213 results found
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, ISSN: 0378-7788
© 2020 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.
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.
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
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.
Michalakoudis I, Aurisicchio M, Childs P, et al., 2018, Empowering manufacturing personnel through functional understanding, Production Planning and Control, Vol: 29, Pages: 688-703, ISSN: 0953-7287
A growing interest in organizational knowledge management, along with increasingly widespread adoption of Quality Standards such as ISO 9001, has increasingly led organizations to implement training programs for all employees. Training for the manufacturing workforce, however, remains limited to informal “On-the-Job” training, administered by peer colleagues or supervisors - particularly in Small and Medium Enterprises (SMEs) where economic, educational, cognitive and cultural constraints to training are often deeply embedded. This paper proposes a methodology for training the manufacturing workforce on the functions of products and their constituent parts, and presents a case study conducted in a UK-based manufacturing SME - aiming to verify our two research hypotheses: Functional Analysis Diagrams (FAD) of the company’s products and parts would assist in knowledge assimilation; and, the knowledge assimilation has a positive effect on work quality and productivity levels. This intervention provided training on the purpose of the processes the participants are involved, aiming to empower them in supporting the optimization of these same processes. By using surveys and applying statistical inference on long-term quantitative data, the study confirmed subjective observations of substantial improvements in work quality (scrap reduction of 63%) and increased productivity (setup time reduced by 67%). To our knowledge, we were the first to examine the effect of functional modelling methods for workforce training in a manufacturing setup. Although this paper presents a single case study, the results suggest that the proposed methodology can be a promising solution for the industry.
Han J, Shi F, Chen L, et al., 2018, The Combinator – a computer-based tool for creative idea generation based on a simulation approach, Design Science, Vol: 4, ISSN: 2053-4701
Idea generation is significant in design, but coming up with creative ideas is often challenging. This paper presents a computer-based tool, called the Combinator, for assisting designers to produce creative ideas. The tool is developed based on an approach simulating aspects of human cognition in achieving combinational creativity. It can generate combinational prompts in text and image forms through combining unrelated ideas. A case study has been conducted to evaluate the Combinator. The study results indicate that the Combinator, in its current formulation, has assisted the tool users involved in the case study in improving the fluency of idea generation, as well as increasing the originality, usefulness, and flexibility of the ideas generated. The results also indicate that the tool could benefit its users in generating high-novelty and high-quality ideas effectively. The Combinator is considered to be beneficial in expanding the design space, increasing better idea occurrence, improving design space exploration, and enhancing the design success rate.
Spyrakos-Papastavridis E, Kashiri N, Childs PRN, et al., 2018, Online impedance regulation techniques for compliant humanoid balancing, Robotics and Autonomous Systems, Vol: 104, Pages: 85-98, ISSN: 0921-8890
This paper presents three distinct techniques, aimed at the online active impedance regulation of compliant humanoid robots, which endeavours to induce a state of balance to the system once it has been perturbed. The presence of passive elastic elements in the drives powering this class of robots leads to under-actuation, thereby rendering the control of compliant robots an intricate task. Consequently, the impedance regulation procedures proposed in this paper directly account for these elastic elements. In order to acquire an indication of the robot’s state of balance in an online fashion, an energy (Lyapunov) function is introduced, whose sign then allows one to ascertain whether the robot is converging to or diverging from, a desired equilibrium position. Computing this function’s time derivative unequivocally gives the energy-injecting nature of the active stiffness regulation, and reveals that active damping regulation has no bearing on the system’s state of stability. Furthermore, the velocity margin notion is interpreted as a velocity value beyond which the system’s balance might be jeopardized, or below which the robot will be guaranteed to remain stable. As a result, the unidirectional and bidirectional impedance optimization methods rely upon the use of bounds that have been defined based on the energy function’s derivative, in addition to the velocity margin. Contrarily, the third technique’s functionality revolves solely around the use of Lyapunov Stability Margins (LSMs). A series of experiments carried out using the COmpliant huMANoid (COMAN), demonstrates the superior balancing results acquired when using the bidirectional scheme, as compared to utilizing the two alternative techniques.
