Industry 4.0 Industry 4.0 is a project in the high-tech strategy of the German government, which promotes the computerization of the manufacturing industry. The goal is the intelligent factory (#SmartFactory), which is characterized by adaptability, resource efficiency and ergonomics as well as the integration of customers and business partners in business and value processes. Technological basis are cyber-physical systems and the Internet of Things. Experts[who?] believe that Industry 4.0 or the fourth industrial revolution could be a reality in about 10 to 20 years. Meanwhile, in the United States, an initiative known as the #SmartManufacturingLeadershipCoalition is also working on the future of manufacturing. Smart Manufacturing Leadership Coalition (#SMLC) is a non-profit organization of manufacturing practitioners, suppliers, and technology companies; manufacturing consortia; universities; government agencies and laboratories. The aim of this coalition is to enable stakeholders in the manufacturing industry to form collaborative R & D, implementation and advocacy groups for development of the approaches, standards, platforms and shared infrastructure that facilitate the broad adoption of manufacturing intelligence. Similarly, GE has been working on an initiative called The Industrial Internet. #TheIndustrialInternet aims to bring together the advances of two transformative revolutions: the myriad machines, facilities, fleets and networks that arose from the Industrial Revolution, and the more recent powerful advances in computing, information and communication systems brought to the fore by the Internet Revolution. According to GE, together these developments bring together three elements, which embody the essence of the Industrial Internet: INTELLIGENT MACHINES, ADVANCED ANALYTICS and PEOPLE AT WORK. Description The term industrie 4.0 refers to the fourth industrial revolution. The first industrial revolution was the mechanization of production using water and steam power, it was followed by the second industrial revolution which introduced mass production with the help of electric power, followed by the digital revolution, the use of electronics and IT to further automate production. The term was first used in 2011 at the Hanover Fair. In October 2012 the Working Group on Industry 4.0 chaired by Siegfried Dais ( Robert Bosch GmbH ) and Kagermann (acatech) presented a set of Industry 4.0 implementation recommendations to the German federal government. On 8 April 2013 at the Hanover Fair the final report of the Working Group Industry 4.0 was presented. Industry 4.0 is based on cyber physical production systems (#CPPS) which can be based on a 5C architecture (connection, conversion, cyber, cognition, and configuration). Please see imscenter.net/cyber-physical-platform . In the Connection level, devices can be designed to self-connect and self-sensing for its behavior. In the Conversion level, data from self-connected devices and sensors are measuring the features of critical issues with self-aware capabilities, machines can use the self-aware information to self-predict its potential issues. In the Cyber level, each machine is creating its own twin by using these instrumented features and further characterize the machine health pattern based on a Time-Machine methodology. The established twin in the cyber space can perform self-compare for peer-to-peer performance for further synthesis. In the Cognition level, the outcomes of self-assessment and self-evaluation will be presented to users based on an infographic meaning to show the content and context of the potential issues. In the Configuration level, the machine or production system can be reconfigured based on the priority and risk criteria to achieve resilient performance. The five levels of CPPS Architecture can be described as: Smart Connection- Acquiring accurate and reliable data from machines and their components is the first step in developing a cyber-physical system application. The data might be directly measured by sensors or obtained from controller or enterprise manufacturing systems such as ERP, MES, SCM and CMM. Two important factors at this level have to be considered. First, considering various types of data, a seamless and tether-free method to manage data acquisition procedure and transferring data to the central server is required where specific protocols such as MTConnect and etc. are effectively useful. On the other hand, selecting proper sensors (type and specification) is the second important consideration for the first level. Data-to-Information Conversion-Meaningful information has to be inferred from the data. Currently, there are several tools and methodologies available for the data to information conversion level. In recent years, extensive focus has been applied to develop these algorithms specifically for prognostics and health management applications. By calculating health value, estimated remaining useful life and etc., the second level of CPS architecture brings self-awareness to machines. Cyber-The cyber level acts as central information hub in this architecture. Information is being pushed to it from every connected machine to form the machines network. Having massive information gathered, specific analytics has to be used to extract additional information that provide better insight over the status of individual machines among the fleet. These analytics provide machines with self-comparison ability, where the performance of a single machine can be compared with and rated among the fleet and on the other hand, similarities between machine performance and previous assets (historical information) can be measured to predict the future behavior of the machinery. In this paper we briefly introduce an efficient yet effective methodology for managing and analyzing information at cyber level. Cognition-Implementing CPS upon this level generates a thorough knowledge of the monitored system. Proper presentation of the acquired knowledge to expert users supports the correct decision to be taken. Since comparative information as well as individual machine status is available, decision on priority of tasks to optimize the maintaining