This paper (Wu 2016), which was published in AI EDAM online on August 22, 2016, has been retracted by Cambridge University Press as it is very similar in content to a published ASME Conference Proceedings paper. The article in question and the ASME Conference Proceedings paper were submitted for review with AI EDAM and the ASME at similar times, but copyright was assigned to ASME before the paper was accepted in AI EDAM and therefore the article in AI EDAM is being retracted. (In recent years, industrial nations around the globe have invested heavily in new technologies, software, and services to advance digital design and manufacturing using cyber-physical systems, data analytics, and high-performance computing. Many of these initiatives, such as cloud-based design and manufacturing, fall under the umbrella of what has become known as Industry 4.0 or Industrial Internet and are often hailed as pillars of a new industrial revolution. While an increasing number of companies are developing or already offer commercial cloud-based software packages and services for digital design and manufacturing, little work has been reported on providing a review of the state of the art of these commercial software and services as well as identifying research gaps in this field. The objective of this paper is to present a state-of-the-art review of digital design and manufacturing software and services that are currently available on the cloud. The focus of this paper is on assessing to what extent engineering design, engineering analysis, manufacturing, and production across all phases of the product development lifecycles can already be performed based on the software and services accessed through the cloud. In addition, the key capabilities and benefits of these software packages and services are discussed. Based on the assessment of the core features of commercial software and services, it can be concluded that almost all phases of product realization can be conducted through digital design and manufacturing software and services on the cloud. Finally, existing research gaps and related challenges to overcome are identified. The state-of-the-art review serves to provide a technology guide for decision makers in their efforts to select suitable cloud-based software and services as alternatives to existing in-house resources as well as to recommend new research areas.)

Current methods to develop standard Architecture, Engineering, Construction (AEC) product models focus on the definition of product model semantics without concurrent and formal consideration of the engineering analyses that such models must support, or formal consideration of the requirements for sharing information between applications. We present two case studies that demonstrate a service to extract data from product models and provide inputs to component analysis applications. The service was validated in a proof-of-concept application called the Internet Broker for Engineering Services (IBES) that extracts information for component analysis from product models that are external to the application and accessed across the Internet. IBES was tested for two research cases. The product model for the first case, control valve selection is based on STEP Application Protocol 227. The product model for the second case, control valve diagnosis, specifies additional semantics that support the operations and maintenance (O&M) phase of the facility life cycle. The cases offer evidence that large standard data models can support routine analyses for control valves. However, the amount of shared information between the case applications is small and is largely dependent upon the concurrence of component behaviors that are necessary to model analysis. The IBES reference model and reasoning to support information extraction was consistent for both cases. This consistency suggests that it is possible to define a general set of computational methods that integrate project information models with external component analysis applications across the product life cycle. We argue that enabling a web-based link between product models and applications requires a set of capabilities, including bi-directional communication between separated data and analysis nodes, query generation, data translation, and validation of data extracted from semistandard models. We discuss the tentative implication that minimal shared information calls into question the assumption that large core product models will work effectively in practice.