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Cascading Forgetting in Product Development Challenges and Evaluation

Proceedings of the Design Society: International Conference on Engineering Design 2019年第1期1卷 2527-2536页

作者： Patricia Kügler Claudia Schon Benjamin Schleich Steffen Staab Sandro Wartzack Friedrich-Alexander-Universität Erlangen-Nürnberg University of Koblenz-Landau

Vast amounts of information and knowledge is produced and stored within product design projects. Especially for reuse and adaptation there exists no suitable method for product designers to handle this information overload. Due to this, the selection of relevant information in a specific development situation is time-consuming and inefficient. To tackle this issue, the novel approach Intentional Forgetting (IF) is applied for product design, which aims to support reuse and adaptation by reducing the vast amount of information to the relevant. Within this contribution an IF-operator called Cascading Forgetting is introduced and evaluated, which was implemented for forgetting related information elements in ontology knowledge bases. For the evaluation the development process of a test-rig for studying friction and wear behaviour of the cam/tappet contact in combustion engines is analysed. Due to the interdisciplinary task of the evaluation and the characteristics of semantic model, challenges are discussed. In conclusion, the focus of the evaluation is to consider how reliable the Cascading Forgetting works and how intuitive ontology-based representations appear to engineers.

关键词： Intentional Forgetting Ontologies Knowledge management semantic data processing

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semantic NETWORKS FOR ENGINEERING DESIGN: A SURVEY

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Proceedings of the Design Society 2021年 1卷 2621-2630页

作者： Ji Han Serhad Sarica Feng Shi Jianxi Luo University of Liverpool Singapore University of Technology and Design Amazon Web Services

There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

关键词： semantic data processing Knowledge management Design informatics semantic network Knowledge base

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EFFICIENT FORMALISATION OF TECHNICAL REQUIREMENTS FOR GENERATIVE ENGINEERING

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Proceedings of the Design Society 2023年 3卷 1595-1604页

Currently, engineers need to manually analyse requirement specifications for determining parameters to create geometries in generative engineering. This analysis is time-consuming, error-prone and causes high costs. Generative engineering tools (e.g. Synera) cannot interpret natural language requirements directly. The requirements need to be formalised in a machine-readable format. AI algorithms have the potential to automatically transform natural language requirements into such a formal, machine-readable representation. In this work, a method for formalising requirements for generative engineering is developed and implemented as a prototype in Python. The method is validated in a case example using three products of an automotive engineering service provider. Requirements to be formalised are identified in the specifications of these three products, which are used as a test set to evaluate the performance of the method. The results show that requirements for generative engineering are formalised with high performance (F1 of 86.55 %). By applying the method, efforts and therefore costs for manually analysing requirements regarding parameters for generative engineering are reduced.

关键词： Requirements Artificial intelligence semantic data processing Computational design methods

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Supporting Knowledge Re-Use with Effective Searches of Related Engineering Documents - A Comparison of Search Engine and Natural Language processing-Based Algorithms

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Proceedings of the Design Society: International Conference on Engineering Design 2019年第1期1卷 2597-2606页

作者： Ivar Örn Arnarsson Otto Frost Emil Gustavsson Daniel Stenholm Mats Jirstrand Johan Malmqvist Chalmers University of Technology Fraunhofer-Chalmers Centre

Product development companies are collecting data in form of Engineering Change Requests for logged design issues and Design Guidelines to accumulate best practices. These documents are rich in unstructured data (e.g., free text) and previous research has pointed out that product developers find current it systems lacking capabilities to accurately retrieve relevant documents with unstructured data. In this research we compare the performance of Search Engine & Natural Language processing algorithms in order to find fast related documents from two databases with Engineering Change Request and Design Guideline documents. The aim is to turn hours of manual documents searching into seconds by utilizing such algorithms to effectively search for related engineering documents and rank them in order of significance. Domain knowledge experts evaluated the results and it shows that the models applied managed to find relevant documents with up to 90% accuracy of the cases tested. But accuracy varies based on selected algorithm and length of query.

关键词： Natural Language processing semantic data processing Machine learning Knowledge management

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AN AUTOMATED METHOD TO CONDUCT IMPORTANCE-PERFORMANCE ANALYSIS OF PRODUCT ATTRIBUTES FROM ONLINE REVIEWS - AN EXTENSION WITH A CASE STUDY

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Proceedings of the Design Society 2021年 1卷 417-426页

作者： Kangcheng Lin Harrison Kim University of Illinois at Urbana-Champaign

With the growth of online marketplaces and social media, product designers have been seeing an exponential growth of data available, which can serve as an extremely valuable source of information communicated from customers without geographical limitations. The data will reveal customers’ preferences, which can be expensive and slow to obtain via traditional methods such as survey and questionnaires. While existing methods in the literature have been proposed to extract product information and make inference from online data, they have limitations, especially in providing reliable results and in dealing with data sparsity. Therefore, this paper proposes a method to conduct an Important-performance analysis from online reviews. The major steps of this method involve using latent Dirichlet allocation (LDA) to identify product attributes, using IBM Watson Natural Language Understanding tool to perform aspect-based sentiment analysis, and using XGBoost model to infer product attribute importance from the collected dataset. In our case study, we have collected over 150,000 text reviews of more than 3,000 laptops from Amazon.

