2014 IEEE International Conference on Automation Science and Engineering (CASE), 2014
For complex multi-axis or compound machine tools, checking machining processes by simulation soft... more For complex multi-axis or compound machine tools, checking machining processes by simulation software is needed. This paper aims to develop open virtual machine tool (VMT) for cloud computing. According to users' needs, they can extend virtual machine's functions by themselves, so virtual machine tool can be used widely with machining systems and reduces costs. Due to cloud computing, it can save large memory space in computer, and virtual machine tool can also be used to simulate machining of multi-axis machines in large scales production line.
The International Journal of Advanced Manufacturing Technology, 2016
With recent advances in five-axis milling technology, feedrate optimization methods have shown si... more With recent advances in five-axis milling technology, feedrate optimization methods have shown significant effects in regard to enhancing milling productivity, especially when machining complex surface parts. The existing study is aimed at calculating the optimal feedrate values through modeling milling processes. However, due to the complexity of five-axis milling processes, optimization efficiency is the bottleneck of applying them in practice. This paper proposes a novel milling process optimization method based on hybrid forward-reverse mappings (HFRM) of artificial neural networks. The feedrate values are directly used as the outputs of network mappings. Three kinds of artificial neural networks are compared to determine the one with the highest accuracy and the best training efficiency. The study shows that with the collected datasets, the trained Levenberg-Marquardt back-propagation network (LMBPN) could predict feedrate values more precisely than other alternatives. Compared with previous methods, this HFRM-based optimization method is more adept in the area of parameter adjustment because as it has the advantages of high precision and much less calculation time. Combining other multiple milling constraints, an optimization system is developed for five-axis milling processes. The optimized results could be directly used to modify a cutter location (CL) file. A typical milling case was provided to verify the optimization performance of this method, which was found to be effective and reliable.
Correction activities (CAs), which can take the form of redesign, rework, or repair, are essentia... more Correction activities (CAs), which can take the form of redesign, rework, or repair, are essential to system development. Whereas verification activities (VAs) provide information about the state of the system, CAs modify the state of the system to facilitate its correct operation. However, existing approaches to modeling and optimizing verification strategies take a simplistic approach to CAs. Specifically, CAs are modeled as an expected cost to achieve a desired confidence level after a VA has failed and are inherent to such VAs. In this paper, we present a modeling paradigm based on Bayesian networks (BNs) that captures the effects of different types of CAs. This modeling paradigm allows for the integration of verification and correction decisions (CDs) under a common framework. The modeling paradigm is illustrated in the notional case of a communication system.
System verification activities (VAs) are used to identify potential errors and corrective activit... more System verification activities (VAs) are used to identify potential errors and corrective activities (CAs) are used to eliminate those errors. However, existing math-based methods to plan verification strategies do not consider decisions to implement VAs and perform CA jointly, ignoring their close interrelationship. In this article, we present a joint verification–correction model to find optimal joint verification–correction strategies (JVCSs). The model is constructed so that both VAs and CAs can be chosen as dedicated decisions with their own activity spaces. We adopt the belief model of Bayesian networks to represent the impact of VAs and CAs on verification planning and use three value factors to measure the performance of JVCSs. Moreover, we propose an order-based backward induction approach to solve for the optimal JVCS by updating all verification state values. A case study was conducted to show that our model can be applied to effectively solve the verification planning problem.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022
Verification is a critical process in the development of engineered systems. Through verification... more Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system’s development and verification activities (VAs) are executed as the development progresses. Such an approach appears to give inferior results as the selection of the VAs does not leverage information gained through the system’s development process. In contrast, a set-based design (SBD) approach to verification, where VAs are dynamically selected as the system’s development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate an exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument.
Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Vari... more Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Various diagnosis models, such as Bayesian networks (BNs), have been proposed to deal with this kind of problem in the past. However, the diagnosis results may not be reliable if second-order uncertainty is involved. This article proposes a hierarchical system diagnosis fusion framework that considers the uncertainty based on a belief model, called subjective logic (SL), which explicitly deals with uncertainty representing a lack of evidence. The proposed system diagnosis fusion framework consists of three steps: 1) individual subjective BNs (SBNs) are designed to represent the knowledge architectures of individual experts; 2) experts are clustered as expert groups according to their similarity; and 3) after inferring expert opinions from respective SBNs, the one opinion fusion method was used to combine all opinions to reach a consensus based on the aggregated opinion for system diagnosis. Via extensive simulation experiments, we show that the proposed fusion framework, consisting of two operators, outperforms the state-of-the-art fusion operator counterparts and has stable performance under various scenarios. Our proposed fusion framework is promising for advancing state-of-the-art fault diagnosis of complex engineered systems.
