229 results found
Yu Z, Sadati SMH, Hauser H, et al., 2022, A Semi-Supervised Reservoir Computing System Based on Tapered Whisker for Mobile Robot Terrain Identification and Roughness Estimation, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 7, Pages: 5655-5662, ISSN: 2377-3766
Yu Z, Perera S, Hauser H, et al., 2022, A tapered whisker-based physical reservoir computing system for mobile robot terrain identification in unstructured environments, IEEE Robotics and Automation Letters, Vol: 7, Pages: 3608-3615, ISSN: 2377-3766
In this letter, we present for the first time the use of tapered whisker-based reservoir computing (TWRC) system mounted on a mobile robot for terrain classification and roughness estimation of unknown terrain.Hall effect sensors captured the oscillations at different locations along a tapered spring that served as a reservoir to map time-domain vibrations signals caused by the interaction perturbations from the ground to frequency domain features directly. Three hall sensors are used to measure the whisker reservoir outputs and these temporal signals could be processed efficiently by the proposed TWRC system which can provide morphological computation power for data processing and reduce the model training cost compared to the convolutional neural network (CNN) approaches.To predict the unknown terrain properties, an extended TWRC method including a novel detector is proposed based on the Mahalanobis distance in the Eigen space, which has been experimentally demonstrated to be feasible and sufficiently accurate.We achieved a prediction success rate of 94.3\% for six terrain surface classification experiments and 88.7\% for roughness estimation of the unknown terrain surface.
Jiang P, Dieckmann E, Han J, et al., 2021, A Bibliometric Review of Sustainable Product Design, ENERGIES, Vol: 14
Sikhwal RK, Childs PRN, 2021, Towards Mass Individualisation: setting the scope and industrial implication, Design Science, Vol: 7, Pages: 1-34, ISSN: 2053-4701
Within the last few years, a need for renewed product personalisation has been observed in some markets such as consumer electronics, fashion to meet the exact demands of the customers. Product customisation emphasises the fulfilment of explicit requirements of a defined market segment, but product individualisation targets at satisfying the particular needs of a customer. Mass Individualisation (MI) is a new product design approach comprising of an open hardware platform and multiple modules to be integrated with the platform. It gives freedom to end-users to integrate different modules into the platform as per their choice. Technological and strategic integration of all actors involved in the design process is the primary focus of this research. This paper identifies key areas which need to be focussed on to realise this approach and convert it into an industrial practice by an explorative study of existing product design and customisation approaches. An industrial survey was conducted, and results for the industrial implication and insights on this approach are presented. The findings show that the end product from product design for MI will be more creative and innovative by the networking of all actors, and offers more individualised and technologically advanced products.
Han J, Park D, Hua M, et al., 2021, Is group work beneficial for producing creative designs in STEM design education?, International Journal of Technology and Design Education, ISSN: 0957-7572
Creativity is a significant element in design education, and frequently a significant competency during recruitment for design professions. Group work and individual work are widely employed in higher education. Many studies have highlighted the merits of employing group work in design education, cultivating collaborative design abilities and fostering sought-after employability skills. Although the benefits of group work in design practice and education are widely recognised, few studies have shown evidence that group work outperforms individual work regarding creative design activities in higher education contexts. Therefore, the aim of this research is to explore whether group or individual work is more beneficial for fostering students in generating creative designs in STEM design education. A case study, involving two cohorts of second-year undergraduate students studying a UK Engineering degree Industrial Design programme, is reported. The case study compares the design outputs produced by the two cohorts tackling the same design challenge in a product design module but employing individual and group work, respectively. The case study results show that no significant differences have been found between the design outputs produced by group work and individual work, considering novelty, usefulness and overall creativity. Further analysis reveals that a student’s academic performance is not significantly related to the level of creativity of the design produced. This research indicates design educators should employ both group and individual work to complement each other in design education, and suggests potential solutions to enhance students’ design creativity.
Yu Z, SMHadi S, Hasitha W, et al., 2021, A method to use reservoir computing in a whisker sensor for terrain identification by mobile robots, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have a dominant frequency at the vertical perturbation frequency of the mobile robot sandwiched by two other less dominant but distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.Index Terms— Robotic whiskers, Surface identification, multi-layer perceptron, Modal analysis.
