Distinguishing artefacts:evaluating the saturation point of convolutional neural networks

Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search \& retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets ($<100$ models), the question remains as to the ability of a CNN to differentiate between models in increasingly large model repositories. This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the-shelf CNN architecture trained on synthetic data to classify models as class size increases. 1,000 CAD models were curated and processed to generate large scale surrogate data sets, featuring model coverage at steps of 10$^{\circ}$, 30$^{\circ}$, 60$^{\circ}$, and 120$^{\circ}$ degrees. The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's performance is observed to deteriorate significantly, limiting its present ability for automated twinning of physical to digital artefacts. Although, a match is more often found in the top-5 results showing potential for information search and retrieval on large repositories of surrogate models.Comment: 6 Pages, 5 Figures, 2 Tables, Conference, Design Engineering, CNN, Digital Twi

Knowledge dimensions in prototyping: investigating the what, when and how of knowledge generation during product development

Prototyping is a knowledge generation activity facilitating improved understanding of problem and solution spaces. This knowledge can be generated across a range of dimensions, termed knowledge dimensions (KDs), via a range of methods and media, each with their own inherent properties. This article investigates and characterises the relationships between prototypes and knowledge generated from prototyping activities during the design process, by establishing how different methods and media contribute across KDs. In so doing, it provides insights into prototyping activity, as well as affording a means by which prototyping knowledge generation may be studied in detail. The investigation considers sets of prototypes from eight parallel 16-week design projects, with subsequent investigation of the knowledge contributions that each prototype provides and at what stage of the design process. Results showed statistical significance supporting three inferences: i) teams undertaking the same design brief create similar knowledge profiles; ii) prototyping fidelity impacts KD contribution and iii) KDs align with the different phases of the project. This article demonstrates a means to describe and potentially prescribe knowledge generation activities through prototyping. Correspondingly, the article contends that consideration of KDs offers potential to improve aspects of the design process through better prototyping method selection and sequencing

The prototype taxonomised:Towards the capture, curation, and integration of physical models in new product development

The management of data related to prototypes created during new product development is seen as a beneficial yet challenging activity. While attempts have been made to understand prototypes and their context in a range of use-cases, there is a gap in the understanding of the data that captures a prototype's context and physical form. This paper highlights this gap, and addresses it through the development of a new taxonomy. Using existing literature, a body of domain-specific terms, and the combined experience of the nine authors, a robust and systematic taxonomy development process was followed. A comparison of the developed and pre-existing taxonomies, and an illustrative example, is used for evaluation. The taxonomy is fully presented along with a description of each of the 53 dimensions, and it is intended to be the foundation upon which methods and processes can be developed to improve the capture, curation and integration of physical prototypes in new product development.</p

Carta respuesta del Illmo. señor Obispo de Orihuela al señor fiscal del supremo, y Real Consejo de Castilla don Miguél Ric de Exea, á la que de orden de dicho Supremo Consejo escrivió á su illma. para que obedeciese a la Real Audiencia ... y absolver los reos de inobediencia a los mandatos apostolicos y reales ... arreglando la processión del Corpus de la ciudad de Alicante a dicho sagrado ceremonial, haciendose por la mañana, y no al anochecer ... como se practicaba ...

Texto asinado por Juan Elías, Obispo de Orihuela e dirixido a Miguél Ric de ExeaPrecede a tít.: "[Cristus]Texto datado en Caudete, 1743Sign.: APort. con orla ti