CIRP Annals - Manufacturing Technology 65 (2016) 421–424
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp
Performance modelling and simulation of metal powder bed fusion production system Simon Mounsey *, Bernard Hon (1), Chris Sutcliffe School of Engineering, University of Liverpool, Liverpool, UK
A R T I C L E I N F O
A B S T R A C T
Keywords: Selective laser melting (SLM) Simulation Optimization
New opportunities arise for the deployment of metal powder bed fusion as a scalable batch production method particularly for high value manufacturing in aerospace and medical industries. To avoid costly errors in design of such ‘Factories of the Future’, a discrete event simulation of a concept factory of 22 additive manufacturing machines is constructed. The impacts of part variety, operators and shift patterns on throughput and cost are investigated. Results showed optimum staffing are dependent upon the build duration of parts being manufactured. In all cases, significant improvements in machine utilisation and throughput can be achieved by full analysis of operator levels and shift patterns. ß 2016 CIRP.
1. Introduction Production of parts by additive manufacturing can offer significant advantages in terms of weight saving, part consolidation, and the construction of complex solid or porous variational geometries that cannot be produced by more conventional manufacturing methods such as casting or machining [1]. The use of metal powder bed fusion techniques allows the use of high strength engineering materials such as steel or titanium to create end use components for manufacture direct from CAD data with a shortened process chain. Research on the development of such end use engineering components has been spearheaded in a number of industries, especially medical, aerospace, motorsport, and defence. For example, based on successful research into the construction of porous, bone in-growth, orthopaedic implants, such implants are now routinely produced for use in patients, both animal and human, showing significant benefits to conventional implants [2]. As AM parts gain increasing industrial appeal, their scalable production has emerged as a new demand and opportunity. For an AM production system, careful planning must be undertaken to ensure that a change from very low volume to medium volume will operate optimally. The characteristics of an AM production system differ significantly from a conventional manufacturing cell as the system is totally flexible, i.e., the routing of machines is fully interchangeable and it is essentially a single stage process apart from auxiliary preparations and post-processing. System modelling and simulation are effective techniques for gaining insights of system behaviour in order to increase
* Corresponding author. E-mail address:
[email protected] (S. Mounsey). http://dx.doi.org/10.1016/j.cirp.2016.04.065 0007-8506/ß 2016 CIRP.
production throughput, minimise production cost or reduce energy consumption [3–5]. While modelling and simulation have been used to improve the laser based additive manufacturing process technology [6–8], end to end large batch production by metal powder bed fusion have not been simulated previously. 2. Model development An AM production system model was developed using Simio discrete event simulation software. Simio was chosen due to its flexibility and its object-oriented programming nature that does not require in-depth knowledge of coding in a language such as C++ or Python, allowing for much more rapid construction of detailed simulations. Models within Simio are built by combining objects that represent the physical components of the system [9]. The 3D objects are placed within a ‘Facility’ view to allow construction of a complete animated model with customisable view-states, this allows visual inspection of the simulation as it is running through the use of animated simulation runs. Behaviour of each object is customised through definition of additional process logic which is executed by the objects at discrete points during a simulation run. To reduce the computational power required to complete simulations, each object utilises a shared set of logic processes, but the processes are written to address variables specific to the object that called it. The modelled facility is based on a real-world facility containing 22 Renishaw AM250 machines, to meet projected market demand for large batch size of AM parts. The completed model is designed for large batch production with new jobs being added when both a machine and operator are available on a first-in first-out basis. User specified variables are input to a specification spreadsheet. Types of variables are categorised as materials, machine settings and costs. Materials specify the build material for the part which
S. Mounsey et al. / CIRP Annals - Manufacturing Technology 65 (2016) 421–424
422
includes 316L and 17-4PH steels, AlSi10Mg, Ti6Al4V, CoCr, Inconel 718 and Inconel 625. Machine settings include the size of the overflow, argon bottle and filter size, and the doser setting. Costs cover powder costs, argon costs and energy costs. The combination of these variables determines parameters such as the amount of powder dosed per layer and therefore impacts upon the frequency of auxiliary processes such as overflow changes and silo refilling. These values are then imported into the model as a data table which is referenced initially or dynamically during the simulation. Additional data tables were created and defined within the model and are used to set work schedules for operators and routing sequences for objects. Properties such as the number of machines in operation, batch size, and operator population for each shift are also defined to allow the scheduling of simulation in batches. The use of animated runs, breakpoints in the process logic, and simulation trace functionality tracks the state of all objects and variables. This facilitates full diagnostics into the state of a simulation run at any point in time and simplifies debugging of the model. The duration of each simulation can be specified either in terms of batch size or as a duration of simulated calendar time. An instantaneous image of the complete model of an animated simulation is shown in Fig. 1.
Table 1 Machine specific simulation variables. Variable
Description
Argon_Used
Stores amount of argon used from current bottle Stores total processing time filter has been in use Stores last finished layer in case of machine failure Flag used to determine if the previous build failed Stores current layer number for processing Stores no. of overflow bottles to be sieved post build Stores total mass of powder wiped to current overflow bottle Stores total mass of powder dosed from silo Flag used to record if machine repair is required Flag used to record if machine service is required Used to create visual indication of machine state during animated simulation run Stores builds completed using current wiper blade
Filter Finish_Layer Last_Build_Failed Layer Overflow_Bottles Overflow_M Powder_Laid_M Repair_Required Service_Required Status_Light Wiper
Fig. 1. The AM production facility model.
2.1. Core machine processing Powder bed fusion additive manufacturing processes build complete parts using a laser beam; in the case of the Renishaw AM250 as modelled a 200 W ytterbium fibre laser is used to selectively melt layers of metal powder, under an inert atmosphere of argon gas. CAD data is sliced into layers and sent to the machine. After a layer of powder is placed on the substrate, the laser is fired in raster patterns determined by the layer geometry. When a layer is complete the substrate plate is lowered in the Z-axis and the machine deposits another layer of powder ready to construct the next layer. This process is repeated until the complete part has been made, at which point the substrate, with part attached, can be removed from the machine. The core build logic of the machine in the simulation uses a data table containing the layer number and the time taken to produce each layer. Data are imported from the log file of a completed build on a real machine, to determine the duration of the build in the simulation. Clearly this data could be calculated from a pre build processor; however, for the sake of precision, real log file data is used here. An indexing variable ‘Layer’, specific to each machine in the simulation, tracks the current layer being built as the process loop is executed and ‘Layer’ is incremented on completion of each layer. During the process loop a time delay, of the same duration as the layer build time, is executed to replicate the processing of an individual layer. Additional variables are evaluated during the process in order to determine if auxiliary supporting activities such as changes of filter or overflow bottles are required. A summary of the machine specific variables for the simulation is given in Table 1. A flow diagram representing a simplified version of the main build process logic is shown in Fig. 2.
Fig. 2. Flow diagram of simplified main build process loop.
2.2. Auxiliary processes In order to fully model the end-to-end processing of the AM250 machine, a number of additional logic processes are required. The timings for all manual processes were measured in this investigation. With regard to process variations, a standard deviation is superimposed on these values according to a random normal distribution. Following the transfer of a substrate plate to the machine, there is a setup activity of average duration of 30 min including wiper blade inspection. The build chamber is then purged of oxygen and flooded with argon gas to create an inert atmosphere for oxidation free processing. After that, there is a 45 min heat soak to raise the temperature of the substrate to 170 8C, common across all materials. When the final layer of the build is completed, the machine is subject to a 3 h cooldown before the part cake debuild activity can commence. After that, all unmelted powder from the chamber is removed to the overflow bottles and recycled at one of twelve powder reclaim sieves. The duration of the sieving activity is dependent on the build material and varies between 10 and 15 min per overflow bottle. The simulation allows for two different methods of removing parts from the substrate. The first requires an operator to transfer the substrate to one of two debuild stations where the part is
S. Mounsey et al. / CIRP Annals - Manufacturing Technology 65 (2016) 421–424
423
manually removed. The second method removes the part by wire EDM. During layer processing, the current values of the filter life, overflow level, silo level and argon level are evaluated to determine if any auxiliary activities such as silo filling, filter change and washing, overflow change or argon bottle change, are required. If necessary, the next available operator will be allocated to the machine to complete the activity. Argon bottle levels are also evaluated prior to chamber purge or post build activities. Machine failures occur in the simulation based on a normal distribution with a mean of 1000 processing hours with a standard deviation of 10%. When a failure occurs, the ‘Layer’ index is increased to the value of the final layer of that build. Part debuild and machine cleaning are then performed as normal. A failure process will determine if the failure is a simple build failure or a machine failure based on a probability distribution with 10% chance of machine failure occurring. In the event of a machine failure, 2 weeks will be allowed for machine repair. All machines are subject to biannual servicing, lasting a duration of 1 week. Servicing is staggered a week apart, with a maximum of 4 machines under servicing at any one time. Sieve mesh inspection, cleaning and replacement are carried out on all sieves at intervals of 4, 26 and 52 weeks respectively.
average result was taken. This meant each set of simulations comprised a total of 165 individual runs, taking approximately 30 min for completion. Simio’s ‘experiment’ functionality allows multiple runs to be setup for sequential executions without animation, enabling generation of large data sets very rapidly. For instance, one run simulating 12 weeks calendar time takes between 45 and 120 s depending on selected options. However, multi-threading is employed and allows eight simulations to run concurrently on a modern PC. For each simulation set, the total number of machine operators was varied between 1 and 16 across four different shift patterns. The shift patterns included a standard 37.5 h working week, a two-shift, a three-shift and a four-on four-off shift pattern, i.e. four shifts of 12 h days with 1 h break, 4 days on and 4 days off, providing 24 h coverage 7 days a week. The total number of operators in a simulation was split evenly across the number of shifts available, e.g. on a two-shift pattern the total number of workers in the simulation was always a multiple of 2. The material for all parts were 316L steel and all machine parameters such as doser setting, filter size, overflow size, etc. were kept constant for all simulations.
2.3. Simulation reporting and costing
Fig. 3 shows the effect on throughput due to variations of worker population and shift patterns. The relationship between the unit cost and worker population and shift pattern are shown in Fig. 4. The results show that the part cost reaches its minimum when 8 machine operators are employed on a four-on four-off shift pattern. However, employing 4 additional staff can improve the throughput by 6.3% with a negligible increase in part cost of 0.02%. This improvement occurs due to the frequency at which lengthy debuild and setup activities must be completed, where there is a clear advantage to having operators present at all times in order to decrease the turnaround time on machines that have recently completed a build.
C B ¼ rVC P
(1)
In the event of a failed build, the cost of that build, CF, is calculated as a fraction of the cost of a complete build, as shown in Eq. (2):
Throughput Over 12 Weeks Of Producon - 26 hr Build 1,000
Good Parts Built
For an accurate assessment of machine utilisation and costing of a simulated production run, a number of additional parameters were included in the default Simio report. Customised resource states allow direct reporting of the percentage time that each machine has spent in the production cycle. These are further broken down to detail the time spent performing each process and the time spent waiting for resources, e.g., operator, filters, powder etc. in order to provide a complete reporting of machine utilisation. Costing in the model accounts for the depreciation of the machines, cost of powder used, cost of powder lost, energy usage of all machines, repair costs, servicing costs, staff wages, and consumables. The cost of the powder used to produce the build, CB, is based on the volume of the build, including support structures, the density of the bulk material, and the cost/kg of the powder used, CP, as shown in Eq. (1):
3.1. Batch with 26 h built time
800
Standard Shi
600
Two-Shi
400
ThreeShi
200
CF ¼
LF C B LB
(2)
Four-On Four-Off
0 1
where LF is the cumulative number of layers of the failed build, LB is the total number of layers for a successful build. In addition, it is assumed that some powder is lost during processing, i.e., an estimated 2% is lost to the gas recirculation and filtration system, 2% of the powder in the part cake debuild process and 2% of the powder sieved cannot be recycled.
2
3
4
5 6 7 8 9 10 11 12 13 14 15 16 Total Number Of Machine Operators
Fig. 3. Simulated throughput for 26 h build.
Unit Cost Over 12 Weeks Of Producon - 26 hr Build 3,000
Three separate sets of simulations were run, with each set replicating the first 12 weeks of continuous production of a different part. The three parts, defined by three input log files, require 26, 61 and 108 h of build time respectively. The three parts represent a typical range of parts produced by metal powder bed fusion. In this case, a small medical device takes 26 h build time, followed by parts for the automotive and the aerospace industry taking longer build times. Five replications of each simulation were run to account for variability in the time taken to perform each process, and the
Unit Cost (£)
3. Results and analysis 2,500
Standard Shi
2,000
Two-Shi
1,500
ThreeShi
1,000
Four-On Four-Off
500 1
2
3
4
5 6 7 8 9 10 11 12 13 14 15 16 Total Number Of Machine Operators
Fig. 4. Simulated unit cost for 26 h build.
S. Mounsey et al. / CIRP Annals - Manufacturing Technology 65 (2016) 421–424
424
3.2. Batch with 61 h built time
Unit Cost Over 12 Weeks Of Producon - 108 hr Build 5,000
Good Parts Built
600 500
Standard Shi
400
Two-Shi
300
ThreeShi Four-On Four-Off
100 2
1
3
4
5 6 7 8 9 10 11 12 13 14 15 16 Total Number Of Machine Operators
Fig. 5. Simulated throughput for 61 h build.
Unit Cost Over 12 Weeks Of Producon - 61 hr Build 4,500
Unit Cost (£)
4,000 3,500
Standard Shi
3,000
Two-Shi
2,500
ThreeShi
2,000
Four-On Four-Off
1,500 1,000 1
2
3
4
Standard Shi
4,000 Two-Shi 3,500 ThreeShi
3,000 2,500
Four-On Four-Off
2,000 1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Total Number Of Machine Operators Fig. 8. Simulated unit cost for 108 h build.
employing just 4 operators, and that the throughput is only increased by 7.8% when increasing operators to a total of 8, a much smaller improvement than on shorter builds.