关键词： Big data semantic data processing Market implications

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DESIGN KNOWLEDGE REPRESENTATION WITH TECHNOLOGY semantic NETWORK

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Proceedings of the Design Society 2021年 1卷 1043-1052页

作者： Serhad Sarica Jianxi Luo Engineering Product Development Pillar Singapore University of Technology and Design

Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

关键词： semantic data processing Design informatics Visualisation Technology semantic Network Knowledge Representation

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Integrating the Mechanical Domain into Seed Approach

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Proceedings of the Design Society: International Conference on Engineering Design 2019年第1期1卷 2587-2596页

作者： Fabian Dworschak Patricia Kügler Benjamin Schleich Sandro Wartzack

data-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in *** fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.

关键词： Ontologies semantic data processing Knowledge management

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ANALYSIS OF CUSTOMER SENTIMENT ON PRODUCT FEATURES AFTER THE OUTBREAK OF CORONAVIRUS DISEASE (COVID-19) BASED ON ONLINE REVIEWS

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Proceedings of the Design Society 2021年 1卷 457-466页

作者： Jinju Kim Seyoung Park Harrison Kim University of Illinois at Urbana-Champaign

The outbreak of the coronavirus disease not only caused many deaths worldwide but also severely affected the development of the global economy, such as supply chain disruptions, plummeted demand, unemployment, etc. These social changes have led to changes in customers' purchasing patterns. Therefore, it is more important than ever for manufacturers to quickly identify and respond to changing customer purchasing patterns and requirements. However, few studies have been done on dynamic changes in customer preferences for product features following COVID-19 spread. This study aims to investigate the dynamic change of customer sentiment on product features following COVID-19 through sentiment analysis based on online reviews. The proposed methodology consists of two main processes: feature extraction and sentiment analysis. After finding a specific feature of the product through feature extraction, the words used to mention the feature in the review were analyzed for sentiment analysis of customers. To demonstrate the methodology, a case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment during COVID-19.

关键词： semantic data processing Machine learning Market implications

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REPRESENTATION AND APPLICATION OF DIGITAL STANDARDS USING KNOWLEDGE GRAPHS

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Proceedings of the Design Society 2021年 1卷 2551-2560页

作者： Janosch Luttmer Dominik Ehring Robin Pluhnau Arun Nagarajah University Duisburg-Essen

Standards are an important source of knowledge in product development. Due to the increasing digitization of the product development process, standard development organizations aim to develop machine-actionable standards that automatically enforce operations in output devices. However, the current representation format in PDF or XML does not meet the requirements of machine-actionable standards. This paper examines existing approaches towards the representation of XML data in knowledge graphs and their transferability towards the domain of digital standards. Based on these approaches, the paper aims to develop and validate a concept for transferring standard content from XML format to a graph-based representation, using the example of formulas. For this purpose, a concept for the automatic identification, extraction and modeling of formulas will be presented. Afterwards, the concept is validated using the example of DIN ISO 281 whereas a chatbot application serves as conversational user interface. It is proven, that knowledge graphs are suitable for the representation of machine-actionable standard content. Future work will investigate the abstraction towards a general approach as well as further information objects of standards.

关键词： Standards Conceptual design Knowledge graphs Knowledge management semantic data processing

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DETECTING BAD DESIGN AND BIAS FROM PATENTS

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Proceedings of the Design Society 2021年 1卷 1173-1182页

作者： Nicola Melluso Sara Pardelli Gualtiero Fantoni Filippo Chiarello Andrea Bonaccorsi University of Pisa B4DS Lab - Business Engineering for Data Science Erre Quadro s.r.l.

The representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and *** this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that defines syntactic rules to detect sentences containing these artifacts. These rules are defined based on an exploratory analysis of the explicit mention of “bad design” and “bias” and then, tested with multiple experiments on a sample of patents. The results give a first quantitative evidence of the presence of bad design and biases in patents and consequently of their importance in the design theory. In particular, it is provided a fine grain analysis of the linguistic structure of sentences containing these artifacts helping designers in detecting automatically them from patents.

关键词： Design cognition Design theory semantic data processing Bad Design Bias

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