Nitrogen plasma source ion implantation of AISI S1 tool steel

Significant economic savings can be achieved by improving the wear lifetime of precision manufacturing tools through nitrogen-ion implantation. This near-ambient temperature, surface modification process preserves dimensional integrity and surface finish while eliminating delamination problems that are often associated with overlay coatings. Conventional ion implantation is a line-of-sight process which requires elaborate manipulation and masking to uniformly implant components of complex shapes. A recently developed process, plasma source ion implantation (PSII), circumvents this line-of-sight restriction and makes ion implantation more attractive economically. In this article, the effects of PSII of nitrogen at a target bias of 50 kV, to a dose of 0.3 × 10¹⁸ atoms/cm² on the surface microstructure and mechanicalproperties of AISI S1 tool steel are presented.     

Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study

This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence dependent. A discrete event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop load, setup time ratios and due date tightness. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures.     

Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system

This paper presents the details of a simulation study carried out for analyzing the impact of scheduling rules that control part launching and tool request selection decisions of a flexible manufacturing system (FMS) operating under tool movement along with part movement policy. Two different scenarios have been investigated with respect to the operation of FMS. In scenario 1, the facilities such as machines, tool transporter and part transporter are assumed to be continuously available without breakdowns, whereas in scenario 2, these facilities are prone to failures. For each of these scenarios, a discrete-event simulation model is developed for the purpose of experimentation. A number of scheduling rules are incorporated in the simulation models for the part launching and tool request selection decisions. The performance measures evaluated are mean flow time, mean tardiness, mean waiting time for tool and percentage of tardy parts. The results obtained through the simulation have been statistically analyzed. The best possible scheduling rule combinations for part launching and tool request selection have been identified for the chosen FMS.     

A study on the effects of using short utterance length development data in the design of GPLDA speaker verification systems

This paper studies the performance degradation of Gaussian probabilistic linear discriminant analysis (GPLDA) speaker verification system, when only short-utterance data is used for speaker verification system development. Subsequently, a number of techniques, including utterance partitioning and source-normalised weighted linear discriminant analysis (SN-WLDA) projections are introduced to improve the speaker verification performance in such conditions. Experimental studies have found that when short utterance data is available for speaker verification development, GPLDA system overall achieves best performance with a lower number of universal background model (UBM) components. As a lower number of UBM components significantly reduces the computational complexity of speaker verification system, that is a useful observation. In limited session data conditions, we propose a simple utterance-partitioning technique, which when applied to the LDA-projected GPLDA system shows over 8% relative improvement on EER values over baseline system on NIST 2008 truncated 10–10 s conditions. We conjecture that this improvement arises from the apparent increase in the number of sessions arising from our partitioning technique and this helps to better model the GPLDA parameters. Further, partitioning SN-WLDA-projected GPLDA shows over 16% and 6% relative improvement on EER values over LDA-projected GPLDA systems respectively on NIST 2008 truncated 10–10 s interview-interview, and NIST 2010 truncated 10–10 s interview-interview and telephone-telephone conditions.     

Improving Speech Recognition Accuracy for Small Vocabulary Applications in Adverse Environments

The problem of improving the accuracy of small vocabulary isolated word speaker dependent speech recognition under adverse conditions such as factory environments is considered. A new approach to solving this problem, by using Output Probability Distributions (OPDs), is presented. OPDs improve the system performance by modelling inter-word relationships, something that a standard maximum likelihood (ML) technique fails to do. The system was tested using the TI46 database, corrupted with the NOISEX-92 database, as well as in a real-world factory environment, and achieved good results.     

Knowledge representation for mathematical discovery: Three experiments in graph theory

This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graphy theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factural knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.     

Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time

This paper deals with the problem of scheduling a flow shop operating in a sequence dependent setup time environment. The objective is to determine the sequence that minimises the makespan. Two efficient neighbourhood search-based heuristics have been developed and tested using 960 problems, and the results obtained reveal their usefulness. The algorithms make use of two existing constructive heuristics. A neighbourhood search known as variable neighbourhood descent is used to improve the two constructive heuristics. Experimentation is carried out on the 96 groups of problems with 10 problem instances in each group. Performance analysis is carried out using the relative performance improvement of each heuristic. The analysis shows a consistently better performance of the neighbourhood-based improvement heuristics. A paired comparison test is used for validating the superiority of the proposed heuristics. The statistical analysis reveals that the performance of the neighbourhood-based heuristics is very much dependent on the initial constructive heuristics used.     

Relationship between the coefficient of friction and the wear rate of metals

A very approximate mathematical analysis is given for the relationship between the coefficient of friction and the wear factor of metals for the case of sliding wear. The analysis is based on the delamination theory of wear, the empirically determined plastic strain field near the surface at the onset of delamination of a wear sheet and a constitutive relation which takes into account the effect of cyclic softening. An energy argument is used. The agreement between experimentally observed values and theoretically predicted values is good.     

Corrosion of Alloy Haynes 230 in High Temperature Supercritical Carbon Dioxide with Oxygen Impurity Additions

The corrosion of Ni-based alloy Haynes 230 in supercritical carbon dioxide at temperatures of 650 and 750 °C at a pressure of 20 MPa was investigated. In high-purity research grade CO₂, the corrosion performance of the alloy was excellent with a thin, uniform, protective chromium-rich oxide layer forming on the surface. Introduction of 10 and 100 ppm O₂ impurity in the CO₂ environment noticeably enhanced oxidation with evidence of oxide spallation and nodule formation. In these oxygen impurity added tests, increased oxidation led to subsurface voids due to the more rapid outward diffusion of chromium as well as intergranular alumina and chromia. The oxygen concentration at the inlet and the outlet of the autoclave was measured and used to support the results of characterization of the surface oxide to develop a more holistic understanding of the role of oxygen impurity on the corrosion process. In all cases, there some carbon was observed, which manifested as slightly higher concentration of chromium–carbide phase at the grain boundaries compared to the unexposed alloy.