The Experts below are selected from a list of 6531 Experts worldwide ranked by ideXlab platform

Xiongbiao Chen - One of the best experts on this subject based on the ideXlab platform.

  • Modeling of the Fluid Volume Transferred in Contact Dispensing Processes
    IEEE Transactions on Electronics Packaging Manufacturing, 2009
    Co-Authors: Xiongbiao Chen, N. Cao
    Abstract:

    In the contact Dispensing Process, the contact of the fluid with the target board is essentially needed in order to transfer a certain volume of fluid to the board. Due to the action of surface tension, part of the fluid extruded from the needle hangs on the needle after the Process, and this causes the difference between the fluid volume extruded and the one transferred to the board. This difference is usually ignored in the literature, yet is critical to the precise Process control. In this paper, a model to represent the difference is developed based on the Young-Laplace capillarity equation as well as the boundary conditions established for this particular problem. Experiments and simulations were carried out to verify the model effectiveness as well as to investigate the influence of the fluid volume extruded from the needle, the needle size, and the initial height of the needle on the fluid volume transferred in the contact Dispensing Process.

  • Modeling and control of fluid Dispensing Processes: a state-of-the-art review
    The International Journal of Advanced Manufacturing Technology, 2008
    Co-Authors: Xiongbiao Chen
    Abstract:

    Fluid Dispensing is a method by which fluid materials, such as epoxy, adhesive, and encapsulant, are delivered in a controlled manner in electronics packaging. This paper presents a brief review of past and recent developments in the modeling and control of the time-pressure fluid Dispensing Process. In particular, the characterization of the fluid flow behavior is addressed by reviewing several promising models from both time-independent and time-dependent perspectives. In the modeling of the time-pressure fluid Dispensing Process, various approaches for representing the flow rate of fluid dispensed and the profile of fluid formed on target are examined; and the issues involved are identified. In the control of time-pressure Dispensing Process, a brief review of various control methods is presented along with their limitations. The challenges associated with this control problem are also discussed. This paper is concluded with the recommendations of research in the future.

  • THEORETICAL INVESTIGATION INTO THE PERFORMANCE OF THE ROTARY-SCREW FLUID Dispensing Process
    Transactions of the Canadian Society for Mechanical Engineering, 2008
    Co-Authors: M. Hashemi, Xiongbiao Chen
    Abstract:

    This paper represents the development of a dynamic model for the rotary-screw Dispensing Process, by taking into accounts for both fluid compressibility and non-Newtonian flow behavior. In particular, the flow behavior of the fluid being dispensed is characterized by using the power law equation; and then based on the fundamentals of flow in the screw channel and needle, a model is developed to represent the dynamics of the flow rate in the rotary-screw Dispensing Process. Simulations are carried out to investigate the Process performance, with an emphasis on identifying the influence of the key Process parameters.

  • Modeling of Rotary Screw Fluid Dispensing Processes
    Journal of Electronic Packaging, 2006
    Co-Authors: Xiongbiao Chen
    Abstract:

    Fluid Dispensing is a Process widely used in electronics packaging manufacturing, by which fluid materials are delivered in a controlled manner for the purpose of bonding, sealing, coating, or conducting. Among various Dispensing approaches, the use of a motor-driven screw is recognized as one of the most promising approaches due to its capacity of achieving high flow rates without the need of refilling. In a Dispensing Process, the flow rate of fluid dispensed is critical to control the volume or amount of fluid dispensed. This paper presents the development of a model for the rotary screw Dispensing Process. By using the power law equation, the flow behavior of the fluid being dispensed is characterized and then, based on the fundamentals of flow in screw channels and circular tubes, a model is developed to represent the flow rate in the rotary screw Dispensing Process. Experiments and simulations were carried out to verify the model effectiveness as well as to investigate the performance of the rotary screw Dispensing Process.

  • Effects of fluid properties on Dispensing Processes for electronics packaging
    IEEE Transactions on Electronics Packaging Manufacturing, 2006
    Co-Authors: Xiongbiao Chen
    Abstract:

    The fluid Dispensing Process has been widely employed in electronics packaging manufacturing to deliver fluid materials (such as epoxy, encapsulant, adhesive) on substrates or printed circuit boards (PCBs) for the purpose of die attachment, encapsulation, coating, or surface mounting. In this Process, the fluid properties such as How behavior, surface tension, and contact angle can have a significant influence on the How rate of the fluid dispensed and the profile of fluid formed on the substrate or PCB, thereby affecting the quality of electronics packaging. At present, massive measurements are always required to characterize the fluid properties by using specific instruments, and the procedure of measuring is time-consuming. This paper presents a method upon which the fluid properties and their influence on the Dispensing Process can be readily identified from a few measurements of the Process. By experiments, this method was proven to be not only cost and time effective but also promising for the investigation into the effects of fluid properties on the Dispensing Process.

