Hidden tracking champion How to track work station activities via commodity hardware

Transcription

1 5 Hidden tracking champion How to track work station activities via commodity hardware The digital transformation became a key driver for process innovation at shop floor level. Especially, real-time information are the key to setup and run an effective manufacturing control system. Nowadays, especially small and medium-sized enterprises (SME) have a lack of capabilities to track the WIP, control material flow, and work station utilization. The Camouflage research project addresses these issues. Camouflage monitors activities and material flows at work station level by analyzing the worker s activities in real-time. A Camouflage enabled shop floor keeps track of material flows and work in progress. With Camouflage, there is no longer a need for none value adding activities to report the work in progress, material consumption or quality grades. Camouflage is fully integrated with the InFrame Synapse MES. All data being collected by Camouflage are processed and reported by MES, automatically. The talk introduces the technical components and technologies to create a contactless and markerless work center monitoring and the system integration. Furthermore, an overview on various use cases for Camouflage is given. Prof. Dr. Dirk Reichelt Chair of Information Management Faculty computer science / mathematics Hochschule für Technik und Wirtschaft Dresden University of Applied Sciences, Germany Dirk Reichelt earned a diploma and PhD in business informatics from the Technical University of Ilmenau. After his PhD studies he joined Infineon / Qimonda in the production automation department. He has strong background on factory automation systems and factory logistics. Since 2010, he holds the chair of information management at the University of Applied Sciences Dresden. Additionally, Dirk is the head of the smart wireless production research group at the Fraunhofer- IPMS. He and his teams research focus is on cyber physical systems for the digital transformation and production automation. 5-1

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3 Hidden tracking champion How to track work station activities via commodity hardware Prof. Dr. Dirk Reichelt Slide 1 Industrial Internet: The power of 1% Slide 2 5-3

4 The potential of the Internet of Things Source: McKinsey, Unlocking the potential of the Internet of Things, June 2015 Slide 3 What s about SMEs and Industrie 4.0? Reduce Scrap Increased productivity Cost focused High Utilization Optimized material flows Avoid empty tools Compliance Warranty Required Traceability Increasing Flexibility Lot size 1 Increasing number of product options Fulfil requirements of a fully automated fab at SME production costs!!! Slide 4 5-4

5 Tracking at SME As-is Manually tracked Paper based Filled by workers Disadvantages None value adding activities for keeping records No real time information is available Hard to alter once being printed No planning base for scheduling and dispatching Lack of information on shop floor performance Slide 5 Camouflage at a Glance Slide 6 5-5

6 Goals and Challenges Ensure a sustainable competitiveness of manufacturing companies New challenges in the field of future production methods MES integration Goals Gesture recognition Full-stack integration of Camouflage to client-server based Synapse MES Create a low-latency interface for equipment integration Reach strong persistence, at any time of the process Provide lightweight but nevertheless visually intuitive worker instructions Cost-effective method for the automated and non-contact monitoring of manual work processes Track, simplify and speed up the manufacturing process Increase efficiency of the worker Robust detection of actions, picking, material boxes, and sensible areas at the tabletop by inexpensive technology Robust, cost-effective, non-contact real-time analysis Recognition under varying conditions Slide 7 Main Use Cases Supports preparing the Camouflage workplace Setup Tracking Commissions or matches item to be tracked next Supports and controls picking material from correct box Processing Slide 8 5-6

7 Layer concept and MES integration User Interface Web-based Shows live instructions Business Logic Provides persistence layer Controls gesture interaction received via EI Realizes tracking and tracing Equipment Integration (EI) Message-driven connection to Camouflage workplace CAMOUFLAGE Workplace Slide 9 Existing Technology: Examples Source: Source: Non-contact detection! Integration with MES! Robust and cost-efficient! Slide

8 Gesture Recognition and Interaction Actions Material boxes Picking Areas QR code scanner and action cards Unique identification of the boxes by AR tags Real-time detection by RGB image channels Real-time detection by depth channel Change of histograms per box Additional AR tags on tabletop Functional areas for additional interaction During set-up mapping of material and boxes Slide 11 Technologies for Gesture Recognition Microsoft Kinect v2 Intel RealSense Camera F200 Leap Motion RGB + depth data Skeletal data of whole body Detection insensitive to differences in brightness Large tracking space [depth cm] Windows 8, USB 3.0 RGB + depth data Skeletal data of hands Detection sensitive to differences in brightness, no recognition of black subjects Small tracking space [depth cm] Windows 8, USB 3.0 Depth data Skeletal data of hands Detection sensitive to differences in brightness, errors in detection of hands Small tracking space [depth cm] Windows 7, USB 2.0 Slide

9 Results Slide 13 Outlook and visions Adaptation for work places with different spatial dimensions Analysis of picking and assembly times Optimization of the construction process and variation if worker suffers from (cognitive) fatigue Gamification elements as additional motivation, based on picking and assembly times Ergonomic feedback to the worker about repetitive activities with to much load (Key Indicator Method) by use of additional sensors Automated quality control of construction processes (instead of visual assessment by worker at move-out)! Focus on meaningful assistance and feedback. Slide

10 Industrie 4.0 Test Bed at HTW Dresden RTLS S C A D A Festo Cyber-Physical-Factory ABB mutipurpose industrial robots Camouflage workplace Message broker Rule System Big Data Storage facilities and environmental sensors Workers Slide 15 Summary Camouflage demonstrator Automated tracking Paper-less Real-time feedback Non-contact detection of work processes in real time Low distraction of the worker Real-time information Possibility of feedback to worker and guidance for manufacturing Real-time connection to MES Slide

11 Contact Prof. Dr. Dirk Reichelt Tel.: Slide

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