Tecnomatix Plant Simulation in the context of research and development

Transcription

1 Tecnomatix Plant Simulation in the context of research and development B. Denkena, S. Wilmsmeier Room: Estrel Hall B Berlin, 10/24/2017, 14:30 15:30

2 Content 1. The IFW 2. Research with Tecnomatix Plant Simulation An overview 3. Practical example 1 Plant Simulation as part of the digital factory 4. Practical example 2 Employee competency based simulation 5. Summary Seite 2 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

3 Content 1. The IFW 2. Research with Tecnomatix Plant Simulation An overview 3. Practical example 1 Plant Simulation as part of the digital factory 4. Practical example 2 Employee competency based simulation 5. Summary Seite 3 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

4 Production Engineering at the Leibniz Universität Hannover Photo: sliwonik.com Associated Centers Laser Zentrum Hannover Institut für Integrierte Produktion Hannover Leibniz Research Center Energy 2050 Seite 4 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

5 Infrastructure of the Center for Production Technology Employees (PZH wide / IFW only) Researchers: ca. 260 / 88 Technicians and administrations: ca. 100 / 20 Student assistants: ca. 500 / 211 Students: ca. 800 Machines and equipment High-quality machine tools and installations Latest measuring equipment, SEM, laboratories Cleanroom (350 m 2, class 100) Building Approx. 22,000 m 2 effective surface for office buildings, proving grounds, lecture and seminar rooms, library, cafeteria etc. Seite 5 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

6 Institute of Production Engineering and Machine Tools Manufacturing processes (Dr.-Ing. Thilo Grove) Grinding technology Cutting Tailored surfaces Machines and controls (Benjamin Bergmann) Machine components Machines and Monitoring Production systems (Dr.-Ing. Marc-André Dittrich) Production planning and control Process planning and simulation Seite 6 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

7 Department production systems Process planning and simulation CAD / CAM process chain NC programming NC optimization Development of CAM modules Virtual control Analysis of cutting conditions Visualization of machine kinematics 5-axis simultaneous machining Production planning and control Integrated work planning and production control Process chain optimization Availability and maintenance Skill-oriented planning Technological simulation of the process chain Interface solutions for coupled simulations Sustainable production Work planning and production control Seite 7 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

8 Content 1. The IFW 2. Research with Tecnomatix Plant Simulation An overview 3. Practical example 1 Plant Simulation as part of the digital factory 4. Practical example 2 Employee competency based simulation 5. Summary Seite 8 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

9 Selection of projects with Tecnomatix Plant Simulation Planning Control Employees Modelling Checklist Development of a method for integrated production and maintenance planning Exploration of interdisciplinary planning approaches Development of algorithms for optimal production control by means of simulation Exploring measures of sequencing and pooling Development of a method for simulation-based cost-benefit analysis of training measures Exploration of further training potentials Development of a method for the fully automated adaptation of simulation models by means of machine data Exploration of adaptive simulation models Trainings Seite 9 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

10 Content 1. The IFW 2. Research with Tecnomatix Plant Simulation An overview 3. Practical example 1 Plant Simulation as part of the digital factory 4. Practical example 2 Employee competency based simulation 5. Summary Seite 10 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

11 Practical example 1 Planning Control Employees Modelling Checklist Development of a method for integrated production and maintenance planning Exploration of interdisciplinary planning approaches Development of algorithms for optimal production control by means of simulation Exploring measures of sequencing and pooling Development of a method for simulation-based cost-benefit analysis of training measures Exploration of further training potentials Development of a method for the fully automated adaptation of simulation models by means of machine data Exploration of adaptive simulation models Seite 11 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

12 Problems in production and maintenance planning Separate consideration of production and maintenance leads to inefficient use of resources Digitization offers the possibility to plan maintenance measures at an early stage Impact of individual maintenance measures on production difficult to quantify Static methods for production and maintenance planning do not show the interactions? Seite 12 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