In current practice, a verification strategy is defined at the beginning of an acquisition progra... more In current practice, a verification strategy is defined at the beginning of an acquisition program and is agreed upon by customer and contractor at contract signature. Hence, the resources necessary to execute verification activities at various stages of the system development are allocated and committed at the beginning, when a small amount of knowledge about the system is available. However, contractually committing to a fixed verification strategy at the beginning of an acquisition program fundamentally leads to suboptimal acquisition performance. Essentially, the uncertain nature of system development will make verification activities that were not previously planned necessary, and will make some of the planned ones unnecessary. In order to cope with these challenges, this paper presents an approach to apply set-based design to the design of verification activities to enable the execution of dynamic contracts for verification strategies, ultimately resulting in more valuable verification strategies than current practice.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
Verification planning is a sequential decision-making problem that specifies a set of verificatio... more Verification planning is a sequential decision-making problem that specifies a set of verification activities (VA) and correction activities (CA) at different phases of system development. While VAs are used to identify errors and defects, CAs also play important roles in system verification as they correct the identified errors and defects. However, current planning methods only consider VAs as decision choices. Because VAs and CAs have different activity spaces, planning a joint verificationcorrection strategy (JVCS) is still challenging, especially for large-size systems. Here we introduce a UCB-based tree search approach to search for near-optimal JVCSs. First, verification planning is simplified as repeatable bandit problems and an upper confidence bound rule for repeatable bandits (UCBRB) is presented with the optimal regret bound. Next, a tree search algorithm is proposed to search for feasible JVCSs. A tree-based ensemble learning model is also used to extend the tree search algorithm to handle local optimality issues. The proposed approach is evaluated on the notional case of a communication system. Keywords verification planning • Bayesian network • multi-armed bandit problem • correction activity • tree search • random forest
System verification is a critical process in the development of engineered systems. Engineers gai... more System verification is a critical process in the development of engineered systems. Engineers gain confidence in the correct functionality of the system before it is deployed into operation by executing verification activities. Choosing the right set of verification activities at the right system development stage, that is, designing a verification strategy (VS), is essential to balancing information discovery and verification cost. Only recently, quantitative methods have been proposed to support the design of verification strategies. However, their applicability in real-life scenarios is impractical due to their limited computational efficiency in the high dimensional solution space of the VS selection problem. This paper presents a reinforcement learning (RL) approach to search for a near-optimal VS. Specifically, the VS design problem is formulated as a Markov decision process (MDP) in which a value function is required. Then we combine tree search and a neural network (NN) to design a RL algorithm. In the RL algorithm, the value function is approximated as a NN that is trained in an iterative way. The near-optimal VS can be generated from the trained NN. A case study is presented to show the superiority of the proposed method.
2014 IEEE International Conference on Automation Science and Engineering (CASE), 2014
For complex multi-axis or compound machine tools, checking machining processes by simulation soft... more For complex multi-axis or compound machine tools, checking machining processes by simulation software is needed. This paper aims to develop open virtual machine tool (VMT) for cloud computing. According to users' needs, they can extend virtual machine's functions by themselves, so virtual machine tool can be used widely with machining systems and reduces costs. Due to cloud computing, it can save large memory space in computer, and virtual machine tool can also be used to simulate machining of multi-axis machines in large scales production line.
The International Journal of Advanced Manufacturing Technology, 2016
With recent advances in five-axis milling technology, feedrate optimization methods have shown si... more With recent advances in five-axis milling technology, feedrate optimization methods have shown significant effects in regard to enhancing milling productivity, especially when machining complex surface parts. The existing study is aimed at calculating the optimal feedrate values through modeling milling processes. However, due to the complexity of five-axis milling processes, optimization efficiency is the bottleneck of applying them in practice. This paper proposes a novel milling process optimization method based on hybrid forward-reverse mappings (HFRM) of artificial neural networks. The feedrate values are directly used as the outputs of network mappings. Three kinds of artificial neural networks are compared to determine the one with the highest accuracy and the best training efficiency. The study shows that with the collected datasets, the trained Levenberg-Marquardt back-propagation network (LMBPN) could predict feedrate values more precisely than other alternatives. Compared with previous methods, this HFRM-based optimization method is more adept in the area of parameter adjustment because as it has the advantages of high precision and much less calculation time. Combining other multiple milling constraints, an optimization system is developed for five-axis milling processes. The optimized results could be directly used to modify a cutter location (CL) file. A typical milling case was provided to verify the optimization performance of this method, which was found to be effective and reliable.
Correction activities (CAs), which can take the form of redesign, rework, or repair, are essentia... more Correction activities (CAs), which can take the form of redesign, rework, or repair, are essential to system development. Whereas verification activities (VAs) provide information about the state of the system, CAs modify the state of the system to facilitate its correct operation. However, existing approaches to modeling and optimizing verification strategies take a simplistic approach to CAs. Specifically, CAs are modeled as an expected cost to achieve a desired confidence level after a VA has failed and are inherent to such VAs. In this paper, we present a modeling paradigm based on Bayesian networks (BNs) that captures the effects of different types of CAs. This modeling paradigm allows for the integration of verification and correction decisions (CDs) under a common framework. The modeling paradigm is illustrated in the notional case of a communication system.