W J Zhang - One of the best experts on this subject based on the ideXlab platform.

  • modeling and control of Dispensing Processes for surface mount technology
    IEEE-ASME Transactions on Mechatronics, 2005
    Co-Authors: Xiongbiao Chen, Greg Schoenau, W J Zhang
    Abstract:

    Dispensing is a key Process in surface mount technology (SMT), in which minute amounts of fluid materials (such as solder paste, adhesive) are delivered controllably onto printed circuit boards for the purpose of conducting, bonding, etc. Time-pressure Dispensing by means of pressurized air is currently the most widely used approach in SMT. Due to air compressibility, the control of the time-pressure Dispensing Process has proven to be a challenging task in achieving a high degree of consistency in the amount of fluid dispensed. This paper presents the development of a model of the amount of fluid dispensed by taking air compressibility into account. Based on the model, a control strategy is then developed to improve the consistency in the amount of fluid dispensed. Experiments were conducted to verify the effectiveness of the control strategy.

  • Characterization of the Time-Dependent Rheological Behavior of Fluids for Electronics Packaging
    Fluids Engineering, 2003
    Co-Authors: X. B. Chen, W J Zhang, G. Schoenau, B. W. Surgenor
    Abstract:

    To effectively control the Dispensing Process by which fluids are delivered onto substrates in electronics packaging, one of the key issues is to understand and characterize the flow behavior of the fluids being dispensed. However, this task has proven to be a demanding one as the fluids used for electronics packaging usually exhibit the time-dependent rheological behavior, which has not been well documented in the literature. In this paper, the characterization of time-dependent rheological behavior of fluids is studied. In particular, a model using the structural theory is proposed and applied to the characterization of the decay and recovery of fluid behavior, which are typically encountered in a Dispensing Process. Experiments are conducted to validate the proposed model.Copyright © 2003 by ASME

  • Off-line control of time-pressure Dispensing Processes for electronics packaging
    IEEE Transactions on Electronics Packaging Manufacturing, 2003
    Co-Authors: Xiongbiao Chen, W J Zhang, G. Schoenau, B. Surgenor
    Abstract:

    Fluid Dispensing is one critical Process in electronics packaging, in which fluid materials (such as encapsulant, adhesive) are delivered controllably onto substrates for the purpose of encapsulation. Time-pressure Dispensing is recently the most widely used approach, and its control has proven to be a challenging task due to the fact that the Dispensing Process performance is significantly affected by the behavior of the fluid dispensed. Moreover, if the fluid exhibits time-dependent behavior, the control becomes more difficult and demanding. This paper presents a method to model the time-pressure Dispensing Process, taking into account the time-dependent fluid behavior. Based on the model developed, an off-line control strategy is developed for improving the Process performance. Experiments on a typical commercial Dispensing system were carried out to verify the effectiveness of the modeling method and the off-line control strategy.

  • Modelling of time-pressure fluid Dispensing Processes
    IEEE Transactions on Electronics Packaging Manufacturing, 2000
    Co-Authors: Xiongbiao Chen, G. Schoenau, W J Zhang
    Abstract:

    The Process of time-pressure fluid Dispensing has been widely employed in the semiconductor industry, where the fluid is applied to boards or substrates. In such a Process, the flow rate of fluid dispensed and the shape of fluid formed on the board are the two most critical performance indexes, yet extremely difficult to represent because of their complex behavior. This paper presents the development of a model for the time-pressure fluid Dispensing Process, by which the flow rate and shape can be established. Experiments have been performed to validate the model developed.

Kit Yan Chan - One of the best experts on this subject based on the ideXlab platform.

  • Modeling of epoxy Dispensing Process using a hybrid fuzzy regression approach
    The International Journal of Advanced Manufacturing Technology, 2012
    Co-Authors: Kit Yan Chan, C. K. Kwong
    Abstract:

    In the semiconductor manufacturing industry, epoxy Dispensing is a popular Process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy Dispensing Process is important because it enables us to understand the Process behavior, as well as determine the optimum operating conditions of the Process for a high yield, low cost, and robust operation. Previous studies of epoxy Dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy Dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the Process in a real-world environment. Previous studies of modeling the epoxy Dispensing Process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between Process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two Process models are generated for the two quality characteristics of the Process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression.Department of Industrial and Systems Engineerin

  • Modeling of epoxy Dispensing Process using a hybrid fuzzy regression approach
    The International Journal of Advanced Manufacturing Technology, 2012
    Co-Authors: Kit Yan Chan, C. K. Kwong
    Abstract:

    In the semiconductor manufacturing industry, epoxy Dispensing is a popular Process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy Dispensing Process is important because it enables us to understand the Process behavior, as well as determine the optimum operating conditions of the Process for a high yield, low cost, and robust operation. Previous studies of epoxy Dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy Dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the Process in a real-world environment. Previous studies of modeling the epoxy Dispensing Process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between Process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two Process models are generated for the two quality characteristics of the Process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression.

