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EN
Purpose: Aluminum-Silicon (Al-Si) alloys are commonly used in the automotive industry. At high Si levels they offer good wear resistance. Abrasive wear however, has been identified as the main insert cutter damage mechanism during High-Speed-Milling (HSM). This study investigates the effect of the tool cutter immersion on Al-Si bi-metallic materials in HSM operation. Design/methodology/approach: This study considers the effects of the tool cutter immersion on the resultant cutting forces, associated machined surface roughness, and machined subsurface microstructural damage caused by the tool cutter during the Minimum Quantity Lubricant – High Speed Milling (MQL-HSM) operation of AlSi bi-metallic materials with varying amounts and morphologies of the silicon phase. Findings: Experimental results indicate that a combination of gray cast iron with the W319 microstructure yields the greatest resistance to the tool cutter rake face during the face milling operation for all investigated tool cutter radial immersions. Machined surface roughness measurements reveal that surface roughness is a function of both the silicon content and morphology, as well as the percentage of tool cutter immersion. Matrix hardness measurements indicate that machining at all immersions has the same effect on compressing the matrix structure. Research limitations/implications: This study considers the effects of the radial tooling immersion and material selection while the speed, feed, and axial depth-of-cut are kept constant. Future work should address variability in the machining parameters in an attempt to maximize tool life, while optimizing the machined surface quality. Practical implications: Material selection affects the machining conditions in HSM of Al-Si bi-metallic materials. As a result careful consideration should be given when tailoring the machining conditions to the cast microstructures. Originality/value: North American automakers rely heavily on Al-Si precision sand cast components. As a result bi-metallic machining has to be often addressed during the face milling of engine blocks and cylinder heads. The research conducted here broadens the understanding of the impact of radial immersion on the machining behavior of Al-Si bi-metallic materials.
2
Content available remote Automata Recognizing No Words : A Statistical Approach
EN
How likely is that a randomly given (non-) deterministic finite automaton recognizes no word? A quick reflection seems to indicate that not too many finite automata accept no word; but, can this intuition be confirmed? In this paper we offer a statistical approach which allows us to conclude that for automata, with a large enough number of states, the probability that a given (non-) deterministic finite automaton recognizes no word is close to zero. More precisely, we will show, with a high degree of accuracy (i.e., with precision higher than 99% and level of confidence 0.9973), that for both deterministic and non-deterministic finite automata: a) the probability that an automaton recognizes no word tends to zero when the number of states and the number of letters in the alphabet tend to infinity, b) if the number of states is fixed and rather small, then even if the number of letters of the alphabet of the automaton tends to infinity, the probability is strictly positive. The result a) is obtained via a statistical analysis; for b) we use a combinatorial and statistical analysis. The present analysis shows that for all practical purposes the fraction of automata recognizing no words tends to zero when the number of states and the number of letters in the alphabet grow indefinitely. From a theoretical point of view, the result can motivate the search for ``certitude'', that is, a proof of the fact established here in probabilistic terms. In the last section we critically discuss the result and the method used in this paper.
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