13 Solution Integrated production & maintenance planning Process step PS k Buffer P l Reserved capacity for production order A i Setup changeover Production-free period (e. g. weekend) PS 1 M 1 A 1 A 2 PS 1 M 2 A 3 A 4 PS 2 Starting point of maintenance measures t Sj Maintenance time t j Machine M p A 1, A 2 A 3, A 4 Machine assignment for production order A i PS 2 M 3 M 4 A 1 A 1 A 2 A 2 1 t 1 t S1 MA 1 Maintenance planning (variable) Starting point of maintenance measures t Sj 1 M 5 A 3 A 4 Maintenance measure MA j Constant parameters Batch sizes Sequences of production orders Machine assignments Planning period T Time t Krö/68786 IFW Seite 13 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

14 Solution Integrated production & maintenance planning m simulation experiments, n views per experiment P 1 M 1 M 2 M 3 A 1 A 2 t S1,1 t S1,2 Time interval t Observation time t B Planning period T t S1,m MA 1 Time t P 2 P 3 M 3 M 4 M 5 P 4 Krö/68788 IFW Seite 14 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

15 Decision algorithm Choose order variant RF Choose lot size LG Choose machine utilisation variant B Choose maintenance start time t Sj,z RF = RF+1 no LG = LG+1 no B = B+1 no z = z+1 no Order variants processed? yes RF = 1 yes LG = 1 Lot size vatriants processed? yes Machine utilisation variants processed? yes B = 1 t Sj,z = 1 Maintenance start times processed? Reduction of experimental plan eliminate invalid planning scenarios eliminate not sufficient planning scenarios eliminate scenarios by company specific knowhow Combine RF, LG, B and t Sj,z Simulate scenarios Save planning scenario p p = p+1 RF: Order variants LG: Lot sizes B: Machine utilisation variants Seite 15 PLM Europe User Conference 10/24/2017 S. Wilmsmeier z: Maintenance start time p: possible planning scenarios Wil/91104 IFW

16 Dynamic Maintenance costs Direct maintenance costs Indirect Maintenance costs Simulation results first research phase Observation period t B A 1 A 2 A 3 A 4 Time t [ *] *Values have been removed due to confidentiality I II III IV Direct Maintenance costs Dynamic Maintenance costs ΔK Weekend Indirect Maintenance costs [h] 120 Starting point of maintenance measure t sj Legend Production-free period (weekend) Setup changeover Maintenance measure I. Constant Maintenance time t j : 12 h Observation period t B : 120 h Time interval t : 2 h Confidence interval : 90 % II. Variable Starting point of maintenance measure t sj Krö/68791 IFW Seite 16 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

17 Summary first research phase Dynamic input Simulation model Static input Order variant Lot size Machine utilisation variant Maintenance start time Layout related information Machine behaviour Dynamic planning method Output Optimal production and maintenance plan Reduction of unit costs by up to 7 % Continious manual adaption needed for application during operation time Further need for research Development of a method for the automated adaptation of simulation models by means of machine data Seite 17 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

18 Simulation model adaption with machine data acquisition Model validity Self-parameterizing and learning simulation model Target validity Traditional simulation model Model creation Traditional simulation models Model usage Adaption Time Decreasing accuracy of the simulation models in use with increasing deployment time due to changed plan data base Adaptation of the simulation models time-, personnel- and cost-intensive Self-parameterizing and learning simulation models Plan data base is updated at the beginning of the simulation Data base improves with increasing deployment time (edited historical data) Manual adaptation only necessary for structural changes Seite 18 PLM Europe User Conference 10/24/2017 S. Wilmsmeier Wil/86787 IFW

19 Integration of machine data in material flow simulation Adaptive simulation model Evaluation algorithms Web Applikation Create XML files SQL query time related data SQL time period related data Protocols (selection) FTP, TCP Interfaces (selection) OPC-DA/UA, DDE, MCIS, DNC, FOCAS 1&2, NIO Interface, Open Core Interface, Modbus MDE/BDE Seite 19 PLM Europe User Conference 10/24/2017 S. Wilmsmeier MDE/BDE MDE/BDE MDE/BDE MDE/BDE Wil/86761 IFW

20 Duration Distance Frequency ok reworking waste Data preparation and automated model adaption Stored data Process data Transfer of results Calculate percentage distribution Machine 1 95 % 2 % 3 % Machine 1 Failure Failure duration distance Tests of goodness of fit DataFit Machine 1 Data logger Simulation model Wil/86798 IFW Seite 20 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