process can be made. For this level, proper info-graphics are necessary to completely transfer acquired knowledge to the users. Configuration-The configuration level is the feedback from cyber space to physical space and act as supervisory control to make machines self-configure and self-adaptive. This stage acts as resilience control system (RCS) to apply the corrective and preventive decisions, which has been made in cognition level, to the monitored system. Meaning Characteristic for industrial production in an Industry 4.0 environment are the strong customization of products under the conditions of high flexibilized (mass-) production. The required automation technology is improved by the introduction of methods of self-optimization, self-configuration, Self-diagnosis, cognition and intelligent support of workers in their increasingly complex work. The largest project in Industry 4.0 at the present time is the BMBF leading-edge cluster Intelligent Technical Systems OstWestfalenLippe (its OWL). Another major project is the BMBF project RES-COM, as well as the Cluster of Excellence Integrative Production Technology for High-Wage Countries. Effects Recently, McKinsey released an interview featuring an expert discussion between executives at Robert Bosch - Siegfried Dais (Partner of the Robert Bosch Industrietreuhand KG) and Heinz Derenbach (CEO of Bosch Software Innovations GmbH), and McKinsey experts. This interview addressed the prevalence of the Internet of Things in manufacturing and the consequent technology-driven changes that promise to trigger a new industrial revolution. At Bosch, and generally in Germany, this phenomenon is referred to as Industry 4.0. The basic principle of Industry 4.0 is that by connecting machines, work pieces and systems, we are creating intelligent networks along the entire value chain that can control each other autonomously. Some examples for Industry 4.0 are machines that predict failures and trigger maintenance processes autonomously or self-organized logistics that react to unexpected changes in the production. According to Siegfried Dais, “it is highly likely that the world of production will become more and more networked until everything is interlinked with everything else.” While this sounds like a fair assumption and the driving force behind the Internet of Things, it also means that the complexity of production and supplier networks will grow enormously. Networks and processes have so far been limited to one factory. But in an Industry 4.0 scenario, these boundaries of individual factories will most likely no longer exist. Instead, they will be lifted in order to interconnect multiple factories or even geographical regions. The differences between a todays factory and an Industry 4.0 factory. In current industry environment, providing high-end quality service or product with the least cost is the key to success and industrial factories are trying to achieve as much performance as possible to increase their profit as well as their reputation. In this way, various data sources are available to provide worthwhile information about different aspects of the factory. In this stage, the utilization of data for understanding the current condition and detect faults and failures is an important topic to research. e. g. in production, there are various commercial tools available to provide OEE (Overall Equipment Effectiveness) information to factory management in order to highlight root cause of problems and possible faults in the system. In contrast, in an Industry 4.0 factory, in addition to condition monitoring and fault diagnosis, components and systems are able to gain self-awareness and self-predictiveness, which will provide management with more insight on the status of the factory. Furthermore, peer-to-peer comparison and fusion of health information from various components provides a precise health prediction in component and system levels and enforce factory management to trigger required maintenance at the best possible time to reach just-in time maintenance and gain near zero downtime. Challenges 1. Lack of adequate skill-sets to expedite the march towards fourth industrial revolution 2. Threat of redundancy of the corporate IT department 3. General reluctance to change by stakeholders Role of big data and analytics Modern information and communication technologies like Cyber-Physical Systems, Big Data or Cloud Computing will help predict the possibility to increase productivity, quality and flexibility within the manufacturing industry and thus to understand advantages within the competition. Big Data Analytics consists of 6Cs in the integrated Industry 4.0 and Cyber Physical Systems environment. 6C system that is consist of Connection (sensor and networks), Cloud (computing and data on demand), Cyber (model & memory), Content/context (meaning and correlation), Community (sharing & collaboration), and Customization (personalization and value). In this scenario and in order to provide useful insight to the factory management and gain correct content, data has to be processed with advanced tools (analytics and algorithms) to generate meaningful information. Considering the presence of visible and invisible issues in an industrial factory, the information generation algorithm has to capable of detecting and addressing invisible issues such as machine degradation, component wear, etc in the factory floor. Impact of the Industry 4.0 There are many areas that are foreseen to have an impact with the advent of the fourth industrial revolution. Of which four key impact areas emerge: 1. Machine safety 2. Industry value chain 3. Workers 4. Socio-economic 5. Industry Demonstration: To help industry understand the impact of Industry 4.0, Cincinnati Mayor, John Carnely, signed a proclamation to state Cincinnati to be Industry 4.0 Demonstration City. See also Big data Computer-integrated manufacturing Digital modeling and fabrication Industrial control system Industrial Internet Intelligent Maintenance Systems Internet of Things Machine to machine Predictive manufacturing system SCADA
Posted on: Fri, 16 Jan 2015 06:50:12 +0000
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