Sohyung Cho

A distributed time-driven simulation method for enabling real-time manufacturing shop floor control

This paper proposes a distributed simulation approach for scheduling discrete-events in manufacturing shop floors. The proposed approach employs a time-driven method to simulate occurrence of discrete-events using distributed entities that replicate physical entities in the manufacturing shop floor. In specific, the proposed approach iteratively controls the timing of discrete-events occurrence using a control theoretic model. In this approach, changing the speed of the simulation clock, termed time-scaling factor, can accelerate or decelerate the simulation speed resulting in simpler synchronizations of discrete-events and faster simulation than standard distributed discrete-event simulations according to the capability of the communication networks. Computational experiments are conducted to test the performance of the proposed system with different values of the time-scaling factor, and the relationship between the system performance and the time-scaling factor is investigated through analysis of the system model. Results obtained from the computational experiments show significant successes in speeding up discrete-event simulations in such a way that the proposed approach can be used for the control of manufacturing shop floors, providing real-time decision supports.

A vector space model for variance reduction in single machine scheduling

Reducing the variance of part completion times about promised due dates is an important element of Just-In-Time production because it reduces the work-in-process inventory and tardiness simultaneously. Scheduling models and algorithms are developed to minimize the Mean Squared Deviation (MSD) of completion times about due dates on a single machine. A generic model is developed in real vector space for understanding the structural relationship between the optimal schedule and the location of the due dates. Geometric insights gained from this vector space model are used to relate the shortest and longest processing time sequences to the level of difficulty of the MSD optimization problem. The vector space model is used to develop dominance conditions for a branch and bound algorithm and to analytically synthesize parameters for a continuous variable feedback control algorithm for distributed scheduling. The control algorithm lends itself to massively parallel / distributed computation and is found to produce near optimal solutions efficiently, which makes it more scalable and practical compared to the branch and bound algorithm. Computational experiments with both approaches are presented.

Interaction-based complexity measure of manufacturing systems using information entropy

The primary objective of this paper is to develop a model that can quantitatively assess the complexity of manufacturing systems in various configurations including assembly and disassembly systems. In this paper, an analytical model for measuring the system complexity that employs information entropy is proposed and verified. The model uses probability distribution of information regarding resource allocations such as part processing times, part mix ratios and process plans or routings. In the proposed framework, both direct and indirect interactions among resources are first modelled using a matrix, referred to as interaction matrix in this paper, which accounts for part processing and waiting times. The proposed complexity model in this paper identifies a manufacturing system that has evenly distributed interactions among resources as being more complex, because in this case more information is required to identify source of the disruption. Then, the proposed framework is applied for the operation of a complicated manufacturing system taken from a previous work. Finally, relationships between the system complexity and performance in terms of resource utilisations and throughput of the system are studied through case studies. It is shown that the application of the proposed measure can result in optimal operating policies for the companies considered in the case studies.

Design of predictable production scheduling model using control theoretic approach

As one of the most important planning and operational issues in manufacturing systems, production scheduling generally deals with allocating a set of resources over time to perform a set of tasks. Recently, control theoretic approaches based on nonlinear dynamics of continuous variables have been proposed to solve production scheduling problems as an alternative to traditional production scheduling methods that deal with decision-making components in discrete nature. The major goal of this paper is to improve predictability and performance of an existing scheduling model that employs the control theoretic approach, called distributed arrival time controller (DATC), to manage arrival times of parts using an integral controller. In this paper, we first review and investigate unique dynamic characteristics of the DATC in regards to convergence and chattering of arrival times. We then propose a new arrival time controller for the DATC that can improve predictability and performance in production scheduling. We call the new mechanism the double integral arrival-time controller (DIAC). We analyse unique characteristics of the DIAC such as oscillatory trajectory of arrival times, their oscillation frequency, and sequence visiting mechanism. In addition, we compare scheduling performance of the DIAC to the existing DATC model through computational experiments. The results show that the proposed system can be used as a mathematical and simulation model for designing adaptable manufacturing systems in the future.

