Design Repository Research Articles

Business Models for Open Source Hardware Repositories

Free and open source hardware repositories provide massive public good, but funding their operation has proven tenuous with conventional business models. This study evaluates business models to foster that public good. Business models for online design repositories are reviewed and a new model is conceptualized to fund repository operations. The greatest added value an open hardware repository brings to the user-developer community is validation and vetting of the designs. A business model was proposed that uses revenue from the vetting process to fund validation studies and sustainable operations of the open hardware repository itself. As the return on investment of laterally-scaled open hardware that can leverage distributed manufacturing has the potential for creating enormous value, maintaining repositories for this hardware enables vast wealth generation for everyone. This is the first study specifically focused on ways to ensure economic sustainability of open hardware repositories.

Development of solid microneedles for transdermal drug delivery in companion animals

The objective of this paper was to create an economical and abecedarian 3D printing method for the production of solid microneedles, as a more efficient transdermal drug delivery method, for day-to-day use in companion animals. The process of 3D printing was conducted using two types of 3D printers, utilizing the FDM and SLA printing techniques. Modulus of Elasticity was calculated for the determination of mechanical properties of the material, wherethe printed specimen was subjected to axial loading, and deformations were measured using an optical scanner. Post-processing was conducted by washing microneedles in isopropyl alcohol, followed by UV curing. The procedure of testing penetration capabilities was conducted at two sites of cat skin: Auris externa and the lateral part of the abdomen. The SLA printing method was more precise, resulting in higher quality microneedles for animal use compared to the FDM printing technique. Modulus of Elasticity was calculated and the value E=0.9 GPa can be used. Testing proved that the printed model was able to penetrate the skin at the tested sites. The use of microneedles is simple and economical, and therefore has wide applications in small animal practice. Veterinarians can access microneedle design repositories and print them for more effective transdermal drug delivery. The multifunctionality and transferability of the design in the present study ensure that it can be further modified to provide personalized therapy.

What’s in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models Through User-Provided Names in Computer Aided Design Files

Abstract Semantic knowledge of part-part and part-whole relationships in assemblies is useful for a variety of tasks from searching design repositories to the construction of engineering knowledge bases. In this work, we propose that the natural language names designers use in computer aided design (CAD) software are a valuable source of such knowledge, and that large language models (LLMs) contain useful domain-specific information for working with this data as well as other CAD and engineering-related tasks. In particular, we extract and clean a large corpus of natural language part, feature, and document names and use this to quantitatively demonstrate that a pre-trained language model can outperform numerous benchmarks on three self-supervised tasks, without ever having seen this data before. Moreover, we show that fine-tuning on the text data corpus further boosts the performance on all tasks, thus demonstrating the value of the text data which until now has been largely ignored. We also identify key limitations to using LLMs with text data alone, and our findings provide a strong motivation for further work into multi-modal text-geometry models. To aid and encourage further work in this area we make all our data and code publicly available.

A generative-based design methodology to enable the democratisation of 3D printing

Abstract 3D printing technologies, such as material extrusion (MEX), hold the potential to revolutionise manufacturing by providing individuals without traditional manufacturing capabilities with powerful and affordable resources. However, widespread adoption is impeded by the lack of user-friendly design tools due to the necessity of domain-specific expertise in computer-aided design (CAD) software and the overwhelming level of design freedom afforded by the MEX process. To overcome these barriers and facilitate the democratisation of design (DoD), this article introduces an innovative, generative-based design (GBD) methodology aimed at enabling non-technical users to create functional components independently. The novelty of this methodology lies in its capacity to simplify complex design tasks, making them more accessible to non-designers. The proposed methodology was tested in the design of a load-bearing part, yielding a functional component within two design iterations. A comparative analysis with the conventional CAD-based process revealed that the GBD methodology enables the DoD, reflected in a 68% reduction in design activities and a decrease in design difficulty of 62% in requisite know-how and a 55% in understanding. Through the creation and implementation of this methodology, the article demonstrates a pioneering integration of state-of-the-art techniques of generative design with design repositories enabling effective co-design with non-designers.