Chen L, Wang P, Shi F, et al., 2018, A COMPUTATIONAL APPROACH FOR COMBINATIONAL CREATIVITY IN DESIGN, 15th International Design Conference, Publisher: Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia; The Design Society, Glasgow, UK
Zhu L, Li N, Childs P, 2018, Light-weighting in aerospace component and system design, Propulsion and Power Research, Vol: 7, Pages: 103-119, ISSN: 2212-540X
Light-weighting involves the use of advanced materials and engineering methods to enable structural elements to deliver the same, or enhanced, technical performance while using less material. The concept has been extensively explored and utilised in many industries from automotive applications to fashion and packaging and offers significant potential in the aviation sector. Typical implementations of light-weighting have involved use of high performance materials such as composites and optimisation of structures using computational aided engineering approaches with production enabled by advanced manufacturing methods such as additive manufacture, foam metals and hot forming. This paper reviews the principal approaches used in light-weighting, along with the scope for application of light-weighting in aviation applications from power-plants to airframe components. A particular area identified as warranting attention and amenable to the use of light-weighting approaches is the design of solar powered aircraft wings. The high aspect ratio typically used for these can be associated with insufficient stiffness, giving rise to non-linear deformation, aileron reversal, flutter and rigid-elastic coupling. Additional applications considered include ultralight aviation components and sub-systems, UAVs, and rockets. Advanced optimisation approaches can be applied to optimise the layout of structural elements, as well as geometrical parameters in order to maximise structural stiffness, minimise mass and enable incorporation of energy storage features. The use of additive manufacturing technologies, some capable of producing composite or multi-material components is an enabler for light-weighting, as features formally associated with one principal function can be designed to fulfil multiple functionalities.
Han J, Shi F, Park D, et al., 2018, The conceptual distances between ideas in combinational creativity, 15th International Design Conference, Publisher: Design Society, Pages: 1857-1866, ISSN: 1847-9073
Combinational creativity plays a significant role in design for supporting designers in producing creative ideas at early phases of design. This study provides insights into conceptual distances for forming combinational ideas. The results from a case study indicate that far-related ideas are used more often than closely-related ones to produce creative combinational designs and that far-related ideas could lead to more creative outcomes. The study provides new insights to aid designers in understanding the value of combinational creativity, and support in the production of creative designs.
Chen X, Liu X, Childs P, et al., 2018, Design and fabrication of a low cost desktop electrochemical 3D printer, Pro-AM Conference in 2014, Pages: 395-400, ISSN: 2424-8967
Copyright © 2018 by Nanyang Technological University. Additive manufacturing (AM) (3D printing) is the process of creating 3D objects from digital models through the layer by layer deposition of materials. Electrochemical additive manufacturing (ECAM) is a relatively new technique which can create metallic components based depositing adherent layers of metal ions onto the surface of conductive substrate. In this paper, the design considerations for a meniscus confined ECAM approach is presented which demonstrates superior print speeds to equivalent works. This is achieved through the increase of the meniscus diameter to 400 \im which was achieved through the integration of a porous sponge into the print head to balance the hydraulic head of the electrolyte. Other piston based methods of controlling the electrolyte meniscus are discussed.
Sikhwal RK, Childs PRN, 2018, Innovation toolkit for identification of the optimal module options in open platform architecture products, NordDesign 2018, Publisher: Design Society
Open platform architecture products (OPAP) are the keyenablers for Product designfor Mass Individualisation.Itis a new product design paradigm that comprises an open hardware platform, mass-producedby large manufacturersand multiple independent modules,invented and produced by other smaller companies and by the end-userthat are integratedwith the platform. It gives freedom to end-usersto integrate different modules into the platform as per their choice. This type of product integration will be engaged with by the all actors involved in the design and aims to help them to be more creative and innovative. The end product will be highly individualised and technologically advanced.Based on explorative literature analysis, with practical insights from an industrial questionnaire survey, anInnovation toolkitfor the end-userhas beendeveloped.TheInnovation toolkitprovides a mean of selecting an optimal module option for each module which will be integrated onthe hardware platform. The design of theInnovation toolkitfor OPAP has been approachedin three different steps: Modelling of OPAP, Modelling of evaluation measures and evaluation indices with end-userpreferences and Identification of the optimal module options. In this work, variations in module options for a given module are modelledby an AND-OR tree and parameters of the nodes in this tree. Different module options for the selected module are evaluated by various evaluation measures. These evaluation measures are convertedinto comparable customer satisfaction indices. The optimal OPAP is identifiedby constrained optimisationof the overall customer satisfaction index. Twocase studieshavebeen presented to demonstrate theeffectiveness of the introduced Innovation toolkit.These case studies illustrate that the Innovation toolkit can readily be appliedto these typesof product developmentto obtain a highly individualised OPAP with optimised mo
Spyrakos Papastavridis E, Dai J, Childs PRN, et al., 2018, Selective-compliance based lagrange model and multilevel non-collocated feedback control of a humanoid robot, Journal of Mechanisms and Robotics, Vol: 10, ISSN: 1942-4302
This paper presents unified control schemes for compliant humanoid robots that are aimed at ensuring successful execution of both balancing tasks and walking trajectories for this class of bipeds, given the complexity of under-actuation. A set of controllers corresponding to the single support (SS) and double support (DS) walking phases has been designed based on the flexible sagittal joint dynamics of the system, accounting for both the motor and link states. The first controller uses partial state feedback (PDD), whereas the second considers the full state of the robot (PPDD), whilst both are mathematically proven to stabilize the closed-loop systems for regulation and trajectory tracking tasks. It is demonstrated mathematically that the PDD controller possesses better stability properties than the PPDD scheme for regulation tasks, even though the latter has the advantage of allowing for its associated gain-set to be generated by means of standard techniques, such as Linear Quadratic Regulator (LQR) control. A switching condition relating the Centre-of-Pressure (CoP) to the energy functions corresponding to the DS and SS models, has also been established. The theoretical results are corroborated by means of balancing and walking experiments using the COmpliant huMANoid (COMAN), whilst a practical comparison between the designed controller and a classical PD controller for compliant robots, has also been performed. Overall, and a key conclusion of this paper, the PPDD scheme has produced superior trajectory tracking performance, with 9%, 15% and 20% lower joint space error for the hip, knee and ankle respectively.
