Posted: June 30th, 2022

ENG3104 Engineering Simulations And Computations

Question:

Simulating a discrete event

After a warm-up period, create the model and optimize the revenue for your plant in ten days.

Description

The 20-m conveyor transports the gears from a separate plant to an assembly area. It follows a distribution chart in the Excel file.

This conveyor feeds an assembly zone where the gearboxes can be assembled using two gears and two bearings (always in stock).

The conveyor is a belt-type conveyor. The gears can be spaced 100 mm apart on the conveyor and have an 80x80x100 mm footprint.

Maximum capacity is 180 parts, speed 5 m/min

The gearboxes are made from castings. They are then loaded onto a CNC machine for further boring and machining.

Each casting is loaded to the workstation by an operator in 0.5 to 1.0 minutes. Then, they are unloaded to a bin with a maximum capacity of 50 or to a sampling station.

The unloading process takes between 0.5 and 0.9 minutes, while the machining cycle takes about 2.3 minutes.

Each 20th machined gearbox is sent to an inspection station and the rest to a buffer.

The inspection cycle time is triangularly divided with a minimum of 0.8 minutes and a maximum of 2.25 minutes. There’s also a mode of 1.1 minute.

The operator(s) performs this inspection.

Tests have shown that there is a 78% chance of the critical dimensions (bores for bearings) being at the upper tolerance limit after inspecting 20 samples.

This means that the CNC workstation tools must be replaced and the machine should be cleaned.

The operation time is divided in three parts, with a minimum of 2.5 minutes, maximum of 3.7 minutes and a maximum of 3.2.

The gearbox that failed is removed.

Two gearboxes made from machined gear are assembled by an operator with two bearings and two gears.

The average time is 3 minutes. There is a standard deviation of 0.7%. However, it should not take less than 2.2 minutes or more than 5.

For testing, the assemblies are placed in a buffer.

The gearboxes are tested on the bench for 10 minutes. It can take up to five units at a given time.

The operator loads the gearboxes and unloads them at the test points.

Each gearbox takes between 0.2 and 0.4 minutes to load and unload.

The gearbox will be sent to the disassembly station with a 93% chance of being shipped. Failures are removed from the conveyor and replaced on the 5 m distance.

The bearings and castings of the gearbox are thrown away.

The average time for disassembly is 2.3 minutes, with a standard deviation of 0.4. However, it should not take less than 1.7 minutes and not longer than 3.5 minutes.

A number of operators are employed at the plant, who have been trained in all aspects.

Budget and Costs

The CNC workstation was purchased for PS72K. It must be repaid within three years.

The eight-hour shift is the norm at this plant.

The operators have a 15-minute break in the morning, afternoon, and lunch.

You will also need a budget of PS15K for the rest of the plant.

You must repay this expenditure within one year.

Prices

With the right tooling, you can assemble stations

Bins for machined gearboxes

Fixed cost of hiring an operator (advertising and training, etc.)

Hourly rate for operator including on-costs

Task

Optimize the plant for maximum revenue and budget.

It may be necessary to optimize the plant first for the machine priorities.

Each completed and functioning gearbox will generate PS80 in revenue.

Examine the impact of a 90-minute break between each CNC workstation’s conveyor and the CNC workstation on daily production rates.

Prepare a report that describes your model. Also, present the results of a series testing on the model.

Your conclusions should include an assessment of the model, taking into account all assumptions and identifying any limitations.

Answer:

Introduction

This work is based upon the manufacturing gear simulation. It is used to set up the separate plant. The conveyor will handle the 20m length as per the distribution in the excel file.

The conveyor tends to handle both the gearboxes and the assembly process.

Maximum capacity for the discrete even process is 180 parts at a speed of 5m/min.

The gear boxes were set up with the stock as castings and the workstations that can handle boring as well.

The operator can load the forms using the workstations that allow them to cast and then unload them to the bin of maximum capacity.

