Abstract:Abstract-Concurrent engineering (CE) is a methodology applied to product lifecycle development so that high quality, well designed products can be provided at lower prices and in less time. Many research works have been proposed for efficiently modeling of different domains in CE. However, an integration of these works with consistent data flow is absent and still in great demand in industry. In this paper, we present a generic integration framework with a semantic feature model for knowledge representation an… Show more
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.
Digital twins in manufacturing plays a key factor for the digital transformation. A necessary component of any digital twin in manufacturing is a geometric model of a workpiece as it is processed through steps. DT requires solid 3d models, machining features, and information regarding machines, tools, and its constraints such as initial setup, machining direction, etc. The objective of this paper is to generate alternate feature interpretations to identify geometric constraints, machine and tool requirements, and stock materials to generate flexible manufacturing plans that fit a defined criterion. In this study we propose using the IMPlanner system to retrieve a 3d model from a CAD software, read its geometric features and convert them into possible machining features. This information along with information from the database of stock materials, tools, machines, and tolerances, the system generates several feature interpretations, thus offering a more flexible manufacturing plan.
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