Han J, Jiang P, Childs PRN, 2021, Metrics for Measuring Sustainable Product Design Concepts, ENERGIES, Vol: 14
Park D, Han J, Childs PRN, 2021, 266 Fuzzy front-end studies: current state and future directions for new product development, Research in Engineering Design, Vol: 32, Pages: 377-409, ISSN: 0934-9839
266 fuzzy front-end (FFE) studies in the new product development (NPD) sector were examined. The studies were selected using a bibliometrics method, and chronologically and statistically examined with ten criteria divided into two dimensions. The first dimension is associated with overall attributes of the FFE, consisting of six criteria: the study taxonomy, model type, NPD speed, NPD attributes, model characteristic, and model structure. The second dimension is relevant to the FFE performance structure related to process parameters, comprised of four criteria: the FFE task, activity, performance method, and toolkit. In terms of those two dimensions, the paper looks at previous FFE studies to gain an understanding of features of each FFE study along with related knowledge and theories, as well as identification of evolution trends of FFE studies. Based on the identification, an FFE model development strategy for each criterion is formulated, and this paper proposes possible options for executing those strategies which exert influence on the form of the cluster network. The intention is for the database to be utilised as an overview of all existing FFE studies and allow specific FFE studies to be selected to examine FFE approaches.This paper provides FFE model development guidance on how to deal with the overall attributes and outcomes of the FFE which affect the entirety of the innovation process, and how to manage the performance structure related to process parameters.
Park D, Han J, Childs PRN, 2021, A data-driven fuzzy front end model for contextual performance and concurrent collaboration, IEEE Transactions on Engineering Management, Pages: 1-24, ISSN: 0018-9391
A data-driven model for the fuzzy front end (FFE) stage in new product development (NPD) programs, with a series of toolkits to decrease uncertainty and ambiguity of parameter processing, has been developed. Parameters produced in toolkits provided in previous models tend to exist independently, without any interrelationship in the contextual performance relationship of a single functional domain nor concurrent collaboration relationship across multiple functional domains. This results in uncertainty and ambiguity triggered by an incorrect interpretation of parameters. The new model involved inferring a single representative FFE scenario wherein diverse FFE performance structures interlock from the contextual performance and concurrent collaboration perspectives by analyzing various real-world FFE scenarios gathered from NPD expert interviews. This representative scenario was embodied into the model with a performative structure, through deployment of toolkits. Users are informed of the purpose, roles, and meanings of parameters and their relationships and thus can infer each parameter from other parameters. This contributes to reduction in uncertainty and ambiguity in processing parameters. This article proposes an FFE execution concept, giving mathematical reasoning behind the performance structure of the model.
Simpson K, Childs P, Whyte J, 2021, Sensitivity analysis of heating a typical UK dwelling and implications for retrofit design, International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era (CISBAT), Publisher: IOP PUBLISHING LTD, Pages: 1-6, ISSN: 1742-6588
The aim of this research is to quantify the impact of heating set point on space heating energy demand for a typical UK dwelling. Retrofit includes fabric energy efficiency improvements. Energy performance certificates (EPCs) inform the householder of typical savings per measure, but this has previously been found to inaccurately estimate space heating energy demand, leading to errors in 'typical savings' presented to householders. The most sensitive inputs have been found to be temperature set point, followed by fabric efficiency. The BREDEM methodology assumes a temperature of 21°C for nine hours a day, rather than ~16°C and ~20°C found in research. The methods used to inform this study are local sensitivity analysis of the domestic energy model, based on a typical dwelling example with calibrated inputs. This is done using an open calibrated Python model, based on BREDEM. The impact of heating patterns on space heating energy demand are modelled pre retrofit; according to differing heating set points, following wall and loft fabric upgrade and full fabric upgrade. The BREDEM heating set point assumptions lead to space heating energy demand predicted ~50-100 kWh/m2/yr higher than real heating set points. Implications for retrofit design and EPCs are discussed.
Yu Z, Sadati SMH, Wegiriya H, et al., 2021, A Method to use Nonlinear Dynamics in a Whisker Sensor for Terrain Identification by Mobile Robots, IEEE International Conference on Intelligent Robots and Systems, Pages: 8437-8443, ISSN: 2153-0858
This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have one dominant frequency at the vertical perturbation frequency of the mobile robot and one with distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.
Wang P, Wang S, Peng D, et al., 2020, Neurocognition-inspired design with machine learning, Design Science, Vol: 6, Pages: 1-19, ISSN: 2053-4701
Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.
