Simulation study of a semi-automated flexible production line

In today’s highly competitive and challenging marketplace, manufacturing process improvement is more important than ever before. Conversely, it is probably also harder to achieve than at any time in the past. This is due to several factors. High levels of capital investment combined with short product life cycles mean that maximising utilisation levels of expensive equipment is essential. Increasingly complex production facilities are difficult to analyse and improve. The possibility of worsening the situation rather than improving it means that experimentation on the line itself is often a risk not worth taking. One solution to this problem is the use of computer based manufacturing system simulation. Simulation studies are beneficial because they remove the element of risk associated with experimentation. Potential process improvement strategies can be identified, evaluated, compared and chosen in a virtual environment before eventual implementation on the factory floor. This research aimed to evaluate the use of discrete event system simulation in a real world manufacturing environment. To this end, a flexible simulation model of the main transfer line of Läpple Ireland, a large metal panel production facility, was designed and constructed using Extend simulation software. In conjunction with Läpple personnel, various ‘what if’ scenarios were identified and evaluated. These scenarios were aimed at deciding the best position for providing additional automation by investing in robots. From the results of the simulation modelling of the three main proposed modifications to the line, improvements of 9%, 18% and 33% in press line throughput were predicted. The negative effect on these improvements in the case that the proposed robots failed to achieve the desired speeds were evaluated. These negative effects were found to be not as dramatic as could be expected. The results were compared to those of similar research efforts elsewhere. Finally, future steps for the research to take were identified and suggestions for future areas of application for the model were made

Reusable modelling and simulation of flexible manufacturing for next generation semiconductor manufacturing facilities

Automated material handling systems (AMHS) in 300 mm semiconductor manufacturing facilities may need to evolve faster than expected considering the high performance demands on these facilities. Reusable simulation models are needed to cope with the demands of this dynamic environment and to deliver answers to the industry much faster. One vision for intrabay AMHS is to link a small group of intrabay AMHS systems, within a full manufacturing facility, together using what is called a Merge/Diverge link. This promises better operational performance of the AMHS when compared to operating two dedicated AMHS systems, one for interbay transport and the other for intrabay handling. A generic tool for modelling and simulation of an intrabay AMHS (GTIA-M&S) is built, which utilises a library of different blocks representing the different components of any intrabay material handling system. GTIA-M&S provides a means for rapid building and analysis of an intrabay AMHS under different operating conditions. The ease of use of the tool means that inexpert users have the ability to generate good models. Models developed by the tool can be executed with the merge/diverge capability enabled or disabled to provide comparable solutions to production demands and to compare these two different configurations of intrabay AMHS using a single simulation model. Finally, results from simulation experiments on a model developed using the tool were very informative in that they include useful decision making data, which can now be used to further enhance and update the design and operational characteristics of the intrabay AMHS

Intelligent shop scheduling for semiconductor manufacturing

Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process. Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant

Methodology to develop hybrid simulation/emulation model.

Trends towards reduced life-time of products and globalised competition has increased pressure on manufacturing industries to be more responsive to changing needs of product markets. Consequently, the use of simulation to describe short term future performance of manufacturing system has become more significant than ever. An application of simulation that has attracted attention is for testing of control logic before commissioning on site by using a detailed simulation model called emulation model. However, though the success of using emulation particularly in improving cost-effectiveness of automated material handling system delivery has been acknowledged by industries and simulation model developers, the uptake for this technology is still low. The major inhibitors are the high costs of its model building as well as simulation and emulation models are perceived to be non convertible.The main objective, of this research is to establish a methodology to develop simulation model that can be converted into emulation model with ease, thus making emulation technology more affordable. The product of this research called the methodology to build Hybrid Simulation Emulation Model (HSEM) is a new approach of building emulation model comprising of three phases namely (1) development of base simulation model, (2) development of detail emulation model, and (3) integration of controller with the emulation model. Important requirements for HSEM are flexibility of adding details to the simulation model and inter process communication between model and real control system. To facilitate implementation of the methodology, it is essential that the simulation software package provide functionalities for modular model development, access and adding of codes, integration with other application and real time (RT) modelling.The methodology developed offers a more affordable emulation modelling and an opening for further research into the comprehensive support for the implementation of real time control system testing using emulation

Design of adaptable simulation models.

In today's world, with ever increasing competition, modelling and simulation proves to be a very helpful tool. Many methodologies exist to help build a simulation model from scratch. In terms of adaptability, most current attempts focus on either the operational side, ie the automated integration of data into a model, or the creation of new software. However, very few attempts are being made to improve the adaptability of shelved models built in existing simulation software. As a result, there is a certain reluctance, in some areas, to use simulation to its full potential.Based on these facts, it is obvious that anything, which makes reuse of simulation models easier, can help improve the use and spread of simulation as a valuable tool to maintain a company's competitiveness. In order to find such a solution, the following issues are looked at in this thesis: The changes to a simulation model that constitute the biggest problem, ways to minimise those changes, and possibilities to simplify the implementation of those changes. Those factors are evaluated, first by investigating current practices of building adaptable simulation models via a literature review, then the most difficult changes to implement in a simulation model, and the most frequent types of simulation software, are identified by means of interviews and questionnaire surveys. Next, parameters describing the adaptability of a simulation model are defined. In a further step, two of the most widely used simulation packages are benchmarked against a variety of tasks, reflecting the changes most frequent to models. The benchmarking study also serves to define and test certain elements regarding their suitability for adaptable models. Based on all those steps, model building guidelines for the creation of adaptable simulation models are developed and then validated by means of interviews and a framed field experiment. The interviews and questionnaire reveal that deleting is the easiest task and modifying the most complicated, while handling devices are the most difficult element to modify. The results also show that simulators (eg Arena) are the most widespread type of simulation software. The benchmarking showed that Arena is overall more adaptable than Simul8, and confirms the findings from the user survey. Also, it shows that sequencing is very helpful for modifying models, while the use of sub-models decrease the adaptability. Finally, the validation proves that the model building guidelines substantially increase the adaptability of models