BIBA Competence and Transfer Centre -BIBA labs and LogDynamics Lab

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1 Jannicke Baalsrud Hauge Tel.: BIBA University of Bremen BIBA Competence and Transfer Centre -BIBA labs and LogDynamics Lab

2 AREAS OF APPLICATION Supply chain management Internal production and logistics Life cycle of products 2

3 OVERVIEW-BIBA LABS groundfloor upstairs Serious Gaming Lab upststairs Vision Lab 3

4 RESEARCH ISSUES WITH CPS RELATION- TRANSFER CENTRE Use of CPS data for construction Digitization of interplant material flows (eg. Improving safety in air freight) Autonomous unloading of containers Highly flexible handling technology Digitization of internal material flows Recycling / Refurbishing Business Model Serious Gaming Apprenticeship / Further Training Safe human-robot interaction Quality control by image recognition Automatic control of logistic processes Decentralized testing State-based distribution Preacting maintenance 4

5 SAFE HUMAN-ROBOT INTERACTION 5

6 SEMANTIC TECHNOLOGIES IN THE PRODUCT LIFECYCLE Semantic data integration Integration & interoperability of lifecycle-wide, heterogeneous data sources On-demand integration of product embedded data sources Semantic descriptions and data models of lifecycle data Semantic search and analysis Interoperability with unstructured data Semantic analysis of unstructured data Knowledge-based engineering Knowledge management Big data in the product lifecycle Semantics in design and development Meta-data in the product lifecycle Semantic search, analysis reasoning on design data Automation of knowledge codification Linking product usage data to design data 6

7 Services in the Product Lifecycle Letztes Object Event mit Serienummer M-DL Wareneingang Montageband Warenausgangslager Letztes Object Event mit Lieferscheinnummer Telekom End-User Telekom Service Entsorger Disposal mobil Event Time epclist action bizstep Telekom mobil disposition Seriennummer Lieferscheinnummer Letztes Aggregation Event Time Wareneingang Lager Werkstatt Bauteil EPC ADD Warenanlieferung String Verknüpfung des EPC mit der Lieferscheinnummer Zurück Entsorger Servitization based on item-level lifecycle data Product ideation, design and development Item-level lifecycle assessment Intelligent production, assembly and logistics Product experience in the usage phase Intelligent maintenance Reverse logistics Interaction with intelligent products Human interaction with CPS and safe CPS environments Distributed testing of mechatronic/cps systems Product avatars Integration into social network services Product experience/mtx/meta-products Advanced interfaces to embedded products Wearable computing 7

8 LOGDYNAMICS LAB research in the area of new key technologies for complex processes in production and logistics infrastructure for practical orientated test scenarios combination of the latest technology for individual solutions environment where the progress beyond research to operational reality is designed 8

9 BIBA COMPUTER VISION LAB 3D object recognition & scene understanding Modular software framework Recognition and pose estimation of logistic objects Semantic scene analysis of complex processes Packaging pattern detection Human-machine interface Gesture control for a mobile robot 3D sensor simulation Design of image processing solutions Generation of realistic test data 2D/3D image processing solutions Depalletizing robots [From sensor data to scene understanding] [3D scan of BIBA hall] 9

10 3D OBJECT RECOGNITION / SCENE UNDERSTANDING 3D object recognition of logistic goods Recognition and Localisation of goods for automatic unloading of containers Semantic scene analysis Which objects are in the container? Which object should be unloaded by a robot next? What is the size of the object? Does it contain defects? Comparison with packaging patterns [Deformation analysis] 10

11 2D/3D IMAGE PROCESSING SOLUTIONS Hybrid image processing solutions using 2D and 3D image data Combination of single advantages Geometric properties and distances (3D) Texture analysis (2D) Improved robustness for automation solutions Using machine learning techniques to learn models of object classes [Packaging scenario] [Result scene analysis] 11

12 SIMLAB-PRACTICE DIALOGUE Science (BIBA) The SimLAB provides business and collaborators research-related services: Optimization of existing as well as new production and logistic concepts by means of simulation technology Competence and method transfer SimLAB Practice (Industry) Problemand requirement specification Process and system analysis through modelling techniques (BPMN, GRAMOSA etc.) Evaluation of the use of new technologies and processes by means of simulation (Tecnomatix Plant Simulation, Excel etc.) Advice on funding and research applications for small and medium-sized enterprises (SMEs) Contract research on issues in production and logistics Preparation and participation in national and international research projects 12

13 CONCEPT OF SENSOR-BASED PROCESS SIMULATION IN INDUSTRY 4.0 production target production planning and control production parameters disorder production process in real factory production results LOGISTICALLY Machine assignment, buffer backlog, transportation availability instantly parameter test (experiment) expected production results actual state simulation and forecasting models current production results TECHNICALLY Resource consumption, process stability, component quality Source: Kühn (2006) 13

14 CONCEPT OF AUTOMATIC MODEL GENERATION GRAPHICAL MODELING automatic model generation SIMULATION factory data model module-based material flow simulation Specification of tasks Goals: concept model formal model practicable model result of simulation final documentation Effort reduction compared to manual implementation for model creation, validation, experimental procedure Easier model maintenance, administration and subsequent use by standardized modeling processes Investigation of large solution spaces with several design alternatives in the context of production and technology development 14

15 SERIOUS GAMING 15

16 WHY GAMING Real world become more complex Dynamic interaction Risk sources are manifold (e.g. lack of quality, delays, damages, etc.) How to face increasingly complex production and logistics processes? Increased transparency for early risk identification Qualitatively better data allows counter-measures to be introduced earlier Knowledge about technologies improves their utilization Create a learning environment for risk free experience Baalsrud Hauge and Kalverkamp

17 FEEDBACK Learning analytics Graphics showing: Historical trends, (quantitative) relationship among different measured dimensions/parameters (score, precision/accuracy, time spent in a level, number of level passed, number of wrong attempts, age, sex, estimated competence in some topics, etc.) Supporting learners self-assessment to take decisions and improve learning Supporting teachers assessment to take decisions and improve teaching 17

18 WHO TO CONACT Main contact Jannicke Baalsrud Hauge +49(0) BIBA Competence & Transfer centre Christian Gorldt Vision Lab Hendrik Thamer BIBA Serious Games Lab Logdynamic Lab Marco Lewandowski Intelligent ICT environment Karl Hribernik Simulation Lab Dennis Lappe 18

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