Controller design for an autonomous underwater vehicle using nonlinear observers

The depth and heading control of an autonomous underwater vehicle (AUV) are considered to follow the predetermined depth and heading angle. The proposed control algorithm was based on a sliding mode control, using estimated hydrodynamic coefficients. The hydrodynamic coefficients were estimated employing conventional nonlinear observer techniques, such as sliding mode observer and extended Kalman filter. Using the estimated coefficients, a sliding mode controller was constructed for a combined diving and steering maneuver. The simulated results of the proposed control system were compared with those of a control system that employed true coefficients. This paper demonstrated the proposed control system, and discusses the mechanisms that make the system stable and accurately follow the desired depth and heading angle in the presence of parameter uncertainty.

Estimation of true efficient frontier of organisational performance using data envelopment analysis and support vector machine learning

Data envelopment analysis (DEA) and stochastic frontier functions (SFF) are two well-known tools for performance and efficiency analysis of profit and non-profit organisations, referred to as decision making units (DMUs). The challenge to traditional DEA is how to account for both managerial and observational errors if present in the analysis, so as to determine true efficient frontiers. This paper proposes a novel methodology to determine true frontiers in a non-parametric environment. Specifically, traditional DEA is integrated with SFF through support vector machine (SVM) learning to provide an adaptive way to estimate true frontiers considering managerial and observational errors. A statistical ratio is utilised to find the true frontiers, and the proposed methodology is applied to a real data set where frontiers are compared to ones obtained by other existing methods. The work in this paper can help organisations to plan a more realistic investment by providing reasonable sense of benchmarking.

Web-based algorithm for cylindricity evaluation using support vector machine learning

This paper introduces a cylindricity evaluation algorithm based on support vector machine learning with a specific kernel function, referred to as SVR, as a viable alternative to traditional least square method (LSQ) and non-linear programming algorithm (NLP). Using the theory of support vector machine regression, the proposed algorithm in this paper provides more robust evaluation in terms of CPU time and accuracy than NLP and this is supported by computational experiments. Interestingly, it has been shown that the SVR significantly outperforms LSQ in terms of the accuracy while it can evaluate the cylindricity in a more robust fashion than NLP when the variance of the data points increases. The robust nature of the proposed algorithm is expected because it converts the original nonlinear problem with nonlinear constraints into other nonlinear problem with linear constraints. In addition, the proposed algorithm is programmed using Java Runtime Environment to provide users with a Web based open source environment. In a real-world setting, this would provide manufacturers with an algorithm that can be trusted to give the correct answer rather than making a good part rejected because of inaccurate computational results.

Sensor stream mining for tool condition monitoring

In metal cutting processes, as the surface of the cutting tool worn out, it releases certain odorous compounds into the cutting chamber air. This study proposes a novel approach and its associated stream mining methods to monitor the cutting tool condition by taking advantage of this phenomenon. The chemical composition of the gases released during the cutting process can be used to monitor the tool condition very accurately that is not possible with present direct or indirect measurement techniques. The chemical compounds released and captured by an e-nose change as the tool wear progresses. This change can be made significant as appropriate doping material were identified and doped into the different layers of the tool inserts, thus allowing a well trained data mining system to accurately estimate the level of tool wear.

Volumetric Calibration of Stereo Camera in Visual Servo Based Robot Control

The primary objective of the paper is to propose a calibration method for a stereo camera used in a visual servo control for a robot manipulator. Specifically, projection matrix between the stereo camera and world coordinates is established using few calibration points and solved using the single value decomposition technique. Then calibration accuracy is compared for a randomized and designed set of points, and economical number of calibration points is recommended. Additionally, the non-linear lens distortion is modeled and corrected to improve the accuracy. In addition, this research focuses on the development and implementation of a fully automated visual servo control system using a stereo camera that is calibrated by proposed method.

Rapid manufacturing of rhenium components using EB‐PVD

Purpose – The purpose of this paper is to provide insights for understanding the relationship between rapid manufacturing process for rhenium components in jet nozzle fabrication using electron beam‐physical vapor deposition (EB‐PVD). Specifically, to develop a methodology to characterize and improve this new process through motion planning for maintaining uniformity in the deposition thickness.Design/methodology/approach – This research first identifies several important objectives for the process, and then develops an optimized heuristic method based on a look‐ahead approach to generate motion plans for uniform thickness objective. In this heuristic, the surface of the workpiece is modeled using finite element method and the accumulated thickness of each layer on each element is computed based on its location in the vapor plume using a ray casting algorithm.Findings – Computational experiments show that the proposed algorithm can potentially provide significant improvements in the uniformity of the laye...