Reliability-based performance indicator for a manufacturing network with multiple production lines in parallel

This paper addresses the system reliability evaluation for a manufacturing system with multiple production lines in parallel, where the system reliability is the possibility that a manufacturing system can satisfy demand. In the manufacturing system, the capacity of each machine is stochastic (i.e., multi-state) due to failure, partial failure, or maintenance. Thus, the manufacturing system can be constructed as a manufacturing network in terms of stochastic-flow network model. With considering reworking actions, we propose a graphical-based methodology to transform the manufacturing system into a manufacturing network. Thereafter, the manufacturing network is decomposed into general manufacturing paths and reworking paths. A simple algorithm is subsequently developed to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability is derived in terms of such capacity vectors afterwards.     

A modification of the Jones–Harris method for deep-groove ball bearings

This study presents a modification of the Jones–Harris method (JHM) for the determination of deflection in deep-groove ball bearings. The finite element method (FEM) and curve fitting have been utilized to modify the load–deflection relationships of Hertz contact formulas in JHM. Several cases of deep-groove bearings are simulated to determine contact deflection. Results obtained from the modified JHM (MJHM) are more accurate than the JHM results demonstrated by the comparison between FEM and experimental results.     

Reliability with finite buffer size for a multistate manufacturing system with parallel production lines

Abstract

This study examines system reliability for a manufacturing system with parallel production lines by creating a model of a multistate manufacturing system (MMS). System reliability is defined as the probability of demand satisfaction, which reflects the probability that the current capacity state of the MMS can successfully process a given demand. In particular, a buffer station with a finite size is taken into consideration to avoid blockage and starvation in the MMS. To the best of our knowledge, there is no existing research that considers a finite buffer size in a model of an MMS with parallel production lines. This study proceeds through the following phases. (i) In the model construction phase, the amount of input material, workload, and minimal capacity required by each workstation to satisfy a given demand level are studied by flow analysis; subsequently, a buffer usage matrix (BUM) is proposed to calculate buffer reliability. (ii) In the performance evaluation phase, system reliabilities with both infinite and finite buffer sizes are derived in terms of both minimal capacity vector and buffer reliability. (iii) In the case demonstration phase, two examples are utilized to illustrate the proposed method. Results of both examples show that the system reliability is overestimated with an infinite buffer size.     

A Wide Tuning Range Voltage Controlled Oscillator Using Common-Base Configuration and Inductive Feedback

Usually the tuning range of voltage controlled oscillator (VCO) is much narrower than the possible resonant frequency range of the tank at different tuning voltages. In this letter we analyze the traditional common-base type VCO, give a simple but useful insight about the mechanism behind the topology, and verified the conclusions with a 2 mum heterojunction bipolar transistor (HBT) VCO. The results show that with the CB configuration, proper feedback inductor and wide tuning range varactor, the tuning range can be almost the same as the resonant frequency range of the tank at different tuning voltages. A wideband voltage controlled oscillator using a commercial 2 mum HBT technology is designed, fabricated and measured. The varactor with wide range of capacitance is used to achieve 53.33% measured tuning range from 9.46 to 16.34 GHz. The measured phase noise at 1 MHz offset is between -90 and -102 dBc/Hz. The total chip size is 1 mm² including all testing pads, while the core area is 0.64 mm². The VCO is suitable for wideband application such as in measurement equipment or astronomical exploring telescopes.     

A Low-Power 114-GHz Push–Push CMOS VCO Using LC Source Degeneration

An LC source-degeneration negative-resistance cell of an LC VCO is investigated, which is capable of operating at millimeter-wave (MMW) range with low dc power consumption. Several negative-resistance cells in LC oscillators are also compared. The LC source-degenerated topology is demonstrated through a 114-GHz push-push fully integrated LC VCO implemented in TSMC 0.13-mum CMOS process. With core power consumption of 8.4 mW, the tuning range at the fundamental port is 56.4-57.6 GHz and at the push-push port is 112.8-115.2 GHz. The measured phase noise at the fundamental port is -13.6 dBc/Hz at 10-MHz offset. This VCO is believed to have the best figure of merit among MMW VCOs using bulk CMOS processes.     

FuzzyTree crossover for multi-valued stock valuation

Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except for the vanilla subtree crossover. This study generalizes the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of single-valued and buy-and-hold strategies.     

Fuzzy-based system reliability of a labour-intensive manufacturing network with repair

This paper presents a fuzzy-based assessment model to evaluate system reliability of a labour-intensive manufacturing system with repair actions. Due to the uncertainty in human performance, labour-intensive manufacturing systems must determine the capacity of each labourer in order to accurately characterise the performance of the systems. Therefore, we model such a manufacturing system as a fuzzy multi-state network in order to characterise the labourers’ influence on workstation performance. First, the workstation reliability is defined according to the loading state by three fuzzy membership functions, namely ‘under loading’, ‘normal loading’ and ‘over loading’, respectively. The system reliability is subsequently evaluated with fuzzy intersection operations in terms of these workstation reliabilities. Thus, the system reliability is defined as a fuzzy membership function to assess whether the manufacturing system performance is sufficient to satisfy the demand reliably. A case study of a footwear manufacturing system is illustrated to explain the proposed model. Furthermore, we apply the proposed model to a non-labour-intensive manufacturing network in order to validate the applicability to this class of systems.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

Maintenance reliability estimation for a cloud computing network with nodes failure

The cloud computing network (CCN) has become a new paradigm for the business and clients as the development of information technology. To guarantee the CCN keep a good quality of service (QoS), the maintenance action is necessary when the CCN falls to a specific state such that it cannot afford enough capacity to meet demand d. In the CCN, edges and nodes have various capacities due to failure, partial failure, or maintenance; thus, the CCN has several possible states. This paper proposes an algorithm to estimate the performance of a CCN under maintenance budget with nodes failure. Hence, the maintenance reliability is developed to measure the capability that the CCN can send d units of data from the cloud to the client through multiple paths under the maintenance budget and time constraints. Furthermore, the system supervisor can conduct the sensitive analysis to improve/investigate the most important part in a large CCN afterwards.