Assessing Design Repository Search Effectiveness

Reuse of design knowledge in systems engineering (SE) has been identified as a potential way to decrease design cycle times and development costs of engineering projects. Design repositories (DR) have been proposed and demonstrated as a way to store design knowledge in a reusable way. Yet little attention has been devoted specifically to the search methods used to identifying useful information within a DR, or how search method effectiveness can be measured or assessed. This paper demonstrates a method for assessing the effectiveness of search approaches within DR. The use of this method in assessing multiple search algorithms is demonstrated by example. The assessment method presented herein can also be used to identify the characteristics of repository data or intended reuse applications that affect the performance of search methods. These results can be used by engineers to inform selection of search methods based on anticipated future DR use cases.

Knowledge graph construction for product designs from large CAD model repositories

Product Design based Knowledge graphs (KG) aid the representation of product assemblies through heterogeneous relationships that link entities obtained from multiple structured and unstructured sources. This study describes an approach to constructing a multi-relational and multi-hierarchical knowledge graph that extracts information contained within the 3D product model data to construct Assembly-Subassembly-Part and Shape Similarity relationships. This approach builds on a combination of utilizing 3D model meta-data and structuring the graph using the Assembly-Part hierarchy alongside 3D Shape-based Clustering. To demonstrate our approach, from a dataset consisting of 110,770 CAD models, 92,715 models were organized into 7,651 groups of varying sizes containing highly similar shapes, demonstrating the varied nature of design repositories, but inevitably also containing a significant number of repetitive and unique designs. Using the Product Design Knowledge Graph, we demonstrate the effectiveness of 3D shape retrieval using Approximate Nearest Neighbor search. Finally, we illustrate the use of the KG for Design Reuse of co-occurring components, Rule-Based Inference for Assembly Similarity and Collaborative Filtering for Multi-Modal Search of manufacturing process conditions. Future work aims to expand the KG to include downstream data within product manufacturing and towards improved reasoning methods to provide actionable suggestions for design bot assistants and manufacturing automation.

Uncovering Human Errors Associated With System-User Interactions Using Functional Modeling

Abstract Designers should adequately develop user considerations such as usability, safety, and comfort during the design process of new systems. Nevertheless, incorporating human factors engineering principles during early design phases is not simple. The objective of this work is to assist designers in implementing human factors engineering principles during early design phases using a functional model framework. This effort expands our previous work on automating the function-human error design method (FHEDM) implementation. In this work, we use data mining techniques in a design repository to explore the construction of association rules between components, functions, flows, and user interactions. Such association rules can support designers assessing user-system interactions during the early design stages. To validate this approach, we compare the associations generated by expert designers using the FHEDM while designing a new product to those generated by an algorithm using the repository data. The results show notable similarities between the associations extracted by the algorithm and the associations identified by designers. Thus, the overall results show that association rules extracted from a rich dataset can be used to distinguish user-product interactions, demonstrating the potential of automating the identification of user-product interactions from a functional model.

Classifying Component Function in Product Assemblies With Graph Neural Networks

Abstract Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function-based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learning from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F1-score of 0.617 for tier 1 (broad), 0.624 for tier 2, and 0.415 for tier 3 (specific) functions. Given the imbalance of data features and the subjectivity in the definition of product function, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems and Design-for-X consideration in function-based design.

Impact of 3D Printing on Occupational Therapy Student Technology Efficacy

This study examines a mixed methods experiment, evaluating the impact of 3D printing on Occupational Therapy student’s self-reported technology efficacy levels, before and after an educational module focused on 3D printing technology. Utilizing free and online software and university owned 3D printers, students were challenged to create client centered assistive devices. Software included free computer aided drafting software, slicing software, and an online design repository. Copyright and attribution licensing concepts were also addressed for design modification considerations and use in clinical settings. Participants explored adaptations of 3D designs to meet individualized client needs and integration of rehabilitative theories. Project-based implementation of 3D printing with ‘real world’ clients created increased student ‘buy-in’ and engagement. Participants were recruited from private, rural, higher education, occupational therapy students in pre-clinical lab courses. Comparisons between student groups exposed to 3D printing and a control group without exposure are assessed using quantitative and qualitative data. The control group expressed interest in further technology education in their field. Students show significant increases in technology focused self-efficacy between participant groups.

