feasible materials. In the second phase, each alternative material’s ranking is determined by combining its performance values at weighted critical specifications (selection criteria), which are considered as important in meeting the functional requirements of the component. Usage of PVSEL is illustrated in the paper and the results show that the proposed PVSEL is an effective selection tool and provides meaningful results for the designers.

Rapid Design System for Agricultural Machinery Using Knowledge-Based Engineering: Case Study of a Wheeled Combine Chassis

HighlightsA knowledge-based rapid design system for combine chassis is proposed.Object-oriented knowledge representation uniformly describes the multi-source heterogeneous design knowledge.Multivariate linear regression analysis ratiocinates further unknown design principles behind existing products.Abstract. In the design of agricultural machinery, the lack of systematic and structured knowledge utilization systems results in low development efficiency and poor reusability of design knowledge. This study considers the design of a combine chassis and focuses on the application of knowledge-based rapid design to agricultural machinery. As a typical sophisticated agricultural machine, combines have a complicated design that requires the integration of multiple disciplines according to the crop types, production areas, and ability of the designers. Hence, a knowledge-based design system for a wheeled combine chassis is proposed. By integrating object-oriented technology with a mixed reasoning strategy, multi-source heterogeneous design knowledge can be applied in completing the chassis design task. To date, there are more than 2000 design ideas and 350 parameter models in the knowledge database. In addition, tacit design knowledge acquired by a knowledge discovery method can be employed to perfect the design and improve the reliability of the product. The design of a combine chassis demonstrates that the knowledge-based rapid design of agricultural machinery is completely feasible, and the design system significantly reduces the design workload, shortens the development cycle, and improves design efficiency and knowledge utilization. Keywords: Combine chassis, Knowledge-based design system, Knowledge-based engineering, Rapid design.

Rapid Design System for Agricultural Machinery Employing Knowledge-Based Engineering: Case Study of A Wheeled Combine Chassis

Abstract. Highlights A knowledge-based rapid design system for combine chassis is proposed. The object-oriented knowledge representation uniformly describes the multi-source heterogeneous design knowledge. The multivariate linear regression analysis ratiocinates further unknown design principles behind existing products. Abstract. In the design of agricultural machinery, the lack of systematic and structured knowledge utilization systems results in low development efficiency and poor reusability of design knowledge. This paper considers the design of a combine chassis, and focuses on the application of knowledge-based rapid design to agricultural machinery. As a typical sophisticated agricultural machine, combines have a complicated design that requires the integration of multiple disciplines according to the crop types, production areas, and ability of the designers. Hence, a knowledge-based design system for a wheeled combine chassis is proposed. By integrating object-oriented technology with a mixed reasoning strategy, multi-source heterogeneous design knowledge can be applied in completing the chassis design task. To date, there are more than 2000 design ideas and 350 parameter models in the knowledge database. In addition, tacit design knowledge acquired by a knowledge discovery method can be employed to perfect the design and improve the reliability of the product. The design of a combine chassis demonstrates that the knowledge-based rapid design of agricultural machinery is completely feasible, and the design system significantly reduces the design workload, shortens the development cycle, and improves design efficiency and knowledge utilization.

Human Facial Disease Diagnosis System Using Dempster Shafer Method

Skin is a very important and sensitive part of the human body. It is directly related to the outside world. In everyday life, the skin plays an important role. One of its functions is to remove waste substances. The lack of knowledge and limited sources of information causes low public awareness of skin health as well as not seeing a doctor. Regular skin checks can overcome the beginning of skin diseases that might trigger other more dangerous diseases. The system in this study is designed to detect and to know the symptoms of facial skin diseases that are felt. This knowledge-based system design was developed to diagnose human facial skin disease using the dempster shafer method. The results can help detecting earlier facial skin diseases and overcoming them.
 Key words : diagnosis system, facial skin disease, dempster-shafer

A knowledge-based system for numerical design of experiments processes in mechanical engineering

This paper describes a specific knowledge-based system (KBS) to assist designers in configuring numerical design of experiments (NDoE) processes efficiently. NDoE processes are applied in product design to improve the quality of product, by taking into account variabilities and uncertainties. NDoE processes are defined by various and complex methodologies to achieve several objectives, as optimization, surrogate modeling or sensitivity analysis. On the other hand, NDoE processes may demand huge computing resources to execute hundreds simulations, and also advanced expert knowledge to set the best configuration amongst numerous possibilities. Designers aim to obtain most useful results with a minimal computational cost as soon as possible. Thus, the configuration step must be as fast as possible, and it must lead to an efficient combination of complex methods, algorithms and hyper-parameters, to obtain valuable information on the product. The proposed KBS and its inference engine, a bayesian network, is detailed and applied to a product developed by automotive industry. The KBS propose new efficient configurations to achieve designers' goal. This application shorten the configuration step of the NDoE process, and enables designers to use more complex methods. It also allows designers to capitalize knowledge and learn from each past NDoE process.

Knowledge-based active push system for ecological design

With the arrival of the era of big data, large amounts of information need to be stored and computed. The complicated data analysis makes it impossible to extract, select and apply knowledge of eco-design in a short time. Considering the dynamics and reliability in the design process, this paper presents an approach to accelerate eco-design by establishing an active push system. The system mainly includes the following parts: the user design capability model, the database module (enterprise database, ecological design repository), the resource matching module, active push module, machine learning module. At last the design process of automobile transmission is used to explain the validity and rationality of the system. The test shows the system can reduce design time and optimize the design process when the product characteristics are related to ecological design.

Development of knowledge based parametric CAD modeling system for spur gear: An approach

In almost every mechanism, gears are extensively used machine parts to transmit power from one shaft to other with or without change of speed or direction. The need for better and efficient power transmission has increased the demand for effective gear design systems. All renowned formal gear design systems use AGMA (American Gear Manufacturers Association) standards for design. The conventional processes demand 150 to 200 man-hours to design the new optimized typical gear. Significant time reduction in design calculations can be obtained by using the soft computing tool. Additionally, advanced CAD Modeling techniques like parametric modeling can further trim down the design time. Hence, the present work is aimed to develop a knowledge-based parametric CAD modeling system for Spur Gear Design. This framework or system utilizes Knowledge Based Engineering (KBE) incorporating the rules of AGMA in design computations and parametric modeling in CAD. SolidWorks customization is carried out to generate the spur gear CAD model based on AGMA standards. Further, this system is proven as standard, faster, and simpler gear design system when compared with conventional design system. Results of this system are reasonably close to AGMA results under specific conditions.