A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

A Conjecture on Wiener Indices in Combinatorial Chemistry

Drugs and other chemical compounds are often modeled as polygonal shapes, where each vertex represents an atom of the molecule, and covalent bonds between atoms are represented by edges between the corresponding vertices. This polygonal shape derived from a chemical compound is often called its molecular graph, and can be a path, a tree, or in general a graph. An indicator defined over this molecular graph, the Wiener index, has been shown to be strongly correlated to various chemical properties of the compound. The Wiener index conjecture for trees states that for any integer n (except for a finite set), one can find a tree with Wiener index n. This conjecture has been open for quite some time, and many authors have presented incremental progress on this problem. In this paper we present further progress towards proving this conjecture—through the design of efficient algorithms, we show that enumerating all possible trees to verify this conjecture (as done by all the previous approaches) is not necessary, but instead searching in a small special family of trees suffices, thus achieving the first polynomial (in n) time algorithm to verify the conjecture up to integer n. More precisely, we (i) present an infinite family of trees and prove various properties of these trees, (ii) show that a large number of integers, up to at least 10⁸ (compared with the previous best 10⁴) are representable as Wiener indices of trees in this succinct family, (iii) provide several efficient algorithms for computing trees with given Wiener indices, and (iv) implement our algorithms and experimentally show that their performance is asymptotically much better than their theoretical worst-case upper bound.     

Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

Power Flattening in ARIES-RS Fusion Breeder Reactor Using Mixed Fuels

This study presents the possibility of the power flattening in the ARIES-RS breeder reactor using mixed (Th,U)C or (Th,U)N fuels. Two different types of mixing, namely, homogeneous mixing (HM) and linear mixing (LM) were used to investigate the uniformity of fission power distribution through the fuel zone. In HM, fraction of uranium content were kept constant in all rows of the fuel zone whereas, in LM the fraction of the uranium were linearly increased from the first to last fuel row in the fuel zone. Neutron transport calculations were performed with the aid of the SCALE4.3 system by solving the Boltzmann transport equation with the XSDRNPM code in 238 neutron groups and a S₈–P₃ approximation. Flat fission power distribution was maintained successfully for the blanket using linearly mixed fuels. However, the fission density profile was not uniform in the blanket with homogeneously mixed fuels. It decreased exponentially form the 1st to 10th fuel row.     

Dry wear and friction behaviour of plasma nitrided Ti–6AL–4 V alloy after explosive shock treatment

The unlubricated wear behaviour of explosive shock treated and, subsequently plasma nitrided Ti–6Al–4 V alloy was studied using a ball-on-disc wear tester. Plasma nitriding was carried out at three different temperatures (700, 800 and 900 °C) for 3, 6, 9 and 12 h. Plasma nitriding after explosive shock treatment enabled a reduction in the wear rate of two orders of magnitude. Detailed investigations of this improved wear performance dependent on the nitriding temperature and time were carried out. The friction and wear data showed a clear breakthrough transition from the nitrided layer to the core of the Ti–6Al–4 V alloy matrix. The lowest wear volume was obtained for the sample, nitrided at 900 °C for 12 h, especially at loads of 2.5, 5 and 7.5 N. Obviously, the hard nitride layers were intimately associated with low wear rate, providing a smooth low friction surface. The coefficient of friction reduced from 0.46 to 0.2 due to a thick and hard compound layer resulting from a high nitrogen diffusion rate caused by explosive shock treatment that expected to increase point defects in the alloy. Detailed examination of the wear tracks showed that plasma nitriding changes the mechanism of wear from one of adhesion for untreated Ti–6Al–4 V to both delamination and mild abrasive.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

Evaluation of damage to light structures erected on a fill material rich in expansive soil

The paper reports a study of the cause of defects in light structures and the toppling of a wall constructed on a fill material rich in Ankara clay. Laboratory tests were carried out on vertical and horizontal samples from boreholes and a trial pit was excavated near the damaged structures. The results showed that in the vicinity of the toppled wall, swelling pressures in the horizontal direction were greater than those measured in the vertical direction. The swelling properties of the fill material were higher than those of original Ankara clay as determined previously by other investigators, suggesting that breakdown of the cementing bonds and a change in the fabric are the main factors affecting the swelling pressure of disturbed and compacted expansive soils. The calculations to predict uplift showed a good agreement with the observations in the damaged structures. It is concluded that swelling was the main cause of the damage to the light structures at the study site and resulted from the highly expansive nature of the fill material, poor drainage, the semi-arid climate, poor construction methods and ineffective precautions. Some recommendations for minimizing the effects of swelling at the study site are briefly outlined.     

Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.     

Cobalt loaded organic acid modified kaolin clay for the enhanced catalytic activity of hydrogen release via hydrolysis of sodium borohydride

Inorganic acids such as hydrochloric acid (HCl), nitric acid (HNO₃) and sulphuric acid (H₂SO₄) are generally used in the acid modification of clays. Here, CoB catalyst was synthesized on the acetic acid-activated kaolin support material (CH₃COOH -kaolin- CoB) with an alternative approach. This prepared catalyst, firstly, was used to catalyze the hydrolysis of NaBH₄ (NaBH₄-HR). The structure of the raw kaolin, kaolin-CH₃COOH, and CH₃COOH-kaolin-CoB samples were characterized by X-ray diffraction spectroscopy (XRD), Fourier transforms infrared spectroscopy (FTIR), scanning electron microscope (SEM), and nitrogen adsorption. At the same time, this catalyst performance was examined by Co loading, NaBH₄ concentration, NaOH concentration, temperature and reusability parameters. The end times of this hydrolysis reaction using raw kaolin-CoB and CH₃COOH-kaolin-CoB were found to be approximately 140 and 245 min, respectively. The maximum hydrogen generation rates (HGRs) obtained at temperatures 30 °C and 50 °C were 1533 and 3400 mL/min/g_catalyst, respectively. At the same time, the activation energy was found to be 49.41 kJ/mol.