Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach

Supply chain management allows modern enterprises to relax their own capacities and produce in a more flexible manner for diversified consumer demands. However, for an enterprise with divergent supply chain (DSC) and multiple product lines, to plan the production allocation for higher competitive advantage in the risky global market is a challenging problem. The existing literature still has not address this problem very well. This paper is aimed to treat this problem by using an integrated approach of activity based costing (ABC) and management, five forces analysis, risk and value-at-risk analysis, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy goal programming (FGP). The proposed model can effectively incorporate the key factors of precise costing, managerial constraints, competitive advantage analysis, and risk management into DSC forecasting and multi-objective production planning. A case study of a consumer-oriented cell phone DSC is also presented. The sensitivity analysis shows that identifying and relaxing crucial constraints can play an important role in DSC planning for higher competitive advantage and lower risk.     

An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making

The fuzzy weighted average (FWA), which is a function of fuzzy numbers and is useful as an aggregation method in engineering or management science based on fuzzy sets theory. It provides a discrete approximate solution by α-cuts level representation of fuzzy sets and interval analysis. Since the FWA method has an exponential complexity, thus several researches have focused on reducing this complexity. This paper also presents an enhanced fuzzy weighted average approach to achieve the objective of reducing the complexity. This proposed approach is through an improved initial solution for original FWA algorithm, and a two-phase concept by extending and applying both the algorithms of Chang et al. [4] and Guu [14]. Although the complexity of the proposed FWA algorithm is O(n) the same as Guu [14] which is the best level achieved to date. But from the experimental results appear that the proposed algorithm is more efficient, which only needs a few evaluated numbers and spend much less overall CPU time than Guu [14] and other FWA algorithms. In order to demonstrate the usefulness of this study, a practical example for unmanned aerial vehicle (UAV) selecting under military requirement has illustrated. Additionally, a computer-based interface, which helps the decision maker make decisions more efficiently, has been developed.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

Embedding fault-free cycles in crossed cubes with conditional link faults

The crossed cube, which is a variation of the hypercube, possesses some properties that are superior to those of the hypercube. In this paper, we show that with the assumption of each node incident with at least two fault-free links, an n-dimensional crossed cube with up to 2n−5 link faults can embed, with dilation one, fault-free cycles of lengths ranging from 4 to 2 ⁿ . The assumption is meaningful, for its occurrence probability is very close to 1, and the result is optimal with respect to the number of link faults tolerated. Consequently, it is very probable that algorithms executable on rings of lengths ranging from 4 to 2 ⁿ can be applied to an n-dimensional crossed cube with up to 2n−5 link faults.

Optimal assembly plan generation: a simplifying approach

The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept, the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly sequence modeling allows to handle more complex products with a large number of parts.     

A fuzzy GARCH model applied to stock market scenario using a genetic algorithm

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model.The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.     

Using support vector machine with a hybrid feature selection method to the stock trend prediction

In this paper, we developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method to predict the trend of stock markets. This proposed hybrid feature selection method, named F-score and Supported Sequential Forward Search (F_SSFS), combines the advantages of filter methods and wrapper methods to select the optimal feature subset from original feature set. To evaluate the prediction accuracy of this SVM-based model combined with F_SSFS, we compare its performance with back-propagation neural network (BPNN) along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty, and Correlation-based feature selection via paired t-test. The grid-search technique using 5-fold cross-validation is used to find out the best parameter value of kernel function of SVM. In this study, we show that SVM outperforms BPN to the problem of stock trend prediction. In addition, our experimental results show that the proposed SVM-based model combined with F_SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that SVM combined with F_SSFS can serve as a promising addition to the existing stock trend prediction methods.     

Novel in-line WAT processing of 55 nm CIS product and effect of process waiting time on void growth at interface of SiCN with copper capping layer

The WAT (wafer acceptance test) is the last examination that is performed before a wafer or a chip fab out to ensure the quality and stability of chip performance. In 55 nm CIS (CMOS Image Sensor) technology, a highly smooth wafer surface is critical for the BSI (backside illumination) process. The traditional WAT process cannot be used; rather the in-line WAT must be performed during the process for forming copper interconnect. However, increasing the processing time increases the period of exposure of the copper interconnect to air, which is called the Q-time, affecting the reliability of copper interconnect. Nitrogen-doped silicon carbide (also called NDC or SiCN) has been used to fabricate copper diffusion barrier films. PECVD SiCN dielectric has a promisingly low dielectric constant for use as a copper diffusion barrier. Copper diffusion barrier films comprise one or more layers of silicon carbide. Covering a copper layer with a single thin NDC pre-layer significantly increases the maximum allowable Q-time for wafer probing. However, after the Q-time, a void forms between NDC layer and the NDC pre-layer. This work proposes a new two-step NDC process and the optimization of the thickness of the NDC pre-layer. The process has the advantages of providing a high stability for parametric test and a long allowable Q-time. These advantages are achieved by changing the thickness of the NDC pre-layer. This new approach has been analyzed using TEM and by performing parametric tests, and the feasibility has been confirmed experimentally. No void is formed between the NDC layers and a high test stability is achieved when the thickness of the NDC pre-layer is 120 Å.