Property model methodology : a case study with Modelica

The aim of this paper is twofold. Firstly, it is intend-ed to demonstrate the relevance of the Property Model Methodology (PMM) to specify, validate, de-sign and verify continuous multi-physics systems. Secondly, it aims at verifying the compatibility of PMM concepts with the Modelica simulation lan-guage. We will be using the case study of an aircraft landing gear to show how to translate the theoretical concepts of PMM into executable Modelica models. This article proves the fundamental concepts of PMM and provides a starting point for further re-search so as to not only model other types of engi-neered systems such as discrete and hybrid systems, but also support additional systems engineering ac-tivities, such as safety-reliability

Requirement mining for model-based product design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Requirement Mining for Model-Based Product Design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Property Model Methodology: A case study with Modelica

International audienceThe aim of this paper is twofold. Firstly, it is intended to demonstrate the relevance of the Property Model Methodology (PMM) to specify, validate, design and verify continuous multi-physics systems. Secondly, it aims at verifying the compatibility of PMM concepts with the Modelica simulation language. We will be using the case study of an aircraft landing gear to show how to translate the theoretical concepts of PMM into executable Modelica models. This article proves the fundamental concepts of PMM and provides a starting point for further research so as to not only model other types of engineered systems such as discrete and hybrid systems, but also support additional systems engineering activities, such as safety-reliability

An illustrated glossary of ambiguous PLM terms used in discrete manufacturing

Product lifecycle management (PLM) is a strategic product-centric, lifecycle-oriented and information-driven business approach that strives to integrate people and their inherent practices, processes, and technologies, both within and across functional areas of the extended enterprise from inception to disposal. The integration of people relies on the harmonisation of domain-specific glossaries by standardising a universal PLM vocabulary. So far, unfortunately, there is no PLM standard vocabulary. Therefore, the tremendous amount of knowledge that is continually brought forward by academic research studies, industrial practices and computer-aided applications causes semantic ambiguities. This paper consists of an illustrated glossary and a conceptual map. The glossary identifies, discusses, clarifies and illustrates ambiguous terms used in discrete manufacturing. The conceptual map finally underlines the logical flow of refereed definitions

Requirement mining for model-based product design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Requirement Mining for Model-Based Product Design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Natural Language Processing of Requirements for Model-Based Product Design with ENOVIA CATIA V6

The enterprise level software application that supports the strategic product-centric, lifecycle-oriented and information-driven Product Lifecycle Management business approach should enable engineers to develop and manage requirements within a Functional Digital Mock-Up. The integrated, model-based product design ENOVIA/CATIA V6 RFLP environment makes it possible to use parametric modelling among requirements, functions, logical units and physical organs. Simulation can therefore be used to verify that the design artefacts comply with the requirements. Nevertheless, when dealing with document-based specifications, the definition of the knowledge parameters for each requirement is a labour-intensive task. Indeed, analysts have no other alternative than to go through the voluminous specifications, to identify the performance requirements and design constraints, and to translate them into knowledge parameters. We propose to use natural language processing techniques to automatically generate Parametric Property-Based Requirements from unstructured and semi-structured specifications. We illustrate our approach through the design of a mechanical ring

Design rules management, state of the art analyze and proposal of a context-aware approach

In manufacturing industries, the design of a product needs to comply with many design rules. These rules are essentials as they help industrial designers to create high quality conception in an efficient way. [Problem] Howev-er, the management of an ever-increasing number of design rules becomes a real problem, especially for new designers. Even if there exists some knowledge man-agement tools for design rules, their capabilities are still limited and many compa-nies continue to store their design rules in unstructured documents. Nowadays, de-sign rule application is still a difficult task that needs a circular validation process between many expert services in a manufacturing company. [Proposition] In this paper, we will analyze the main existing approaches for design rules application from which we will demonstrate the need of a new approach to improve the cur-rent state-of-the-art practices. To minimize rule application impact on the design process, we propose to develop a Context-Aware Design Assistant that will per-form design rule recommendation on the fly while designing using computer-aided technologies. Our Design Assistant relies on the modelling of the design rules and the design context in a single knowledge graph that can fuel a recom-mendation engine. [Future Work] In future work, we will describe the technical structure of the Context-Aware Design Assistant and develop it. The potential out-come of this research are: a better workflow integration of design rules applica-tion, a proactive verification of design solutions, a continuous learning of design rules, the detection and automation of design routines

An illustrated glossary of ambiguous PLM terms used in discrete manufacturing

Product lifecycle management (PLM) is a strategic product-centric, lifecycle-oriented and information-driven business approach that strives to integrate people and their inherent practices, processes, and technologies, both within and across functional areas of the extended enterprise from inception to disposal. The integration of people relies on the harmonisation of domain-specific glossaries by standardising a universal PLM vocabulary. So far, unfortunately, there is no PLM standard vocabulary. Therefore, the tremendous amount of knowledge that is continually brought forward by academic research studies, industrial practices and computer-aided applications causes semantic ambiguities. This paper consists of an illustrated glossary and a conceptual map. The glossary identifies, discusses, clarifies and illustrates ambiguous terms used in discrete manufacturing. The conceptual map finally underlines the logical flow of refereed definition