Simpson K, Whyte J, Childs P, 2020, Data-centric innovation in retrofit: A bibliometric review of dwelling retrofit across North Western Europe, Energy and Buildings, Vol: 229, Pages: 1-9, ISSN: 0378-7788
Data-centric innovation can inform the development of effective retrofit strategies through novel methods of collecting, analysing and sharing data. This bibliometric review uses a text-mining tool to identify research trends in dwelling retrofit research across North Western Europe. The review identifies a major focus on energy efficiency, with sub-themes on: 1) energy performance, 2) heat, power and control technologies, 3) indoor environment quality and 4) retrofit practice. In dwelling retrofit, there is now an established research tradition of using data-centric methods in monitoring and modelling energy performance to inform and learn from energy efficiency interventions. Building on the state-of-the-art, our analyses suggest opportunities for data-centric methods to consider the indoor environment quality and material impacts resulting from energy performance improvements. This information can then be openly communicated across the supply chain. Thus the paper discusses the retrofit themes and data-centric methods within North Western Europe, for an emerging trajectory of data-centric retrofit research and practice.
Baxter W, Roots S, Tuomala E, et al., 2020, Ritual Design Toolkit, London, Publisher: Interaction Foundry
Rituals are intentional behaviours with a distinct emotional outcome. They fill our lives with deeper meaning and are found everywhere from the workplace to the kitchen table. We have made the Ritual Design Toolkit to help you understand rituals, how to harness them, and how to design them. In our own work, we have used the toolkit in a range of applications including enhancing key moments in a customer journey, helping people adopt healthier eating habits and building and strengthening communities. The toolkit can be used for grand rituals and micro-interactions. Whether you are a manager of a team, a packaging designer, or a service enthusiast, you can find guidance here to build more meaningful moments into your work. The toolkit offers a ritual design process consisting of three main steps: scoping, creating and testing rituals.
Spyrakos-Papastavridis E, Childs PRN, Dai JS, 2020, Passivity Preservation for Variable Impedance Control of Compliant Robots, IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol: 25, Pages: 2342-2353, ISSN: 1083-4435
Liu X, Ouyang M, Orzech M, et al., 2020, In-situ fabrication of carbon-metal fabrics as freestanding electrodes for high-performance flexible energy storage devices, Energy Storage Materials, Vol: 30, Pages: 329-336, ISSN: 2405-8297
Hierarchical 1D carbon structures are attractive due to their mechanical, chemical and electrochemical properties however the synthesis of these materials can be costly and complicated. Here, through the combination of inexpensive acetylacetonate salts of Ni, Co and Fe with a solution of polyacrylonitrile (PAN), self-assembling carbon-metal fabrics (CMFs) containing unique 1D hierarchical structures can be created via easy and low-cost heat treatment without the need for costly catalyst deposition nor a dangerous hydrocarbon atmosphere. Microscopic and spectroscopic measurements show that the CMFs form through the decomposition and exsolution of metal nanoparticle domains which then catalyze the formation of carbon nanotubes through the decomposition by-products of the PAN. These weakly bound nanoparticles form structures similar to trichomes found in plants, with a combination of base-growth, tip-growth and peapod-like structures, where the metal domain exhibits a core(graphitic)-shell(disorder) carbon coating where the thickness is in-line with the metal-carbon binding energy. These CMFs were used as a cathode in a flexible zinc-air battery which exhibited superior performance to pure electrospun carbon fibers, with their metallic nanoparticle domains acting as bifunctional catalysts. This work therefore unlocks a potentially new category of composite metal-carbon fiber based structures for energy storage applications and beyond.
Muranko Z, Aurisicchio M, Baxter W, et al., 2020, Behaviour chains in circular consumption systems: the reuse of FMCGs, Proceedings of the IS4CE2020 Conference of the International Society for the Circular Economy
Simpson C, Whyte J, Childs P, 2020, Residential retrofit: A review of themes, data-centric methods and future directions to accelerate net zero, CIBSE Technical Symposium
Chen X, Liu X, Ouyang M, et al., 2019, Multi-metal 4D printing with a desktop electrochemical 3D printer, Scientific Reports, Vol: 9, ISSN: 2045-2322
4D printing has the potential to create complex 3D geometries which are able to react to environmental stimuli opening new design possibilities. However, the vast majority of 4D printing approaches use polymer based materials, which limits the operational temperature. Here, we present a novel multi-metal electrochemical 3D printer which is able to fabricate bimetallic geometries and through the selective deposition of different metals, temperature responsive behaviour can thus be programmed into the printed structure. The concept is demonstrated through a meniscus confined electrochemical 3D printing approach with a multi-print head design with nickel and copper used as exemplar systems but this is transferable to other deposition solutions. Improvements in deposition speed (34% (Cu)-85% (Ni)) are demonstrated with an electrospun nanofibre nib compared to a sponge based approach as the medium for providing hydrostatic back pressure to balance surface tension in order to form a electrolyte meniscus stable. Scanning electron microscopy, X-ray computed tomography and energy dispersive X-ray spectroscopy shows that bimetallic structures with a tightly bound interface can be created, however convex cross sections are created due to uneven current density. Analysis of the thermo-mechanical properties of the printed strips shows that mechanical deformations can be generated in Cu-Ni strips at temperatures up to 300 °C which is due to the thermal expansion coefficient mismatch generating internal stresses in the printed structures. Electrical conductivity measurements show that the bimetallic structures have a conductivity between those of nanocrystalline copper (5.41×106 S.m−1) and nickel (8.2×105 S.m-1). The potential of this novel low-cost multi-metal 3D printing approach is demonstrated with the thermal actuation of an electrical circuit and a range of self-assembling structures.