  • Brief paper: Modelling and optimization of fluid Dispensing for electronic packaging using neural fuzzy networks and genetic algorithms
    Engineering Applications of Artificial Intelligence, 2010
    Co-Authors: Kit Yan Chan, C. K. Kwong, Y. C. Tsim
    Abstract:

    Determination of Process conditions for a fluid Dispensing Process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid Dispensing Process is important because it enables us to understand the Process behaviour, as well as determine the optimum operating conditions of the Process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid Dispensing Processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid Dispensing Process for microchip encapsulation. An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modelling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modelling the fluid Dispensing. It is then followed by the determination of Process conditions of the Process based on a genetic algorithm approach. Validation tests were conducted. Results of them indicate that Process conditions determined based on the proposed approaches can achieve the specified quality requirements.

  • A genetic algorithm based knowledge discovery system for the design of fluid Dispensing Processes for electronic packaging
    Expert Systems with Applications, 2009
    Co-Authors: C. K. Kwong, Kit Yan Chan, Y. C. Tsim
    Abstract:

    In the semiconductor manufacturing industry, fluid Dispensing is a very common Process used for die-bonding and microchip encapsulation in electronics packaging. Understanding the Process behaviour is important as it aids in determining appropriate settings of the Process parameters for a high-yield, low cost and robust operation. In this paper, a genetic algorithm (GA) based knowledge discovery system is proposed to discover knowledge about the fluid Dispensing Process. This knowledge is expressed in the form of rules derived from experimental data sets. As a result, appropriate parameters can be set which will be more effective with respect to the required quality of encapsulation. Rules generated by the GA based knowledge discovery system have been validated using a computational system for Process optimization of fluid Dispensing. The results indicate that the rules generated are useful and promising in aiding optimization of the fluid Dispensing Process in terms of better optimization results and shorter computational time.

  • improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid Dispensing for electronic packaging
    IEEE Transactions on Industrial Electronics, 2008
    Co-Authors: Sai Ho Ling, Herbert Ho-ching Iu, F.h.f. Leung, Kit Yan Chan
    Abstract:

    An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for modeling the development of fluid Dispensing for electronic packaging (MFD-EP) is presented in this paper. In modeling the fluid Dispensing Process, it is important to understand the Process behavior as well as determine the optimum operating conditions of the Process for a high-yield, low-cost, and robust operation. Modeling the fluid Dispensing Process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.

C. K. Kwong - One of the best experts on this subject based on the ideXlab platform.

  • Modeling of epoxy Dispensing Process using a hybrid fuzzy regression approach
    The International Journal of Advanced Manufacturing Technology, 2012
    Co-Authors: Kit Yan Chan, C. K. Kwong
    Abstract:

    In the semiconductor manufacturing industry, epoxy Dispensing is a popular Process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy Dispensing Process is important because it enables us to understand the Process behavior, as well as determine the optimum operating conditions of the Process for a high yield, low cost, and robust operation. Previous studies of epoxy Dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy Dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the Process in a real-world environment. Previous studies of modeling the epoxy Dispensing Process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between Process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two Process models are generated for the two quality characteristics of the Process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression.Department of Industrial and Systems Engineerin

  • Modeling of epoxy Dispensing Process using a hybrid fuzzy regression approach
    The International Journal of Advanced Manufacturing Technology, 2012
    Co-Authors: Kit Yan Chan, C. K. Kwong
    Abstract:

    In the semiconductor manufacturing industry, epoxy Dispensing is a popular Process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy Dispensing Process is important because it enables us to understand the Process behavior, as well as determine the optimum operating conditions of the Process for a high yield, low cost, and robust operation. Previous studies of epoxy Dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy Dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the Process in a real-world environment. Previous studies of modeling the epoxy Dispensing Process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between Process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two Process models are generated for the two quality characteristics of the Process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression.

  • Brief paper: Modelling and optimization of fluid Dispensing for electronic packaging using neural fuzzy networks and genetic algorithms
    Engineering Applications of Artificial Intelligence, 2010
    Co-Authors: Kit Yan Chan, C. K. Kwong, Y. C. Tsim
    Abstract:

    Determination of Process conditions for a fluid Dispensing Process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid Dispensing Process is important because it enables us to understand the Process behaviour, as well as determine the optimum operating conditions of the Process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid Dispensing Processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid Dispensing Process for microchip encapsulation. An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modelling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modelling the fluid Dispensing. It is then followed by the determination of Process conditions of the Process based on a genetic algorithm approach. Validation tests were conducted. Results of them indicate that Process conditions determined based on the proposed approaches can achieve the specified quality requirements.