The inspection station for the bugger has been connected to the gearbox machine. This allows for distribution, handling, and distribution of maximum 2.5 kilos. It takes 3.2 minutes.

The two gears for the operator are generally used to set the machined gearbox.

The test benches can run for approximately 10 minutes.

The gearbox type determines how the operator loads or unloads the testing points.

The possibility of the setup includes forms that have gearbox for shipping. Failures are then sent directly to the disassembly stations.

It will take out the gears and place the conveyor at the end.

The simulation technology and the optimization are set up with the gearbox manufacturing facility. This is then simulated through the execution of various tasks.

The simulation tool works by analysing both the characteristics and the various sets that can take control of dynamic systems.

The stochastic approximation is used for discrete problems. This is then applied using the gradient approach.

The continuous event simulation is the one that includes all possible charges.

Continuous variable problems are also being discussed. The process is based upon Lanner Group Inc. and is designed to analyse and obtain the information.

The software was used to set up the gearbox manufacturing plant according to the requirements. It could be easily calculated the revenue and budget using the information provided.

The gradient research is used to determine the method.

Simulation software is used to develop CNC machines for both the production and the repair of defective products.

The operator controls the loading and unloading of parts. This ensures uniform distribution of the cycle times.

It takes approximately 3 minutes to set the capacity using the two gears and bearings of an operator.

This assembly is used to test the buffer and the bench.

It’s primarily used to perform loading operations, which generally take 0.2 to 0.4% minutes. The unloading of the gear boxes takes approximately 0.4 minutes.

Methodology

The base is the plant, which can easily be modelled using the Petri net with stochastic handling.

This tool was created using the concurrent model of distributed systems that includes transitions, places and the arc.

These are indications of the circles and rectangles as well as arrows to help the system handle the state change.

The model can also handle complexity.

This model was designed for machines, buffers, and parts that were used to test bins.

This helps to identify the locations of the gears and allows them to be supplied to the assembly via a set of conveyor at a fixed speed.

The focus of the work is on optimizing the plants in order to maximize the budget revenue.

This requires that the machine working is done in conjunction with the work which has been defined in the gearbox forms.

The purpose of the investigation is to determine if the CNC station holds the conveyor for approximately 90 minutes at a production rate that can handle the test series.

The Witness model was used to set the discrete, logical, and graphical elements.

There are many modules that can be used to identify the entities.

Modules and logical elements are used to handle the manufacturing of the near models or raw materials.

This includes simulation in the SHIP and SCRAP.

It is essential to have a buffer that can easily be filled into the components and then empty as required.

Witness machines can also be set with the classified forms for the activities, which include input rules, cycle times, and output rules. This allows the labour to easily be set with multi-cycle or multi station.

Continuous modelling and discrete events allow for the solution of many problems.

Continuous elements allow the modeling of fluids that are set through pipes with a greater volume. This is possible because of the speed of processing.

One could easily compare the power of witness to the PNDES, where the machines are pre-defined for labour and easy implementation.

It was found that the buffers were well-defined where the parts are. This is true for both the individual elements and statistical analysis.

The discrete event simulation is used for the search activities.

The witness machine type can handle the one machine and the processes for that particular part.

Both the assembly machine and the production machines can take the required number of parts as well as produce the necessary outputs.

Multi-station machines are used for linking.

There are many forms of numbers for part positions. They are used mainly to manage the different cycle times at stations. This allows one to specify the input and output quantities.

Results

The Witness Software was used to implement the model. It is interactive and interpretive and allows the simulator software handle discrete events as well as continuous events.

It is easy to build the model from scratch, which will demonstrate that it is powerful and easy to use.

The engine can also model different applications that are compatible with real-time system objects.

Combining the continuous flows and the discrete events creates models that can be easily addressed using a wide variety of effective approaches.

These elements allow the fluid to flow through pipes and tanks, allowing for higher flows in different areas of the process.

It also includes the modeling with 3D visualization that can easily deliver virtual reality performance.

The development of modular compartmentalized blocks is possible with the proper connectivity and database.

Discussion

Simulation of the results can be done primarily through the system modeling process, which may be useful for ideas with different characteristics.

It is easy to find the manual calculations which set the budget at 15000 pounds. This allows for maximum revenue for both the assembly and the benches using the 2CNC machines.

(Wilsone and colleagues, 2016).

A conveyor belt was also used. This budget is 13700.

The witness model is used primarily to optimize the forecast and to handle the proceedings mainly in order to increase and maximize the income.

Forecasting for liabilities changes and handling performance in real life where values can be evaluated with higher or lower forms of system forecasting will all be possible.

To understand the system’s impact, the breakdown performs analysis.

The conveyor belt is the main cause of the plant’s problems.

The Witness software system requirements are primarily to meet the minimal requirements:

A processor of AMD or Intel is available that is configured with high speed recommendations.

Windows Versions have been created for each platform.

There is 2GB RAM, which is more than 4GB.

The screen and hard drive contain the resolutions of the pixels.

Graphics generally require acceleration to achieve better performance.

This is why we recommend that you use the 3D environment with the best performance.

The USB contains the software, which can be used easily through the support portal.

This includes loading and unloading the gearbox, where shipping is possible.

Failures are sent to disassembly where the operator can easily remove the gear and replace it on the conveyor, mainly at its end.

The CNC workstation costs $72K and there is a 3 year repayment.

The machine also works on the 8 hour shift.

The plant is still available for purchase at $15K.

This cost includes the assembly station, which is used mainly for tooling purposes.

The plant’s cost function is largely based on the cost of direct and indirect costs.

Over 3 years, the cost of the grinder will be PS12000.

This is PS4000/year.

The operator PS1500 was set up for 1 year with PS15.00/hr.

Additional bin = PS200 for 1 year

The revenue from the sprindle comes in the form of PSS

Simulation Time is T hours

Actual Revenue is R

R = NShip (Spindle), X S, – 2TXX NQtyGrinder – 0.1TX NQtyBin – 15.75T* NQtyOperator

It is in its maximum form.

There are four types of conveyor: continuous fixed, continuous fixed, continuous queuing, indexed fixed and continuous queuing.

The type of conveyor is also used to model the category of movement.

The time taken by the conveyor to travel a certain distance has been used to model the motion.

It is dependent on the length, which can be defined as units. The speed can also be defined as units with time.

There are many types of parts that are designed using the fixed distance model, which can be used in conjunction with the belt and chain conveyor.

The roller conveyor, a queuing conveyor, can keep the parts moving towards the end.

Both the indirect and direct costs can easily be converted into direct cost with direct material and labour.

Indirect are used for tooling, machine speed, and administrative burdens for quality control.

Witness can support the multi-core processing of the model execution and allow the users to run replications with the results. This will provide the best outcome.

The simulation is built on the descriptions of the events and frameworks that allow for the time of each event at particular intervals.

These events occur instantly, but the activities can also be spread over time. The event sequence has been modelled.

Conclusion

Certain costs, usages and wastage can be used to reduce costs. This will directly impact material costs as they also affect tooling costs.

A reduction in the number of operations will result in a shorter cycle time and reduce the cost of labour or machine.

With the Witness model, machine rates can be easily calculated.

The state in which there are specifications of the intervals is used to simulate the events. This single threaded form holds both the intervals and the synchronisation between them.

The events are also based upon the interval-based modes and the priority queue, which is sorted at events’ time.

This model was designed to maximize revenue. It could be used for periodic periods for a budget of 13700 pounds.

The model also shows that the plant can easily generate the revenue based on forecasting and the ability to manage the income.

These requirements are calculated based on plants that have been subject to a detailed analysis and the breakdown of the plant within a week.

This also shows that different sufferers have been affected.

(Ozcan and colleagues, 2016). When the machines or units, mainly the CNC, are destroyed, the entire plant is affected. Then, it is subject to the blockage by the different machines.

These results show that the industry is not affected by the need for supply, human or external factors. They also have no need to address the factors that lead to poor performance.

Refer to

Zeigler, B.P.; Praehofer H.; and Kim T.G. 2000.

Theory of modeling simulation: Integrating discrete events and continuous dynamic systems.

Academic presses.

Sahu, A. and Pradhan S.K., September 2016.

Review of Quantitative analysis and optimization based on multiple evaluation criteria using discrete-event simulation:

International Conference on Automatic Control and Dynamic Optimization Techniques, (pp.

2017 and Ceglarek D.

A discrete event simulation model based on role activity diagrams for healthcare service delivery.

International Journal of Systems Science: Operations & Logistics. 4(1). pp.68-83.

Petti, A. Hutabarat W. Oyekan J. Turner, C. Turner, A. Tiwari A. Prajapat N. and Gan X.P. (2016)

Assessment of factory layout using immersive discrete events simulation: Impact of model fidelity

Prajapat (N.), Waller, T. Young, J., and Tiwari A. (2016).

Layout Optimization of a Repair Center Using Discrete Events Simulation.

Procedia CIRP 56, pp.574-579.

R. Iannone, Miranda, S. Miranda, L. Prisco. L. Riemma. S. and Sarno. D.

A proposal for a flexible, discrete event simulation model to assess daily operations decisions in a RoRo terminal.

Simulation Modelling Theory and Practice, 61, pages 28-46.

Alrabghi, A. and Tiwari A., 2016.

An innovative approach to modelling complex maintenance systems through discrete event simulation.

Reliability Engineering and System Safety, 154. pp. 160-170.

Packianather M.S. Davies, A. AlZarooni M.A. Soman, S., and White, J. July 2016.

Simulation and sustainable manufacturing can improve the flow of manufacturing processes.

World Automation Congress (WAC), 2016, pp.

Rybicka J., Tiwari A. and Enticott S., 2016.

A case study on the Automotive industry.

World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 10, pp.690-694.

Wilson, J.; Arokiam A., Belaidi H. and Ladbrook J., 2016.

An easy toolkit for energy usage based on manufacturing simulation data.

Journal of Cleaner Production, 122, pp. 266-276.

Barlas, P., and Heavey C., 2016.

Review of automation of input data for discrete event simulation in manufacturing:

International Journal of Modeling, Simulation and Scientific Computing, 7(1), p.1630001.

Kamaruddin S., 2017.

Analyse on the Effect of Shop Floor Parameters on Preventive Maintenance Through Discrete Event Simulation.

PERTANIKA JOURNAL of SOCIAL SCIENCE and HUMANITIES 25(1), pp.1-16

Galasso F., Ducq Y. Lauras M. Lauras M. Gourc D. and Camara M.

Simulation approach to selecting a successful interoperability solution.

Journal of Intelligent Manufacturing, 27(1) pp.217-229.

Prajapat, N., and Tiwari A., 2017.

Review of assembly optimization applications using discrete-event simulation.

International Journal of Computer Integrated Manufacturing 30(2), pp. 215-228.

Masek C.M. (2016)

Analyse of Clinic Layouts, and Patient-Centered Procedure Innovations Using Discrete-Event Simulation.

Knights, P., 2016.

A discrete-event model that simulates truck bunching caused by payload variance on cycle times, hauled mining materials and fuel consumption.

International Journal of Mining Science and Technology 26(5), pp.745-752.

Pitt, M., Monks T., Crowe S. and Vasilakis C.

Simulation and systems modeling in healthcare service delivery, design and decision-making.

BMJ safety and quality, 25(1), pages 38-45.

Ozcan, Y.A. Tanfani E., and Testi A., 2016.

Vaatainen (K.), Anttila (P.) and Annevelink (E.), 2016.

6: Annexe 3 Logistical case study from Finland.