A virtual factory based approach to on-line simulation and scheduling for an FMS and a case study

The recent years have witnessed a revolution in computing capabilities. At the same time, modern manufacturing systems are becoming increasingly complex and capital-intensive. Here, we propose a virtual factory wherein an efficient prototyping testbed will be provided. It will be possible to develop detailed models to support system design and operation, to test different system configurations. Besides, It will be possible to manipulate all system features which impact performance, and to see the results of these manipulations, all without disrupting the actual system. This paper is one among very few that tries to represent the virtual factory in an analytic form so that many existing mathematical analyses can be applied. New pseudo resources can be added to form a new virtual environment, and control policy designed by engineers will be evaluated before being issued. Since a state-transition sequence of the virtual factory from the initial state to the final state can be seen as a schedule of the modeled system. An effective schedule of the processing can be obtained by using an A* based search algorithm, namely, Limited-Expansion A algorithm.     

Dynamic performance measures for tools with multi-state wear processes and their applications for tool design and selection

Traditional models to evaluate the reliability and performance of tools are binary models, working (success) or failure, to classify the state of the tool. Most machine tools degrade with time and thus a multi-state discrete classification is more realistic for the continuous degradation of the tool. We propose a non-homogeneous continuous-time Markov process model for tool degradation, because the length of time the machine tool stays in a certain state depends not only on the current state, but also on how long the tool has been in the current state. The traditional reliability and life performance measures focus on the mean time between failure or the failure rate. The performance measures must capture the total experience of the manufacturer over the target life of the tool and the impact of its degradation on the quality of the products to the downstream customers. We propose several new measures for tool performance. These measures can be used to evaluate different tool designs or we can use them to select the best tool for a certain application based on the economic/disutility functions.     

Measuring the manufacturing process yield based on fuzzy data

The process yield is the most basic and common criterion used in the manufacturing industry as the basis for measuring process performance. In the conventional case, the underlying data for a manufacturing process are obtained from the output responses of continuous quantities that are always assumed to be real numbers. However, measurement of the output process occasionally appears to be imprecise in practical situations. Accordingly, the output responses should be assumed to be so-called fuzzy data. We propose a constructive methodology to obtain the fuzzy estimate of the yield index S _pk with the help of the extension principle of fuzzy sets theory. This study, based on an analytical approach, is an advancement over existing technology in the area of process capability analysis that is easy to implement in plant applications.     

Effect of adding Sb on microstructure and adhesive strength of Sn-Ag solder joints

This study investigates the influence of adding Sb on the microstructure and adhesive strength of the Sn3.5Ag solder. Both solidus and liquidus temperatures increase as Sb additions increase. Adding 1.5wt.%Sb leads to the narrowest range (6.6°C) between the solidus and liquidus temperature of the solder. Adding Sb decomposes the as-soldered ringlike microstructure of Sn3.5Ag and causes solid-solution hardening. The as-soldered hardness increases with increasing Sb addition. For long-term storage, adding Sb reduces the size of the rodlike Ag₃Sn compounds. The hardness also increases with increasing Sb addition. Adding Sb depresses the growth rate of interfacial intermetallic compounds (IMCs) layers, but the difference between 1% and 2% Sb is not distinct. For mechanical concern, adding Sb improves both adhesive strength and thermal resistance of Sn3.5Ag, where 1.5% Sb has the best result. However, adding Sb causes a variation in adhesive strength during thermal storage. The more Sb is added, the higher the variation reveals, and the shorter the storage time requires. This strength variation helps the solder joints to resist thermal storage.     

Measuring manufacturing capability based on lower confidence bounds of C pmk applied to current transmitter process

Several process capability indices, including C_p, C_pk, and C_pm, have been proposed to provide numerical measures on manufacturing potential and actual performance. Combining the advantages of those indices, a more advanced index C_pmk is proposed, taking the process variation, centre of the specification tolerance, and the proximity to the target value into account, which has been shown to be a useful capability index for manufacturing processes with two-sided specification limits. In this paper, we consider the estimation of C_pmk, and we develop an efficient algorithm to compute the lower confidence bounds on C_pmk based on the estimation, which presents a measure on the minimum manufacturing capability of the process based on the sample data. We also provide tables for practitioners to use in measuring their processes. A real-world example of current transmitters taken from a microelectronics device manufacturing process is investigated to illustrate the applicability of the proposed approach. Our implementation of the existing statistical theory for manufacturing capability assessment bridges the gap between the theoretical development and the in-plant applications.     