Garvey B, Childs P, DESIGN AS AN UNSTRUCTURED PROBLEM: NEW METHODS TO HELP REDUCE UNCERTAINTY – A PRACTITIONER PERSPECTIVE in Impact of Design Research in Industrial Practice Eds A Chakrabarti and U Lindemann Springer 2016, Impact of Design Research on Industrial Practice
Sikhwal RK, Childs PRN, 2018, Product Design for Mass Individualisation for Industrial Application, International Conference on Industrial Engineering and Engineering Management (IEEM), Publisher: IEEE, Pages: 674-680, ISSN: 2157-362X
In the last few years, a demand for renewed product personalisation to satisfy the exact need of the customers has been observed in some markets. As opposed to customisation, which put emphasis on the satisfaction of explicit needs of a defined market segment, individualisation aims at satisfying the specific needs of a customer. Product design for Mass Individualisation (MI) is a new product design paradigm that comprises an open hardware platform and multiple modules that are integrated with the platform, as per end-users’ choice. This paper identifies key areas and components which need to be focused to realise this approach and convert it into an industrial practice by an explorative study of existing product design and customisation approaches. A questionnaire survey has been conducted and results are presented for the industrial implication and insights on this approach. The findings clearly show that MI provides most individualised and technologically advanced product.
Garcia-Herrera C, Perkmann M, Childs PRN, 2018, Industry-led corporate start-up accelerator design: Lessons learned in a maritime port complex, DESIGN 2018 15th International Design Conference, Publisher: The Design Society, Pages: 1845-1856, ISSN: 1847-9073
Given the increasing disruption in every industry, firms can design new interfaces to further their strategic exploration efforts in order to remain competitive. Based on an inductive multi-case study research in a leading maritime port complex, we devised an actionable framework to design and run an industry-led accelerator through four steps: ecosystem orchestration, innovation funnel generation, flexible matching and scaling corporate start-up recurrent engagement. This framework can guide managerial practice and inform corporate start-up acceleration design in similar industrial contexts.
Sikhwal RK, Childs PRN, 2018, Design for mass individualisation: introducing networked innovation approach, Customization 4.0, Editors: Hankammer, Nielsen, Piller, Schuh, Wang, Publisher: Springer International Publishing, Pages: 19-35, ISBN: 978-3-319-77555-5
This paper outlines a nascent field of product innovation, which we believe will become significantly more relevant in the near future. Product design for mass individualisation is a new product design paradigm that comprises an open hardware platform and multiple modules that are integrated with the platform. It gives freedom to end users to integrate different modules into the platform as per their choice. Large manufacturers will produce the platform and some specific modules. Other modules will be invented and produced by smaller companies and by the user. This type of product integration will be engaged with by the all actors involved in the design and aims to help them to be more creative and innovative. Strategic and technological integration of all these actors, which is also the theme of Innovation 4.0, is the main focus of this work to intensify the innovation. Key areas which need to be focused on are identified and presented by an explorative study of existing product design and customisation approaches. Based on the explorative literature analysis, an industrial questionnaire survey has been conducted, and results are presented for the industrial implication and insights on this approach. The findings clearly show that the end product from product design for mass individualisation will be more creative and innovative.
Shi F, Chen L, HAN JI, et al., 2017, Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching, ASME 2017 IDETC/CIE, Publisher: ASME
Shi F, Chen L, Han J, et al., 2017, A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval, Journal of Mechanical Design, Transactions of the ASME, Vol: 139, ISSN: 1050-0472
With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the largescale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a WordNet focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.
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