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

Intelligent feature based resource selection and process planning

Lien vers la version éditeur: https://www.inderscience.com/books/index.php?action=record&rec_id=755&chapNum=3&journalID=1022&year=2010This paper presents an intelligent knowledge-based integrated manufacturing system using the STEP feature-based modeling and rule based intelligent techniques to generate suitable process plans for prismatic parts. The system carries out several stages of process planning, such as identification of the pairs of feature/tool that satisfy the required conditions, generation of the possible process plans from identified tools/machine pairs, and selection of the most interesting process plans considering the economical or timing indicators. The suitable processes plans are selected according to the acceptable range of quality, time and cost factors. Each process plan is represented in the tree format by the information items corresponding to their CNC Machine, required tools characteristics, times (machining, setup, preparatory) and the required machining sequences. The process simulation module is provided to demonstrate the different sequences of machining. After selection of suitable process plan, the G-code language used by CNC machines is generated automatically. This approach is validated through a case

Integrated Process Simulation and Die Design in Sheet Metal Forming

During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent achievements in this very important field of sheet metal forming and to introduce some special results in this development activity. Therefore, in this paper, an integrated process simulation and die design system developed at the University of Miskolc, Department of Mechanical Engineering will be analysed. The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet metal forming in the very important segment of industry. The concept described in this paper may have specific value both for process planning and die design engineers

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Recent Achievements in Numerical Simulation in Sheet Metal Forming Processes

Purpose of this paper: During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent achievements in this very important field of sheet metal forming and to introduce some special results in this development activity. Design/methodology/approach: Concerning the CAE activities in sheet metal forming, there are two main approaches: one of them may be regarded as knowledge based process planning, whilst the other as simulation based process planning. The author attempts to integrate these two separate developments in knowledge and simulation based approach by linking commercial CAD and FEM systems. Findings: Applying the above approach a more powerful and efficient process planning and die design solution can be achieved radically reducing the time and cost of product development cycle and improving product quality. Research limitations: Due to the different modelling approaches in CAD and FEM systems, the biggest challenge is to enhance the robustness of data exchange capabilities between various systems to provide an even more streamlined information flow. Practical implications: The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet metal forming in the very important segment of industry. Originality/value: The concept described in this paper may have specific value both for process planning and die design engineers

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Manufacturing Process Modeling and Simulation

This paper presents a methodology to be employed in the whole process design phase including first and second processing. This methodology consists of a set of steps which are characterised by an independent model. This paper’s objective is to analyse the coherence between the different models and the coherence between the model and the objectives of each step. The final stage is to develop the production plans. The casting process was the first one to be analyzed. Casting models were created using CAD software (Catia V5R17) and imported into the casting simulation environment (Magmasoft). Filling and solidifying processes have been simulated using different casting models in order to optimize the final configuration. The machining process was modeled using the machining features concept and it was simulated using Catia’s Advanced Machining environment. Two machining strategies have been analyzed according to positioning strategies. Process engineering software was used to create the process plans and to analyze the resource allocation

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin