Munich, October 27 th, 2016

Transcription

1 Novel applications in assessing manufacturing and assembly of complex composite structures A pace towards Industry 4.0 in composite manufacturing 11th AIRTEC Congress Munich, October 27 th, 2016 Ali Al-Lami, MSc. Dr.-Ing. Philipp Hilmer Department of Composite Technology Institute of Composite Structures and Adaptive Systems

2 DLR.de Chart 2 11th AIRTEC Congress Ali.Al-Lami@dlr.de Table of contents Introduction Motivation Assessment models Eco-Efficiency Assessment Model (EEAM) Conventional assessment and data collection applications Concept of Smart Work Station (SWS) Functionality and results of SWS Benefits of SWS Outlook Discussion

3 DLR.de Chart 3 11th AIRTEC Congress Ali.Al-Lami@dlr.de Introduction Institute of Composite Structures and Adaptive Systems Prof. Dr.-Ing. M. Wiedemann Our Professional Competences Bricks of the Process Chain of High Performance Lightweight Structures Department of Composite Technology Dr. -Ing. M. Kleineberg Tailored manufacturing concepts Process Assessment Dr. -Ing. P. Hilmer, A. Al-Lami Process development We align our research along the entire process chain for building adaptable, tolerant, efficiently manufactured light weight structures. For excellent results in the basic research and industrial application. From the idea via processes to prototypes New technologies for manufacturing Hybrid manufacturing Assembly Repair Process automation Process analysis Process assessment

4 DLR.de Chart 4 11th AIRTEC Congress Ali.Al-Lami@dlr.de Motivation benefit assessment NOx Rejects % Idea Realization Benefits Design Manufacturing Assembly Operation End-of-Life Simplified product life-cycle

5 DLR.de Chart 5 11th AIRTEC Congress Ali.Al-Lami@dlr.de Motivation Industry 4.0 Wind Energy The term Industry 4.0 describes the integration of product development, production, logistics and customers within intelligent networks. Materials and precursor products, production machines and consumer products will connect with the digital network and be modelled in a virtual world. Automotive Aeronautic Production processes, logistics, sales, service and entire business models can be simulated on a computer to evaluate and assess their viability. Source: DLR, Industry 4.0, Industry of the future

6 DLR.de Chart 6 11th AIRTEC Congress Ali.Al-Lami@dlr.de Assessment models Technical cost analysis Decision Business case estimation Criterions - Data quality Bottom-up Top-down Eco-Efficiency Assessment Model - Single technology evaluation - Data collection effort Bottom-up Top-down - Recurring Cost (RC) - Non-Recurring Cost (NRC) Decision support tools Data Experiences

7 DLR.de Chart 7 11th AIRTEC Congress Ali.Al-Lami@dlr.de Eco-Efficiency Assessment Model (EEAM) description Designed and developed by DLR - Based on the Life Cycle Assessment (LCA): ISO 14040: Gate-to-Gate: Manufacturing and Assembly - Process modeling: Business Process Modeling and Notation (BPMN): ISO/IEC Combined framework of LCA and BPMN Decision support tools Life Cycle Life Cycle Assessment Cost Analysis (LCA) (LCCA)

8 DLR.de Chart 8 11th AIRTEC Congress Ali.Al-Lami@dlr.de Eco-Efficiency Assessment Model (EEAM) framework Inventory analysis Modeling Goal and scope definition EEAM Framework Benefit assessment

9 DLR.de Chart 9 11th AIRTEC Congress Ali.Al-Lami@dlr.de Conventional data collection Data collection tools Inventory analysis Conventional data collection - Dedicated data collector - Dependent data quality - Time consuming data collection - Offline data mining - Offline assessment - Automation is required for digitalization Material flow cost accounting Examples Value Stream Mapping (VSM) Life Cycle Inventory Analysis (LCIA)

10 DLR.de Chart 10 11th AIRTEC Congress Concept of Smart Work Station (SWS) principles Principles System boundary Matter Energy Nothing comes from nothing Law of mass conservation Lucretius c. 99 BC c. 55 BC Lavoisier 1774 Lomonosov 1756 Source: Bloomsbury Mass energy equivalence Einstein's 1905 Source: Springer Source: Springer

11 DLR.de Chart 11 11th AIRTEC Congress Concept of Smart Work Station (SWS) principles implementation Smart Work Station (SWS) Manufacturing/ Assembly (M&A) Unit Process (UP) 1 Unit Process (UP) n Product Adaptation - System: - System boundaries: UP System boundary Matter Energy - Physical: Work station - Time: - Technical: UP duration UP Inputs Benefits

12 DLR.de Chart 12 11th AIRTEC Congress Smart Work Station (SWS) in manufacturing and assembly of composites description Physical system boundary: Work station Mass Power Matter Energy Fiber Matrix Supplies Electricity Equipment Labor Time Inputs Electricity Fiber Matrix Supplies Equipment Labor Unit process (es) Product Carbon footprint Waste Cost impact RC Outputs Time boundary time window t