  • A genetic algorithm based knowledge discovery system for the design of fluid Dispensing Processes for electronic packaging
    Expert Systems with Applications, 2009
    Co-Authors: C. K. Kwong, Kit Yan Chan, Y. C. Tsim
    Abstract:

    In the semiconductor manufacturing industry, fluid Dispensing is a very common Process used for die-bonding and microchip encapsulation in electronics packaging. Understanding the Process behaviour is important as it aids in determining appropriate settings of the Process parameters for a high-yield, low cost and robust operation. In this paper, a genetic algorithm (GA) based knowledge discovery system is proposed to discover knowledge about the fluid Dispensing Process. This knowledge is expressed in the form of rules derived from experimental data sets. As a result, appropriate parameters can be set which will be more effective with respect to the required quality of encapsulation. Rules generated by the GA based knowledge discovery system have been validated using a computational system for Process optimization of fluid Dispensing. The results indicate that the rules generated are useful and promising in aiding optimization of the fluid Dispensing Process in terms of better optimization results and shorter computational time.

  • IEEE Congress on Evolutionary Computation - A GA-based data mining approach to Process improvement of fluid Dispensing for electronic packaging
    2007 IEEE Congress on Evolutionary Computation, 2007
    Co-Authors: K.y. Chan, Sai Ho Ling, C. K. Kwong
    Abstract:

    Determination of the initial Process parameters for fluid Dispensing Process is a highly skilled task and is usually based on skilled engineers' intuitive sense acquired through long-term experience rather than on a knowledge-based approach. In the face of global competition, the current trial-and -error practice is inadequate. In this paper, a rule-based system is developed to aid the determination of initial Process parameters for fluid Dispensing Process by the genetic algorithm. Based on the rule based system, a set of ranges of Process parameters can be recommended with a pre-defined quality requirement of microchip encapsulation. The preliminary validation test of the rule-based system has indicated that it can determine a set of ranges of initial Process parameters for fluid Dispensing Process effectively, from which quality requirement can be achieved without totally relying on engineers' experience.

Sai Ho Ling - One of the best experts on this subject based on the ideXlab platform.

  • improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid Dispensing for electronic packaging
    IEEE Transactions on Industrial Electronics, 2008
    Co-Authors: Sai Ho Ling, Herbert Ho-ching Iu, F.h.f. Leung, Kit Yan Chan
    Abstract:

    An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for modeling the development of fluid Dispensing for electronic packaging (MFD-EP) is presented in this paper. In modeling the fluid Dispensing Process, it is important to understand the Process behavior as well as determine the optimum operating conditions of the Process for a high-yield, low-cost, and robust operation. Modeling the fluid Dispensing Process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.

  • IJCNN - Modelling the development of fluid Dispensing for electronic packaging: Hybrid Particle Swarm Optimization based-wavelet neural network approach
    2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
    Co-Authors: Sai Ho Ling, Herbert Ho-ching Iu, F.h.f. Leung, Kit Yan Chan
    Abstract:

    An hybrid particle swarm optimization PSO-based wavelet neural network for modelling the development of fluid Dispensing for electronic packaging is presented in this paper. In modelling the fluid Dispensing Process, it is important to understand the Process behaviour as well as determine optimum operating conditions of the Process for a high-yield, low cost and robust operation. Modelling the fluid Dispensing Process is a complex non-linear problem. This kind of problem is suitable to be solved by neural network. Among different kinds of neural networks, the wavelet neural network is a good choice to solve the problem. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameters, the network becomes an adaptive one. Thus, the proposed network provides better performance and increased learning ability than conventional wavelet neural networks. An improved hybrid PSO is applied to train the parameters of the proposed wavelet neural network. A case study of modelling the fluid Dispensing Process on electronic packaging is employed to demonstrate the effectiveness of the proposed method.

  • IEEE Congress on Evolutionary Computation - A GA-based data mining approach to Process improvement of fluid Dispensing for electronic packaging
    2007 IEEE Congress on Evolutionary Computation, 2007
    Co-Authors: K.y. Chan, Sai Ho Ling, C. K. Kwong
    Abstract:

    Determination of the initial Process parameters for fluid Dispensing Process is a highly skilled task and is usually based on skilled engineers' intuitive sense acquired through long-term experience rather than on a knowledge-based approach. In the face of global competition, the current trial-and -error practice is inadequate. In this paper, a rule-based system is developed to aid the determination of initial Process parameters for fluid Dispensing Process by the genetic algorithm. Based on the rule based system, a set of ranges of Process parameters can be recommended with a pre-defined quality requirement of microchip encapsulation. The preliminary validation test of the rule-based system has indicated that it can determine a set of ranges of initial Process parameters for fluid Dispensing Process effectively, from which quality requirement can be achieved without totally relying on engineers' experience.