Throughput Over 12 Weeks Of Producon - 61 hr Build
200
4,500 Unit Cost (£)
Fig. 5 shows how the system throughput varies with the worker population and shift pattern. Fig. 6 shows the effect of worker population and shift pattern on the unit cost. Again, the results show that the minimum part cost is achieved through employing 8 machine operators on a four-on four-off shift pattern. The increase from 4 operators to 8 results in an improvement in throughput of 40% while reducing part cost by 13%. On a longer build such as this, it shows that there is little benefit in terms of throughput from additional staff above 8. This is because the amount of debuild and setup activities is reduced due to the increased build time, increasing operators idle time while machines are building parts. The results also show that there is little difference in unit cost between operating the system on a two-shift or three-shift pattern.
4. Conclusions While AM production systems may differ due to capacity and layout among other variables, this simulation study demonstrates clearly the benefits to system performance improvement via the generic approach based on detailed modelling. As metal powder bed fusion AM processes mature, the scenario of large batch production characterised in this investigation will become more common place. This study of a real AM production system therefore provides valuable insight into how large batch production by AM may be operated. The results have shown that throughput on short builds can be increased by up to 550% while simultaneously providing a reduction in part cost of 72% by considering the staffing level and the shift patterns. Although all results show a clear benefit to using a four-on four-off shift pattern, there may be circumstances where this may not be possible. In such situations, the optimum staff level and shift pattern should be determined by simulation. Acknowledgements This work was supported in part by funding from Innovate UK and Renishaw plc on the HiEND programme, and EPSRC Centre for Laser Based Production Processes grant EP/K030884. The authors would also like to thank Renishaw for ongoing support of the project, and Simio LLC for technical software support.
5 6 7 8 9 10 11 12 13 14 15 16 Total Number Of Machine Operators
Fig. 6. Simulated unit cost for 61 h build.
3.3. Batch with 108 h built time References For the aerospace parts with the longest build time, the effect of worker population and shift pattern on throughput is shown in Fig. 7. Fig. 8 shows how the unit cost varies with the worker population and shift pattern. Unlike the two shorter builds, the results of the 108 h build show that the minimum part cost can actually be achieved through Throughput Over 12 Weeks Of Producon - 108 hr Build 350
Good Parts Built
300
Standard Shi
250 Two-Shi 200 ThreeShi
150 100
Four-On Four-Off
50 1
2
3
4
5 6 7 8 9 10 11 12 13 14 15 16 Total Number Of Machine Operators
Fig. 7. Simulated throughput for 108 h build.
[1] Zaeh MF, Ott M (2011) Investigations on Heat Regulation of Additive Manufacturing Processes for Metal Structures. CIRP Annals 60(1):259–262. [2] Mullen L, Stamp RC, Brooks WK, Jones E, Sutcliffe CJ (2008) Selective Laser Melting: A Regular Unit Cell Approach for the Manufacture of Porous, Titanium, Bone In-growth Constructs, Suitable for Orthopaedic Applications. Journal of Biomedical Materials Research Part B: Applied Biomaterials 89(2):325–334. [3] Diaz N, Jondral A, Greinacher S, Dornfield D, Lanza G (2013) Assessment of Lean and Green Strategies by Simulation of Manufacturing Systems in Discrete Production Environments. CIRP Annals 62(1):475–478. [4] Brinksmeier E, Aurich JC, et al (2006) Advances in Modeling and Simulation of Grinding Processes. CIRP Annals 55(2):667–696. [5] Herrmann C, Thiede S, Kara S, Hesselbach J (2011) Energy Oriented Simulation of Manufacturing Systems – Concept and Application. CIRP Annals 60(1):45–48. [6] Hu D, Kovacevic R (2003) Sensing, Modelling and Control for Laser-based Additive Manufacturing. International Journal of Machine Tools and Manufacture 43(1):51–60. [7] Khairallah SA, Anderson A (2014) Mesoscopic Simulation Model of Selective Laser Melting of Stainless Steel Powder. Journal of Materials Processing Technology 214(11):2627–2636. [8] Li Y, Gu D (2014) Thermal Behavior During Selective Laser Melting of Commercially Pure Titanium Powder: Numerical Simulation and Experimental Study. Additive Manufacturing 1–4(1):99–109. [9] Pegden CD, Sturrock DT (2013) Rapid Modeling Solutions: Introduction to Simulation and Simio, Simio LLC.
