Customer Stories

SmartFactoryKL- IBM’s learning factory and Industrie 4.0 Reference Architecture example

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In my previous post, I shared the announcement of IBM’s IoT Industrie 4.0 reference architecture. In this post, I share the news from SmartFactoryKL, the leading partner in one of the five first Mittelstand 4.0 Competence Centers (Small and Medium-sized Enterprises 4.0) announced by the Germany Ministry of Economics and Energy (BMWi). Under the motto “Progress within the network”, within the SmartFactoryKL association, industry and research partners work on new concepts, standards and solutions that form the basis for highly flexible automation technology. As a result, SmartFactoryKL has developed the world’s first manufacturer-independent Industrie 4.0 plant together with its partners.

The production line represents a modular, flexible and configurable system, while it combines production components and system control elements from different manufacturers. It is highly interoperable through universal plugin connections for electricity, compressed air and industrial Ethernet. A standardized infrastructure allows uniform connection of all production modules to coordinate IT services and enable a manufacturer–independent compatibility based on standard protocols like OPC UA and MQTT as well as a common shopfloor data model.

SmartFactoryKL: Industrie 4.0 use cases

Some of the advanced Industrie 4.0 use cases SmartFactoryKL has implemented are:

  • Standardization on hardware and software interfaces, protocols, data models
  • Plug-and-Produce, enabling the a flexible production line re-configuration by fulfilling compliance standards
  • Digital product memory
  • Island production and flexible transport systems
  • Machine-level and production-line-level Digital Twin representation
  • OEE (Overall Equipment Effectiveness) in real-time
  • Cloud-based HMI (Human-Machine Interface) for production flow control
  • Predictive maintenance
  • Lifecycle integration between production and PLM (Product Lifecycle Management)
  • Cognitive Factory by example in the area of maintenance and repair.

IBM has been one of SmartFactoryKL’s core partners since the beginning of 2015. IBM’s main contributions were the flexible integration of all different machines, IT systems and applications, a Digital Twin realization of the whole production line based on IBM’s Analytics and IoT technology as well as a demonstration of the Cognitive Factory capabilities based on Watson. Meanwhile, SmartFactoryKL has become the learning and showcase factory for IBM and its strategic technology in the area of Industrie 4.0 and addresses the full scope of the IBM Industrie 4.0 Reference Architecture.

SmartFactoryKL: Industrie 4.0 architecture

The following diagram shows SmartFactoryKL’s architecture in the style of the IBM Industrie 4.0 Reference Architecture:

SmartFactoryKL Architecture Overview
SmartFactoryKL in the Industrie 4.0 Reference Architecture notation (click to enlarge)

Below are the flows representing how the production process works delivering a personalized business card holder as a sample product:

➀ proALPHA as SmartFactoryKL’s ERP system initiates an order with customer details, which is sent via Web Service to the Plant Service Bus (PSB) based on IBM Integration Bus.

➁ The Manufacturing Execution System (MES) from iTAC receives via Web Service the order information. With the help of the events distributed by PSB from and to all production modules during the process, the MES delivers the production control and OEE information. The manufacturing process is started in parallel by an event to the Storage Module from Pilz to request a new workpiece transporter.

➂ The module Bottom Engraving by Festo initializes the digital product memory to the particular production order via RFID at the bottom part. The Clip Module by BoschRexroth mounts a retaining clip. The Force Fitting module by Harting performs the central mounting of the two housing parts. The Flexible Transport System by Festo is a self-navigating robot system that links the various production cells based on the product memory in the RFID tag. The Phoenix Contact module Laser Marking puts an individual engraving on the top side of the business card holder. The Weighing Module by Mettler Toledo and the Quality Control Module by Lapp Kabel perform quality management. A Manual Workstation by MiniTec provides manual assembly alternative if problems occur in the automated production flow. Infrastructure Modules by TE, CISCO, Weidmüller, Belden/Hirschmann, Phoenix Contact and Harting supply the modules with electrical power, data connectivity and compressed air. All messages (OPC UA, MQTT, TCP/IP) between production and infrastructure modules and IT infrastructure systems and applications are delivered by the Plant Services Bus with the IBM Integration Bus providing routing, transformation and mediation both for information and control flow.

➃ In order to provide an instant overview of the status and operation, all modules-initiated messages are routed to a dashboard by IBM Cognos Analytics, representing a Digital Twin for the whole production line with real-time and historic data as well as topology change visualization.

➄ A second cloud-based HMI is delivered by Watson IoT providing the production flow control for all orders and machine status of the modules by a dashboard implemented in IBM Bluemix. An interesting detail is the speed of its realization: two days after getting the idea to create this new use case for the Hanover Industry Fair in April 2017, the dashboard was ready and operational. Main contributor for this extreme flexibility and agility was the presence of the PSB that just routed the already collected data to the Watson IoT Platform by a secure connection. The rest was just visualization of the orders at the particular module based on the data.

➅ As part of a scenario, showing the lifecycle integration between production and PLM, in case of a problem of a particular module, PSB generates an email to a service technician including the link to the EPLAN electrical wiring diagram of the failed module. Given this alert, he/she is able to check the engineering instructions to start a repair.

➆ In order to support service technicians in recognizing and fixing production machines failures, so-called Repair Experience with Watson delivers a realization of IBM’s view on a Cognitive Factory in the following way: after getting the alert and arriving at the failed module, the service technician takes a photo of the failed machine, which is sent to Watson Visual recognition service. The machine failure then could be read out of the machine’s error log and audibled for the service technician by Watson Text-to-Speech Service. Afterwards, the latter starts a conversation about how and when to fix the failure, based on further Watson services, like Conversation, Speech-to-Text, Retrieve & Rank.

➇ Based on the collected data via PSB, a Predictive Maintenance scenario has been implemented: years ago, a sporadic motor outage of the transport system occurred at different production modules. It appeared that when two transport belts got in contact, the motors of the two adjacent modules became overloaded and burned out. During the overload, an extended energy consumption of the two modules were observed. Based on machine learning algorithms with IBM SPSS Modeler, expected motor failures have been predicted in advance and mitigation actions could be initiated.

➈ Finally, in order to guarantee compliance with certification authorities, a use case with TüV Süd allows detection of new production line configuration, check with certification procedures and notification of compliance.

The above use cases demonstrate a typical Industrie 4.0 project in a public-private initiative combining the expertise of renowned partners and their technology in an open manufacturer-independent way, delivering the vision of Industrie 4.0 today.


I would like to recognize the substantial contributions to the implementation by Renate Franken and Benedikt Krüger.


Industrie 4.0 Chief Architect

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