13 DLR.de Chart 13 11th AIRTEC Congress Functionality of Smart Work Station (SWS) matter: materials Matter Fiber Matrix Supplies Mass Source: HBM Digital scale system - Fiber holder integrated scale system (Cutter) - Mold integrated scale system (preforming, bagging,..) - Scale-integrated matrix vessels - Dedicated mini-platform scale for each supply material - Or/ universal work mini-platform scale for all supplies with barcode detector to identify the supplies

14 DLR.de Chart 14 11th AIRTEC Congress Functionality of Smart Work Station (SWS) energy: electricity & equipment Energy Electricity Equipment Labor power Digital electric meter system - Defined electricity source of each work station - Defined electricity source of each equipment - Dedicated electric meter for each equipment

15 DLR.de Chart 15 11th AIRTEC Congress Functionality of Smart Work Station (SWS) energy: labor work Energy Electricity Equipment Labor Time Cells oriented work station Digital work time detection - Cells oriented work station to detect the work efforts (in case of more than one worker) - Video camera with motion detection functionality for each cell - Infra-red camera with thermal detection (allowed by German works council) - Labor work is measured as simplified input (either yes or no: no effort measurement)

16 DLR.de Chart 16 11th AIRTEC Congress Smart Work Station (SWS) example Manufacturing of fiber reinforced polymers (FRP) Example of CFRP Cutting Preforming Bagging Infusion Tempering Demolding UPs: Technical system boundary Physical system boundary Matter Energy Physical work stations Mass Power Time Time boundary time window Cutting Preforming Bagging Infusion Tempering Demolding t

17 cumulative DLR.de Chart 17 11th AIRTEC Congress Results of Smart Work Station (SWS) example of materials data mass: g Matter 250 Fiber 200 Fiber Matrix Vacuum Bag Supplies Mass 150 Tacky-Tape Adhesive tape 100 Breather Cleaning tissue Copper pipe Example 50 Vacuum film Cotton gloves Preforming Bagging time: min these data are estimated and not collected

18 cumulative DLR.de Chart 18 11th AIRTEC Congress Results of Smart Work Station (SWS) example of electricity & equipment data power: kw Energy 1,4 Electricity Equipment Labor power 1,2 1 0,8 0,6 Hot air blower Vacuum pump Preform System 0,4 Example 0,2 Preforming Bagging time: min these data are estimated and not collected

19 cumulative DLR.de Chart 19 11th AIRTEC Congress Results of Smart Work Station (SWS) example of labor work data Energy Electricity Equipment Labor Time Work time: min Labor work 10 Example Preforming Bagging time: min these data are estimated and not collected

20 DLR.de Chart 20 11th AIRTEC Congress Results of Smart Work Station (SWS) example of combined inputs Fiber Vacuum Bag Mass Matter Power Energy Example Tacky-Tape Adhesive tape Breather Fiber Matrix Supplies Electricity Equipment Labor Preforming Bagging Cleaning tissue Copper pipe Vacuum film Cotton gloves Hot air blower Vacuum pump Preform System Labor work Time time: min

21 DLR.de Chart 21 11th AIRTEC Congress Benefits of Smart Work Station (SWS) benefit assessment & Industry 4.0 Industry 4.0 SWS data collection - No data collector is required - Independent & repeatable data quality - 0 time consuming data collection (automated) - Online data mining - Online assessment - Process automation is not a prerequisite - Universal: process associated time: min Fiber Vacuum Bag Tacky-Tape Adhesive tape Breather Cleaning tissue Copper pipe Labor work Vacuum film Cotton gloves Hot air blower Vacuum pump Preform System

22 DLR.de Chart 22 11th AIRTEC Congress Outlook next steps in SWS Realization - Construction - Development of digital interfaces - Development of data collection software - Validation intern - Validation with partners Enhancement - Size extention: scale limitation - Connection of work stations - Developement of accurate camera detection - Data dual-use: product quality assurance (as designed vs. as manufactured)

23 DLR.de Chart 23 11th AIRTEC Congress Outlook next steps in EEAM Data SWS & EEAM - Correlation of SWS & EEAM - Online benefit assessment - Parameterization - Independent process definition based on parameterization Eco-Efficiency Estimation Model (EEEM) - Based on EEAM & parameterization - Combining bottom-up (RC) with top-down (NRC) - Benefit estimation in early design phase - Design-to-Cost (DTC) - Top-down based on bottom-up - Design for Manufacturing and Assembly (DFMA) UP x UP y

24 DLR.de Chart 24 11th AIRTEC Congress Discussion Thank you for your attention! For any further questions please don t hesitate to contact me: Deutsches Zentrum für Luft- und Raumfahrt e.v. (DLR) German Aerospace Center Institute of Composite Structures and Adaptive Systems FA-FVT Lilienthalplatz Braunschweig Germany Ali Al-Lami, MSc. Tel ali.al-lami@dlr.de