Childs PRN, 2019, Aircraft cabin air supply and the internal air system, Journal of Health and Pollution, Vol: 9, Pages: S17-S21, ISSN: 2156-9614
This paper describes systems commonly employed to deliver aircraftcabin air, and the internal air system which is responsible for thesupply of balancing, sealing and cooling air used with the engine tocontrol the operation of bearings, discs and other critical components,as well as bleed air. A series of mechanisms that could be responsiblefor contamination of bleed and cabin air are considered including:ingestion into the compressor intake of poor quality air; leakage froma seal of oil; ingestion from the gases associated with a stall or surgeevent; off-gassing of cabin fittings and emissions from occupants.
Chen X, Liu X, Ouyang M, et al., 2019, Electrospun composite nanofibre supercapacitors enhanced with electrochemically 3D printed current collectors, Journal of Energy Storage, Vol: 26, Pages: 100993-100993, ISSN: 2352-152X
Carbonised electrospun nanofibres are attractive for supercapacitors due to their relatively high surface area, facile production routes and flexibility. With the addition of materials such as manganese oxide (MnO), the specific capacitance of the carbon nanofibres can be further improved through fast surface redox reactions, however this can reduce the electrical conductivity. In this work, electrochemical 3D printing is used as a novel means of improving electrical conductivity and the current collector-electrode interfacial resistance through the deposition of highly controlled layers of copper. Neat carbonised electrospun electrodes made with a 30 wt% manganese acetylacetonate (MnACAC) and polyacrylonitrile precursor solution have a hydrophobic nature preventing an even copper deposition. However, with an ethanol treatment, the nanofibre films can be made hydrophilic which enhances the copper deposition morphology to enable the formation of a percolating conductive network through the electrode. This has the impact of increasing electrode electronic conductivity by 360% from 10 S/m to 46 S/m and increasing specific capacitance 110% from 99 F/g to 208 F/g at 5 mV/s through increased utilisation of the pseudocapacitive active material. This novel approach thus provides a new route for performance enhancement of electrochemical devices using 3D printing, which opens new design possibilities.
Childs P, 2019, Interplays of humanism, cognitivism and behaviourism in design engineering, Design School: After Boundaries and disciplines, Editors: Rodgers, Bremner, Publisher: Vernon Press, ISBN: 978-1-62273-586-0
Principal learning theories include behaviourism, humanism, cognitivism and constructivism. Each has held sway in education for a period and has contributed to our ability to develop and deploy evidence-based curriculums and learning environments. This chapter considers the contributions of these learning theories and their relevance to the design, engineering and hybrid domains, with a particular focus on the recently developed curriculum for an integrated masters in Design Engineering at Imperial College London.
Wang P, Peng D, Li L, et al., 2019, Human-in-the-loop design with machine learning, The International Conference in Engineering Design (ICED) 19, Publisher: Cambridge University Press (CUP), Pages: 2577-2586
Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.
Hu Z, Rao C, Tao C, et al., 2019, A case-based decision theory based process model to aid product conceptual design, Cluster Computing, Vol: 22, Pages: 10145-10162, ISSN: 1386-7857
In new product development, the rapid proposal of innovative solutions represents an important phase. This in turn relies on creative ideas, their evaluation, refinement and embodiment of worthwhile directions. This study aims to describe a CBDT based process model for product conceptual design that concentrates on rapidly generating innovations with the support of decision-making rationale. Case-based decision theory (CBDT), derived from case-based reasoning, is applied in this paper as a core method to aid design engineers to make an informed decision quickly, thus accelerating the design process. In the process of utilizing CBDT to support a decision, as for the similarity function, the proper value assignment methods to the selected attribute set for calculation are discussed. In order to assist with innovative solution, aspects of the theory of inventive problem solving (TRIZ) are integrated into the case-based reasoning process. Accordingly, a CBDT-TRIZ model is developed. Quality-function deployment is used to translate customer wants into relevant engineering design requirements and thus formulating the design specification. Image-Scale is used to offer an orthogonal coordinates system to aid evaluation. Finally, a case study is used to demonstrate the validity of the proposed process model based on the design of a cordless hand-tool for garden and lawn applications.
