Supporting Knitwear Design Using Case-Based Reasoning

Organised by: Cranfield UniversityKnitwear design is a creative activity that is hard to automate using the computer. The production of the associated knitting pattern, however, is repetitive, time-consuming and error-prone, calling for automation. Our objectives are two-fold: to facilitate the design and to ease the burden of calculations and checks in pattern production. We conduct a feasibility study for applying case-based reasoning in knitwear design: we describe appropriate methods and show how they can be implemented.Mori Seiki – The Machine Tool Compan

References to past designs

Designing by adaptation is almost invariably a dominantambiguity feature of designing, and references to past designs are ubiquitous in design discourse. Object references serve as indices into designers' stocks of design concepts, in which memories for concrete embodiments and exemplars are tightly bound to solution principles. Thinking and talking by reference to past designs serves as a way to reduce the overwhelming complexity of complex design tasks by enabling designers to use parsimonious mental representations to which details can be added as needed. However object references can be ambiguous, and import more of the past design than is intended or may be desirable

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Relaxed lightweight assembly retrieval using vector space model

International audienceAssembly searching technologies are important for the improvement of design reusability. However, existing methods require that assemblies possess high-level information, and thus cannot be applied in lightweight assemblies. In this paper, we propose a novel relaxed lightweight assembly retrieval approach based on a vector space model (VSM). By decomposing the assemblies represented in a watertight polygon mesh into bags of parts, and considering the queries as a vague specification of a set of parts, the resilient ranking strategy in VSM is successfully applied in the assembly retrieval. Furthermore, we take the scale-sensitive similarities between parts into the evaluation of matching values, and extend the original VSM to a relaxed matching framework. This framework allows users to input any fuzzy queries, is capable of measuring the results quantitatively, and performs well in retrieving assemblies with specified characteristics. To accelerate the online matching procedure, a typical parts based matching process, as well as a greedy strategy based matching algorithm is presented and integrated in the framework, which makes our system achieve interactive performance. We demonstrate the efficiency and effectiveness of our approach through various experiments on the prototype system

Comparative study of design: application to Engineering Design

A recent exploratory study examines design processes across domains and compares them. This is achieved through a series of interdisciplinary, participative workshops. A systematic framework is used to collect data from expert witnesses who are practising designers across domains from engineering through architecture to product design and fashion, including film production, pharmaceutical drugs, food, packaging, graphics and multimedia and software. Similarities and differences across domains are described which indicate the types of comparative analysis we have been able to do from our data. The paper goes further and speculates on possible lessons for selected areas of engineering design which can be drawn from comparison with processes in other domains. As such this comparative design study offers the potential for improving engineering design processes. More generally it is a first step in creating a discipline of comparative design which aims to provide a new rich picture of design processes

A similarity-assisted multi-fidelity approach to conceptual design space exploration

In conceptual design studies engineers typically utilize data-based surrogate models to enable rapid evaluation of design objectives that otherwise would be too computationally expensive and time-consuming to simulate. Due to the computationally expensive simulations, the data-based surrogate models are often trained using small sample sizes, resulting in low-fidelity models which can produce results that are not trustworthy. To mitigate this issue, a similarity-assisted design space exploration method is proposed. The similarity is measured between design points that have been evaluated through lower-fidelity data-based surrogate models and design points that have been evaluated using higher-fidelity physics-based simulations. This similarity information can then be used by design engineers to better understand the trustworthiness of the data produced by the low-fidelity surrogate models. Our numerical experiments demonstrate that such a similarity measurement can be used as an indicator of the trustworthiness of the lower-fidelity model predictions. Moreover, a second similarity metric is proposed for measuring the similarity of new designs to legacy designs, thus highlighting the potential to reuse knowledge, analysis models, and data. The proposed method is demonstrated by means of an aero-engine structural component conceptual design study. An open-source software tool developed to assist in data visualization is also presented

Data Representation Methods For Environmentally Conscious Product Design

The challenge of holistically integrating environmental sustainability considerations with design decision-making requires novel representations for design and sustainability-related data that allow designers to understand correlations among them. Challenges such as (1) lack of suitable data & information models, (2) methods that simultaneously consider environmental sustainability as well as design constraints, and (3) uncertainty models for characterizing subjectivity in environmental sustainability-based decision making, pose serious impediments towards this goal