Transmission-type angle deviation microscopy

We present a new microscopy technique that we call transmission angle deviation microscopy (TADM). It is based on common-path heterodyne interferometry and geometrical optics. An ultrahigh sensitivity surface plasmon resonance (SPR) angular sensor is used to expand dynamic measurement ranges and to improve the axial resolution in three-dimensional optical microscopy. When transmitted light is incident upon a specimen, the beam converges or diverges because of refractive and/or surface height variations. Advantages include high axial resolution (approximately 32 nm), nondestructive and noncontact measurement, and larger measurement ranges (+/- 80 microm) for a numerical aperture of 0.21 in a transparent measurement medium. The technique can be used without conductivity and pretreatment.     

The effect of surfactants on the distribution of organic compounds in the soil solid/water system

The efficiency of soil remediation by surfactant washing was evaluated via the measured distribution coefficients of a number of nonpolar compounds in several soil-water mixtures. The studied compounds (contaminants) are BTEX (benzene, toluene, ethylbenzene, and p-xylene) and three chlorinated pesticides (lindane, alpha-BHC, and heptachlor epoxide), which span several orders of magnitude in water solubility (S(w)). A peat, and two natural soils were used that comprise a wide range in soil organic matter (SOM) content. The surfactants tested included cationic, anionic and nonionic types, with concentrations up to five to six times the critical micelle concentration (CMC). The K(d)(*)/K(d), values were used to evaluate the remediation efficiency under various operation conditions. For relatively water soluble BTEX compounds, the surfactant adsorption on the soil surface is the deciding factor on contaminant desorption from soil. For the less-soluble pesticides, surfactant micelles in solution influence the contaminant desorption more. The contaminants partitioning to SOM or adsorbed surfactants lowers the desorption efficiency. Anionic surfactants are found to be a better choice on soil remediation because they do not form admicelle on soil surface that enhances the SOM content. Cationic surfactant, which adsorb onto soil surfaces, leads to poor remediation efficiency. An improper selection of surfactant would result in inefficiency in soil remediation by surfactant washing.     

Fault-tree analysis for liquefied natural gas terminal emergency shutdown system

Natural gas, one of the cleanest, most efficient and useful of all energy sources, is a vital component of the world’s supply of energy. To make natural gas more convenient for storage and transportation, it is refined and condensed into a liquid called liquefied natural gas (LNG). In a LNG site, safety is a long-team and critical issue. The emergency shutdown (ESD) system in the LNG receiving terminal is used to automatically stop the pumps and isolate the leakage section. Fault-tree analysis (FTA) has been widely used for providing logical functional relationships among subsystems and components of a system and identifying the root causes of the undesired failures in a system. In the conventional FTA for the ESD system, we usually assume that exact failure probabilities of events are collected. However, in most real applications, first, the FTA for the ESD system needs to be made at a early design or manufacturing stage, certain new components normally used without failure data; secondly, sometimes the environmental change in the system during the operation periods. This makes more difficult to gather past exact failures data for the FTA. To complete the FTA of the ESD system under these uncertain situations, we apply the intuitionistic fuzzy sets (IFS) theory to the FTA. We generate the intuitionistic fuzzy fault-tree interval, and the intuitionistic fuzzy reliability interval for the ESD system. We also present an algorithm to find the critical components in the system based on IFS–FTA and determine weak paths in the ESD system, where the key improvement must be made.     

Quantum hydrodynamic simulation of discrete-dopant fluctuated physical quantities in nanoscale FinFET

Impact of the discrete dopants on device performance is crucial in determining the behavior of nanoscale semiconductor devices. Atomistic quantum mechanical device simulation for studying the effect of discrete dopants on device's physical quantities is urgent. This work explores the physics of discrete-dopant-induced characteristic fluctuations in 16-nm fin-typed field effect transistor (FinFET) devices. Discrete dopants are statistically positioned in the three-dimensional channel region to examine associated carrier's characteristic, concurrently capturing “dopant concentration variation” and “dopant position fluctuation”. An experimentally validated quantum hydrodynamic device simulation was conducted to investigate the potential profile and threshold voltage fluctuations of the 16-nm FinFET. Results of this study provide further insight into the problem of fluctuation and the mechanism of immunity against fluctuation in 16-nm devices.