Design Principles for Additive Manufacturing: Leveraging Crowdsourced Design Repositories

Abstract Industry 4.0, as the fourth industrial revolution, represents significant challenges and numerous innovation opportunities for future product realization. A critical area of Industry 4.0 is the advancement of new design theories, design methods, and design principles to drive and enable the revolution with designers, engineers, teams, and organizations. This paper focuses on the advancement of a design theory and design principles for a growing manufacturing capability for Industry 4.0: additive manufacturing (AM). With high degrees of freedom, the field and use of AM requires design guidance and highly practical knowledge for supporting ideation processes, enabling understanding of capabilities, and creating a basis to innovative with the technology. Some design principles for AM exist in the literature; however, designers seek more fundamental and practical design guidelines for successfully creating and building their customized design artefacts, especially as Industry 4.0 moves forward. In this study, a crowdsourced repository for additively manufacturable components is used as the source of design data, within an empirical study, to extract practical design principles for AM. A total of 23 crowdsourced design principles for AM are extracted and clustered according to level specificity: (i) design for manufacturing, (ii) design for digital manufacturing, (iii) design for AM, and (iv) design for fused deposition modeling. These 23 AM design principles, as a foundation for AM design and Industry 4.0, are provided in a common framework; expressed for ready use by designers, developers, and researchers; and illustrated through some contemporary designs.

How designers benefit from free 3D design sharing

3D printing has been described as a technological revolution, and it has already changed the way how some low-demand items are designed, produced and delivered to the customers. The threshold for new players entering the 3D business is lower than ever; the lowering costs of printers and the availability of free planning software provide new opportunities for entrepreneurs. However, at the same time, the availability of free 3D designs has exploded, and there is an enormous selection of them available. This raises questions: what kind of designs in particular are being uploaded and downloaded, and can the designers benefit financially from sharing their work for free? Based on former literature, this paper identifies four different strategies how to benefit financially from free 3D design sharing. By analysing the data gathered from Thingiverse, the largest free 3D design repository, it describes how these different strategies realise in the case of Thingiverse.

A Convolutional Neural Network Approach to the Classification of Engineering Models

This paper presents a deep learning approach for the classification of Engineering (CAD) models using Convolutional Neural Networks (CNNs). Owing to the availability of large annotated datasets and also enough computational power in the form of GPUs, many deep learning-based solutions for object classification have been proposed of late, especially in the domain of images and graphical models. Nevertheless, very few solutions have been proposed for the task of functional classification of CAD models. Hence, for this research, CAD models have been collected from Engineering Shape Benchmark (ESB), National Design Repository (NDR) and augmented with newer models created using a modeling software to form a dataset - `CADNET'. It is proposed to use a residual network architecture for CADNET, inspired by the popular ResNet. A weighted Light Field Descriptor (LFD) scheme is chosen as the method of feature extraction, and the generated images are fed as inputs to the CNN. The problem of class imbalance in the dataset is addressed using a class weights approach. Experiments have been conducted with other signatures such as geodesic distance etc. using deep networks as well as other network architectures on the CADNET. The LFD-based CNN approach using the proposed network architecture, along with gradient boosting yielded the best classification accuracy on CADNET.