System verification activities (VAs) are used to identify potential errors and corrective activit... more System verification activities (VAs) are used to identify potential errors and corrective activities (CAs) are used to eliminate those errors. However, existing math-based methods to plan verification strategies do not consider decisions to implement VAs and perform CA jointly, ignoring their close interrelationship. In this article, we present a joint verification–correction model to find optimal joint verification–correction strategies (JVCSs). The model is constructed so that both VAs and CAs can be chosen as dedicated decisions with their own activity spaces. We adopt the belief model of Bayesian networks to represent the impact of VAs and CAs on verification planning and use three value factors to measure the performance of JVCSs. Moreover, we propose an order-based backward induction approach to solve for the optimal JVCS by updating all verification state values. A case study was conducted to show that our model can be applied to effectively solve the verification planning problem.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022
Verification is a critical process in the development of engineered systems. Through verification... more Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system’s development and verification activities (VAs) are executed as the development progresses. Such an approach appears to give inferior results as the selection of the VAs does not leverage information gained through the system’s development process. In contrast, a set-based design (SBD) approach to verification, where VAs are dynamically selected as the system’s development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate an exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument.
Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Vari... more Fault diagnosis plays a critical role in maintaining and troubleshooting engineered systems. Various diagnosis models, such as Bayesian networks (BNs), have been proposed to deal with this kind of problem in the past. However, the diagnosis results may not be reliable if second-order uncertainty is involved. This article proposes a hierarchical system diagnosis fusion framework that considers the uncertainty based on a belief model, called subjective logic (SL), which explicitly deals with uncertainty representing a lack of evidence. The proposed system diagnosis fusion framework consists of three steps: 1) individual subjective BNs (SBNs) are designed to represent the knowledge architectures of individual experts; 2) experts are clustered as expert groups according to their similarity; and 3) after inferring expert opinions from respective SBNs, the one opinion fusion method was used to combine all opinions to reach a consensus based on the aggregated opinion for system diagnosis. Via extensive simulation experiments, we show that the proposed fusion framework, consisting of two operators, outperforms the state-of-the-art fusion operator counterparts and has stable performance under various scenarios. Our proposed fusion framework is promising for advancing state-of-the-art fault diagnosis of complex engineered systems.
In current practice, a verification strategy is defined at the beginning of an acquisition progra... more In current practice, a verification strategy is defined at the beginning of an acquisition program and is agreed upon by customer and contractor at contract signature. Hence, the resources necessary to execute verification activities at various stages of the system development are allocated and committed at the beginning, when a small amount of knowledge about the system is available. However, contractually committing to a fixed verification strategy at the beginning of an acquisition program fundamentally leads to suboptimal acquisition performance. Essentially, the uncertain nature of system development will make verification activities that were not previously planned necessary, and will make some of the planned ones unnecessary. In order to cope with these challenges, this paper presents an approach to apply set-based design to the design of verification activities to enable the execution of dynamic contracts for verification strategies, ultimately resulting in more valuable verification strategies than current practice.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
Verification planning is a sequential decision-making problem that specifies a set of verificatio... more Verification planning is a sequential decision-making problem that specifies a set of verification activities (VA) and correction activities (CA) at different phases of system development. While VAs are used to identify errors and defects, CAs also play important roles in system verification as they correct the identified errors and defects. However, current planning methods only consider VAs as decision choices. Because VAs and CAs have different activity spaces, planning a joint verificationcorrection strategy (JVCS) is still challenging, especially for large-size systems. Here we introduce a UCB-based tree search approach to search for near-optimal JVCSs. First, verification planning is simplified as repeatable bandit problems and an upper confidence bound rule for repeatable bandits (UCBRB) is presented with the optimal regret bound. Next, a tree search algorithm is proposed to search for feasible JVCSs. A tree-based ensemble learning model is also used to extend the tree search algorithm to handle local optimality issues. The proposed approach is evaluated on the notional case of a communication system. Keywords verification planning • Bayesian network • multi-armed bandit problem • correction activity • tree search • random forest
System verification is a critical process in the development of engineered systems. Engineers gai... more System verification is a critical process in the development of engineered systems. Engineers gain confidence in the correct functionality of the system before it is deployed into operation by executing verification activities. Choosing the right set of verification activities at the right system development stage, that is, designing a verification strategy (VS), is essential to balancing information discovery and verification cost. Only recently, quantitative methods have been proposed to support the design of verification strategies. However, their applicability in real-life scenarios is impractical due to their limited computational efficiency in the high dimensional solution space of the VS selection problem. This paper presents a reinforcement learning (RL) approach to search for a near-optimal VS. Specifically, the VS design problem is formulated as a Markov decision process (MDP) in which a value function is required. Then we combine tree search and a neural network (NN) to design a RL algorithm. In the RL algorithm, the value function is approximated as a NN that is trained in an iterative way. The near-optimal VS can be generated from the trained NN. A case study is presented to show the superiority of the proposed method.
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