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Cyber physical systems (CPS) in a manufacturing and automation context can be referred to different manufacturing process, including design, simulation, control, and verification. However, for data analytics, the concept of CPS is relatively new, and a standard methodology is lacking on how to incorporate this type of interface for automation applications. This study discusses a modeling methodology for a cyber physical interface and presents the five levels of information for a cyber physical system, that range from the data connection level to the system configuration level. In order to achieve this awareness and health state of the machine and system, a technical approach that uses adaptive health monitoring algorithms is presented. Lastly, an experimental study on a machine tool ball screw is highlighted, in which a predictive model and a cyber physical interface is developed for this application. The outcomes from this study demonstrate that machine health state awareness is feasible, and the core technologies can aim mechanical systems systematically develop its CPS. This can lead to additional product revenue for the manufacturers, and also a potential competitive edge in the market place.

Cloud computing has brought about new service models and research opportunities in the manufacturing and service indus-tries with advantages in ubiquitous accessibility, convenient scal-ability, and mobility. With the emerging industrial big data prompted by the advent of the internet of things and the wide implementation of sensor networks, the cloud computing para-digm can be utilized as a hosting platform for autonomous data mining and cognitive learning algorithms. For machine health monitoring and prognostics, we investigate the challenges imposed by industrial big data such as heterogeneous data format and complex machine working conditions and further propose a systematically designed framework as a guideline for implement-ing cloud-based machine health prognostics. Specifically, to ensure the effectiveness and adaptability of the cloud platform for machines under complex working conditions, two key design methodologies are presented which include the standardized fea-ture extraction scheme and an adaptive prognostics algorithm. The proposed strategy is further demonstrated using a case study of machining processes. [DOI: 10.1115/1.4030669]

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Abstract. For manufacturing enterprises, product quality is a key factor to assess production capability and increase their core competence. To reduce external failure cost, many research and methodology have been introduced in order to improve process yield rate, such as TQC/TQM, Shewhart Cycle Deming's 14 Points, etc. Nowadays, impressive progress has been made in process monitoring and industrial data analysis because of the Industry 4.0 trend. Industries start to utilize quality control (QC) methodology to lower inspection overhead and internal failure cost. Currently, the focus of QC is mostly in the inspection of single workstation and final product, however, for multistage manufacturing, many factors (like equipment, operators, parameters, etc.) can have cumulative and interactive effects to the final quality. When failure occurs, it is difficult to resume the original settings for cause analysis. To address these problems, this research proposes a combination of principal components analysis (PCA) with classification and association rule mining algorithms to extract features representing relationship of multiple workstations, predict final product quality, and analyze the root-cause of product defect. The method is demonstrated on a semiconductor data set.

Over recent years, a significant amount of research has been dedicated to the development of Prognostics and Health Management (PHM) models. However, less attention is paid towards implementing the developed models to real-world applications to serve the needs of industry. In order to successfully implement a PHM system, a systematic approach is required to deploy the developed analytic tools (algorithms, software and agents) using a scalable hardware platform. In this paper, different PHM deployment platforms including stand-alone PC, embedded and cloud-based platforms are benchmarked. Then, a unified strategy for deploying the developed PHM tools using each of these platforms is presented. A smart deployment platform selection method using Quality Function Deployment (QFD) is also introduced. Following that, several case studies from different applications are provided as examples to demonstrate the capabilities and limitations of each deployment platform.