Leveraging 3D Printing Capacity in Times of Crisis: Recommendations for COVID-19 Distributed Manufacturing for Medical Equipment Rapid Response

The SARS-CoV-2 (COVID-19) pandemic has provided a unique set of global supply chain limitations with an exponentially growing surge of patients requiring care. The needs for Personal Protective Equipment (PPE) for hospital staff and doctors have been overwhelming, even just to rule out patients not infected. High demand for traditionally manufactured devices, challenged by global demand and limited production, has resulted in a call for additive manufactured (3D printed) equipment to fill the gap between traditional manufacturing cycles. This method has the unique ability to pivot in real time, while traditional manufacturing may take months to change production runs. 3D printing has been used to produce a variety of equipment for hospitals including face shields, masks, and even ventilator components to handle the surge. This type of rapid, crowd sourced, design and production resulted in new challenges for regulation, liability, and distribution. This manuscript reviews these challenges and successes of additive manufacturing and provides a forward plan for hospitals to consider for future surge events. Recommendations: To accommodate future surges, hospitals and municipalities should develop capacity for short-run custom production, enabling them to validate new designs. This will rapidly increase access to vetted equipment and critical network sharing with community distributed manufacturers and partners. Clear guidance and reviewed design repositories by regulatory authorities will streamline efforts to combat future pandemic waives or other surge events.

Identification of Human–System Interaction Errors During Early Design Stages Using a Functional Basis Framework

Abstract Engineers have developed different design methodologies capable of identifying failure modes of engineering systems. The most common methods used in industry are failure modes and effects analysis, and failure modes effects and criticality analysis. Nevertheless, such methodologies have a significant limitation regarding incorporating the final user in the analysis and are not suited to identifying potential failure modes caused by physical human–system interactions. Engineering methods usually have a lack of sufficient attention to human–system interactions during the early design stages, even though introducing human factors principles is recognized as an essential analysis during the design process. As a result, designers rely on developing detailed and expensive physical or virtual prototypes to evaluate physical human–system interactions and identify potential failure modes caused by such interactions incorporating design modifications after a prototype is developed can be time-consuming, costly, and if significant changes are needed, the entire prototype requires to be constructed again. Identifying system–user interactions and possible failure modes associated with such interactions before developing a prototype can significantly improve the design process. In previous work, the authors introduced the function–human error design method (FHEDM), a tool capable of distinguishing possible human–system interaction failure modes using a functional basis framework. In this work, we examined the implementation of FHEDM within 148 products extracted from the design repository. The results are grouped in the composite function–user interaction error (FUIE) matrix, which can be used as a preliminary design database presenting information regarding the possible human error present in function-flow combinations.

How designers benefit from free 3D design sharing

3D printing has been described as a technological revolution, and it has already changed the way how some low-demand items are designed, produced and delivered to the customers. The threshold for new players entering the 3D business is lower than ever; the lowering costs of printers and the availability of free planning software provide new opportunities for entrepreneurs. However, at the same time, the availability of free 3D designs has exploded, and there is an enormous selection of them available. This raises questions: what kind of designs in particular are being uploaded and downloaded, and can the designers benefit financially from sharing their work for free? Based on former literature, this paper identifies four different strategies how to benefit financially from free 3D design sharing. By analysing the data gathered from Thingiverse, the largest free 3D design repository, it describes how these different strategies realise in the case of Thingiverse.

SBOLExplorer: Data Infrastructure and Data Mining for Genetic Design Repositories

This paper describes SBOLExplorer, a system that is used to provide intuitive searching within the SynBioHub genetic design repository. SynBioHub stores genetic constructs encoded in the SBOL data format. These constructs can represent genetic parts, circuits, and sequences. These constructs are often numerous, exist in various states of completeness and documentation, and do not lend themselves to simple searching and discovery. In particular, this paper focuses on improving the search capabilities of SynBioHub. Inspiration is drawn from the techniques used to organize and search over the World Wide Web, a linked data set with many of the same properties of the SBOL data in SynBioHub. SBOLExplorer integrates these methods into SynBioHub's data representation and search, providing significant improvement over the previous search implementation based on pattern-matching.

Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing

Abstract Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as GrabCAD and Thingiverse that are readily accessible online. In this study, we focus only on material extrusion, the dominant consumer AM process, and investigate three AM build metrics: (1) part mass, (2) support material mass, and (3) build time. Additionally, we compare the convolutional neural network accuracy to that of a baseline multiple linear regression model. Our results suggest that training on design repositories with less standardized orientation and position resulted in more accurate trained neural networks and that orientation-dependent metrics were harder to estimate than orientation-independent metrics. Furthermore, the convolutional neural network was more accurate than the baseline linear regression model for all build metrics.

Eligibility of BPMN Models for Business Process Redesign

Business process redesign (BPR) is an organizational initiative for achieving competitive multi-faceted advantages regarding business processes, in terms of cycle time, quality, cost, customer satisfaction and other critical performance metrics. In spite of the fact that BPR tools and methodologies are increasingly being adopted, process innovation efforts have proven ineffective in delivering the expected outcome. This paper investigates the eligibility of BPMN process models towards the application of redesign methods inspired by data-flow communities. In previous work, the transformation of a business process model to a directed acyclic graph (DAG) has yielded notable optimization results for determining average performance of process executions consisting of ad-hoc processes. Still, the utilization encountered drawbacks due to a lack of input specification, complexity assessment and normalization of the BPMN model and application to more generic business process cases. This paper presents an assessment mechanism that measures the eligibility of a BPMN model and its capability to be effectively transformed to a DAG and be further subjected to data-centric workflow optimization methods. The proposed mechanism evaluates the model type, complexity metrics, normalization and optimization capability of candidate process models, while at the same time allowing users to set their desired complexity thresholds. An indicative example is used to demonstrate the assessment phases and to illustrate the usability of the proposed mechanism towards the advancement and facilitation of the optimization phase. Finally, the authors review BPMN models from both an SOA-based business process design (BPD) repository and relevant literature and assess their eligibility.

Toward Automated Functional Modeling: An Association Rules Approach for Mining the Relationship between Product Components and Function

AbstractThe objective of this research is to support DfX considerations in the early phases of design. In order to do conduct DfX, designers need access to pertinent downstream knowledge that is keyed to early stage design activities and problem knowledge. Product functionality is one such “key” connection between early understanding of the design problem and component choices which dictate product performance and impact, and repositories of design knowledge are one way to archive such design knowledge. However, curation of design knowledge is often a time-consuming activity requiring expertise in product modeling. In this paper, we explore a method to automate the populating of design repositories to support the overall goal of having up-to-date repositories of product design knowledge. To do this, we mine information from an existing repository to better understand the relationships between the components, functions, and flows of products. The resulting knowledge can be applied to automate functional decompositions once a product's components have been entered and thus reliably provide that “key” between early design activities and the later, component dependent characteristics.

Design-by-Analogy: Exploring for Analogical Inspiration With Behavior, Material, and Component-Based Structural Representation of Patent Databases

Design-by-analogy (DbA) is an important method for innovation that has gained much attention due to its history of leading to successful and novel design solutions. The method uses a repository of existing design solutions where designers can recognize and retrieve analogical inspirations. Yet, exploring for analogical inspiration has been a laborious task for designers. This work presents a computational methodology that is driven by a topic modeling technique called non-negative matrix factorization (NMF). NMF is widely used in the text mining field for its ability to discover topics within documents based on their semantic content. In the proposed methodology, NMF is performed iteratively to build hierarchical repositories of design solutions, with which designers can explore clusters of analogical stimuli. This methodology has been applied to a repository of mechanical design-related patents, processed to contain only component-, behavior-, or material-based content to test if unique and valuable attribute-based analogical inspiration can be discovered from the different representations of patent data. The hierarchical repositories have been visualized, and a case study has been conducted to test the effectiveness of the analogical retrieval process of the proposed methodology. Overall, this paper demonstrates that the exploration-based computational methodology may provide designers an enhanced control over design repositories to retrieve analogical inspiration for DbA practice.