Data Based Model Generation Gas Turbines and Turbo Compressors

Transcription

1 Data Based Model Generation Gas Turbines and Turbo Compressors Pavel Gocev and Andreas Sowinski Page 1 DIFFERENT PRODUCTS -SIMILAR CONFIGURATIONS Different Value Streams channeled in one Modeling and Simulation Workflow GAS TURBINES TURBO COMPRESSORS BLADES & VANES DISCS WHEELS ROTOR STATOR/ CASING ASSEMBLY IMPELLER ROTOR STATOR/ CASING ASSEMBLY PACKAGE/ TESTING LOAD SIMBOM ORDERS/JOBS ROUTINGS FACTORY/ SIMULATION RESULTS KPI PROCESSES Page 2

2 Tecnomatix Plant Simulation Worldwide User Conference 2015 DRIVERS AND CHALLENGES Reduction of Modeling Effort through an Unified Approach DRIVERS AS MOTIVATION CHALLENGES TO RESPOND ON Multiple sites, areas and production lines Many models and modules developed by several simulation experts Modeling of dynamic and complex behaviors Dispersed and different structures of available data and information Different logic descriptions DEFINE Reduction of modeling effort and shortening of the modeling time Reusable modules and model structures Universal solution for different parties Upgradable and adaptable model Improved communication through standardization Page 3 ONE MODELING AND SIMULATION ARCHITECTURE Simulation Model is Connected to the Factory Data Base Simulation Model Plant Simulation Input Data Base Results KPI Cockpit Umlaufbestand Load Routings Bill of Materials Processes Resources Shift Plan Failures MTBF, MTTR Availabilities Work Orders and Routings Generator Resources and Layout Generator WIP-Interface Alternative Resources Alternative Technology Chains Context-sensitive selection of alternatives Dynamic decision of batch-building Event-driven process execution Lead Time Yield On Time Delivery (OTD) Utilization Work In Progress (WIP) Fill Rate Queue Time Wait to batch Wait to match Page 4

3 SIMULATION AS A RESPONSE TO OPERATIONAL CHALLENGES Simulation as a Central Element of Digital Factory is Already in Implementation DATA AND KNOWLEDGE PLANT SIMULATION RESULTS CHALLENGES IMPLEMENTATIONS BENEFITS Page 5 Unsteady Load Make or Buy Decision Layout Constraints Variable Process Times Utilization vs. Lead Time Crossing Material Flow Variety of Scenarios GT Rotor GT Casing GT B&V TCS Casing TCS Rotor GT Combustion DIGITAL FACTORY SIMULATION COMMUNITY (DFSC) IN FOUNDATION Close-to-Reality Stochastic Driven Time-based KPI s Rapid Scenario Validation Statements on Time Periods Adjustable Detail Level Arbitrary Extendable FROM DATA AND INFORMATION TO SIMULATION MODEL SALES LOAD ORDERS PARAMETERS END-PRODUCTS (GT OR TCS) LOAD-LIST PLANT SIMULATION SHOP-FLOOR WORK-IN-PROGRESS (WIP) SIMBOM COMPONENT PARAMTERES DUE DATES ORDERS/JOBS COMPONENT OPERATION RESOURCES PROCESS TIME RULES INTERDEPENDENCIES DISPOSITION CONTROLS PRIORITIZATION REWORK OUTSOURCING BATCHING SIM-RELEVANT COMPONENTS Page 6 META-ROUTING PROCESS COMPONENTS RESOURCES PARAMTERS RESOURCES WORK CENTERS SHIFT MODEL LOCATION FAILUERS

4 RESOURCES AND LAYOUT Automated generation of resources and layout is based data in one simple table Layout Table Visualization of real layout Deep Dive: Machine Area Machine-Area A Simple table to describe the resources (=Machine Area) x-y-coordinates x-y-size Rotation angle Color Page 7 Layout of Machine Areas (red rectangles) is created based on the x-y-coordinates automatically (SimTalk-Method) No direct connectors between areas as there are no simple flows in a job-shop Machine Area = [Frame] Within each Machine Area each resource is represented by a Queue for incoming goods (Qin) the Resource itself (Res) and a Queue for outgoing goods (Qout) Qin / Qout = [PlaceBuffer] Res = [SingleProc] WORK ORDERS AND ROUTINGS Each job is moved from Machine Area to the next Machine Area according to the particular Routing Processing Steps Jobs / Parts SUBTABLE Contains information regarding Main Processing Resource Possible alternative resources if main resource is busy Times (Setup and Processing) Batching rules if necessary Page 8

5 CONTROL LOGIC Basic model is extended by a number of modules to improve the accuracy of the simulation results BASIC-MODEL Machine-Area A Machine-Area B TRANSPORT ALTERNATIVE RESOURCES ASSEMBLY BATCHING + Machine Area A A x y Work Order?? Res A Res B Distance = x + y B Machine Area B? Res C Workstation Page 9 PRIORITIZATION AND DISPATCHING Two levels of prioritization of jobs within the model Macro - Dispatching Micro Micro Micro Station A Station B Station C Production System Jobs waiting to be dispatched into the system (e.g. via ConWIP-Logic) Jobs waiting in front of a resource to be processed (e.g. FIFO, Due Date, Slack Quotient) Page 10

6 LOAD Three types of possible load can be simulated Load Today = Start of Simulation Work-In-Process Projects that are currently being processed WIP does not necessarily start with Step 1 WIP Upcoming Arbitrary Upcoming Orders New orders according to sales forecast Processing starts always with Step 1 Arbitrary Load Projects that are not based on real orders Used for scenario-modelling Page 11 Time ANALYSIS OF SIMULATION RESULTS Timestamps guarantee that the processing of every single part can be traced TBin TQ1in TPin TQ2in TTin TTout Batching Queue 1 Processing Queue 2 Transport Time spent within Machine Area *Quality Control / Rework / Scrap neglected Page 12

7 SIMULATION FOR CONTINUOUS IMPROVEMENT Scenario-based Experiments Validate the Actions to be Taken PLANT SIMULATION RULES RESULTS INTERDEPENDENCIES DISPOSITION CONTROLS PRIORITIZATION REWORK OUTSOURCING BATCHING EXPERIMENTS EVALUATION ACTIONS JOB-SCHEDULE SCENARIOS Page 13 SUMMARY AND NEXT STEPS SUMMARY NEXT STEPS Modeling and Simulation Architecture for complex and dynamic value streams Lean and easy model generation through standardized structures Scalable from macro to micro level Extendable and adaptable for arbitrary products, processes and resource Further standardization and harmonization of the structures Module for flexible scenarios that consider personnel qualifications Module for integration of timing behaviors of different resources Detailed monitoring and reporting of the resource status Integration of costs and energy aspects Page 14

8 BE INVENTIVE USE PLANT SIMULATION Pavel Gocev PG GT LGT LT BLN Huttenstr Berlin Phone: Mobile: Andreas Sowinski PG CP TCS DBG MF Wolfgang-Reuter-Platz Duisburg Phone: andreas.sowinski@siemens.com siemens.com Page 15