21 Adaptive and integrated maintenance and production planning Ausfallfolgekosten Failure Ausfallfolgekosten follow-up costs Frequency Web-based acquisition and visualisation Visualisation of results 8000 [ ] Wochenende Weekend Machine Maschine 3 Machine Maschine 1 Machine Maschine Startzeitpunkt 18 Time 26 of 34maintenance 42Instandhaltungsmaßnahme measure [h] Startzeitpunkt IH-Maßnahme Data acquisition and selection 4 Simulation-based decision support Simulation model Histogram short-term failure distance Data preparation Failure 25 30distance Production planning 1 Maintenance planning Real production system Legend 1 Plan data, real data from MDC and PDC systems 2 Selected real data 3 Prepared database (e.g. cycle and setup time, machine failure behaviour) 4 5 Simulation results (e.g. failure follow-up costs) Decision for real system Wn/72107 IFW Seite 21 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

22 Summary second research phase Dynamic Input Order variant Lot size Machine utilisation variant Maintenance start time Machine behaviour & system status Dynamic planning method Simulation model Output Optimal production and maintenance plan Valid simulation model without cost and time consuming manual efforts Shortening of the settling phase due to initialized buffers Static Input Layout related information Temporarily manual adaption needed if layout changes Requested research project Development of a method for the (fully) automated creation of simulation models based on layout scans Seite 23 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

23 Content 1. The IFW 2. Research with Tecnomatix Plant Simulation An overview 3. Practical example 1 Plant Simulation as part of the digital factory 4. Practical example 2 Employee competency based simulation 5. Summary Seite 24 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

24 Practical example 1 Planning Control Employees Modelling Checklist Development of a method for integrated production and maintenance planning Exploration of interdisciplinary planning approaches Development of algorithms for optimal production control by means of simulation Exploring measures of sequencing and pooling Development of a method for simulation-based cost-benefit analysis of training measures Exploration of further training potentials Development of a method for the fully automated adaptation of simulation models by means of machine data Exploration of adaptive simulation models Seite 25 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

25 Initial considerations for the SAPA project Stable connection between the competence development of employees and innovative ability of companies Benefit of trainings in companies can only be estimated Mathematical models allow a quantitative description of the entire workforce performance of a company Individual employees or their characteristics and abilities are not explicitly taken into account Initial hypothysis If companies are able to flexibly vary key decision-making variables, companies can then develop their business and situation-specific optimal training strategy. Seite 26 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

26 Competence-based simulation System behavior of a production system is often too complex for it to be fully captured and evaluated Material flow simulations represent the system behavior of a production in a model (using appropriate software) Employee competencies are elements of a production system and can be represented in the material flow simulation If employee competencies are represented, a change in competencies (eg. by trainings) leads to a change in the production system Thus planning processes can be supported or optimized, by using simulation approaches Seite 27 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

27 Example: Material flow simulation Seite 28 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

28 Quantity of products Example: Production program 14 Product Product 2 Product Simulation time Seite 29 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

29 Quantity of products Example: Reaction of the production system Product Output Simulation time Seite 30 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

30 Quantity of products Example: Reaction of the production system Stock Simulation time Seite 31 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

31 Quantity of products Example: Further training of employee Output Product 1 8 Stock Simulation time Seite 32 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

32 Quantity of products Example: Further training of employee Stock Output Product 1 Simulation time Seite 33 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

33 Content 1. The IFW 2. Research with Tecnomatix Plant Simulation An overview 3. Practical example 1 Plant Simulation as part of the digital factory 4. Practical example 2 Employee competency based simulation 5. Summary Seite 34 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

34 Summary The IFW uses Tecnomatix Plant Simulation for research and improvement of production systems Production planning and control, the employees and simulation techniques can be investigated with Plant Simulation Digitization allows the integration of Tecnomatix Plant Simulation as part of the Digital Factory into the daily planning processes Practical implementations of developed methods show the reproducibility of simulated results Planning Control Employee Technique Checklist Tecnomatix Plant Simulation Seite 35 PLM Europe User Conference 10/24/2017 S. Wilmsmeier

35 Seite 36 PLM Europe User Conference 10/24/2017 S. Wilmsmeier Thank you for your attention!