Chen L, Wang P, Dong H, et al., 2019, An artificial intelligence based data-driven approach for design ideation, Journal of Visual Communication and Image Representation, Vol: 61, Pages: 10-22, ISSN: 1047-3203
Ideation is a source of innovation and creativity, and is commonly used in early stages of engineering design processes. This paper proposes an integrated approach for enhancing design ideation by applying artificial intelligence and data mining techniques. This approach consists of two models, a semantic ideation network and a visual concepts combination model, which provide inspiration semantically and visually based on computational creativity theory. The semantic ideation network aims to provoke new ideas by mining potential knowledge connections across multiple knowledge domains, and this was achieved by applying “step-forward” and “path-track” algorithms which assist in exploring forward given a concept and in tracking back the paths going from a departure concept through a destination concept. In the visual concepts combination model, a generative adversarial networks model is proposed for generating images which synthesize two distinct concepts. An implementation of these two models was developed and tested in a design case study, which indicated that the proposed approach is able to not only generate a variety of cross-domain concept associations but also advance the ideation process quickly and easily in terms of quantity and novelty.
Garvey B, Chen L, Shi F, et al., 2019, New directions in computational, combinational and structural creativity, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol: 233, Pages: 425-431, ISSN: 0954-4062
This paper examines how new and creative relationships in data sets, not easily revealed by conventional information retrieval methods and technologies, can be identified using a mix of established and new methods. The authors present how the integration of computerised Morphological Analysis with new computational models, incorporating web crawler, data processing networking and data mining algorithms, can help facilitate the identification of new ideas. Boden’s concept of “Combinational Creativity” indicates a structured process which generates unfamiliar combinations of familiar concepts and constructs allowing creative styles of thought. This structured approach has been constrained by the resultant combinatorial explosion and the dearth of easily accessible computer software and supporting methodologies, to help identify viable new solutions. Feature enhanced computerised morphological analysis (MA), provides a new structural support tool for creativity and innovation. MA systematically structures and examines all the possible relationships in a multidimensional, highly complex, usually non-quantifiable problem space. Computerisation of the process now permits large numbers of configurations (millions) in the problem space to be majorly reduced (typically > 95%), identifying only internally consistent solutions. These solutions are likely to embrace configurations containing something which has not previously been considered, thus increasing the probability of some form of technological or design breakthrough and hence truly creative.
Han J, Park D, Shi F, et al., 2019, Three driven approaches to combinational creativity: Problem-, similarity- and inspiration-driven, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol: 233, Pages: 373-384, ISSN: 0954-4062
Creativity is a crucial element of design. The aim of this study is to investigate the driving forces behind combinational creativity. We propose three driven approaches to combinational creativity, problem-, similarity- and inspiration-driven, based on previous research projects on design process, strategy and cognition. A case study involving hundreds of practical products selected from winners of international design competitions has been conducted to evaluate the three approaches proposed. The results support the three driven approaches and indicate that they can be used independently as well as complementarily. The three approaches proposed in this study have provided an understanding of how combinational creativity functions in design. The approaches could be used as a set of creative idea generation methods for supporting designers in producing creative design ideas.
Childs P, Cropley D, 2019, Creativity in design special section, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol: 233, Pages: 371-372, ISSN: 0954-4062
Childs PRN, 2018, Mechanical Design Engineering Handbook, Publisher: Butterworth-Heinemann, ISBN: 9780081023679
Presents a clear, concise text that explains key component technology, with step-by-step procedures, fully worked design scenarios, component images and cross-sectional line drawings Provides essential data, equations and interactive ...
Han J, Shi F, Chen L, et al., 2018, A computational tool for creative idea generation based on analogical reasoning and ontology, AI EDAM, Vol: 32, Pages: 462-477, ISSN: 0890-0604
Analogy is a core cognition process used to produce inferences as well as new ideas using previous knowledge and experience. Ontology is a formal representation of a set of domain concepts and their relationships. The use of analogy and ontology in design activities to support design creativity have previously been explored. This paper explores an approach to construct ontologies with sufficient richness and coverage to support reasoning over real-world datasets for prompting creative idea generation. This approach has been implemented into a computational tool for assisting designers in generating creative ideas during the early stages of design. The tool, called “the Retriever”, has been developed based on ontology by embracing the aspects of analogical reasoning. A case study has indicated that the tool can be effective and useful for idea generation. The results have indicated that the tool, in its current formulation, can significantly improve the fluency and flexibility of idea generation and the usefulness of ideas, as well as slightly increase the originality of ideas, for the case study concerned.
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