دانلود رایگان مقاله انگلیسی تحلیل داده ها در سناریو اینترنت اشیا / اینترنت داده ها - تیلور و فرانسیس 2018

ABSTRACT

Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/ IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNAbased knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.

Results and Discussion

Table 1 gives the sample query that was executed to find the most similar SOEKS. For example, in query 1, VEP similarity is calculated for a product CLY-1 when total time ¼ 12 min, tolerance ¼ −0.1 and finish ¼ 1.8. Figure 9 illustrates the execution of this query. CIM-DNA returns the top most similar SOEKS which, in this particular case, is VEP-Code no 9 having similarity 0.877. The query also returns the codes of ASRS-VEO, Robot-VEO, Lathe-VEO, Arm-VEO, and Mill-VEO for the most similar VEP-Code (Table 1). This enables to fetch all the micro-level details of each component corresponding to most similar VEP-SOEKS.

The approach helps to categorize the past decisions taken on the CIM and then prioritize them according to the situation.

The main contribution of this work is to demonstrate and implement knowledge-based CIM environment in data-intensive Iot/IoD scenario. The CIM-DNA, which is the representation of manufacturing process collective computational intelligence, is created by capturing the experience of engineering objects and engineering processes and then using this information for the construction of VEO and VEP. The SOEKS and DDNA are applied as the knowledge representation structure for gathering the experience. Furthermore, VEF–VEP is used as a tool for decision-making processes that can enhance different CIM systems with predicting capabilities and facilitate knowledge engineering processes. Moreover, CIM-DNA readily copes with self-organizing production and control strategies; this is a strong linking instance of product life-cycle management, industrial automation, and semantic technologies as required for cyber-physical systems and Industry 4.0.