Fluidity Investigation of Pure Al and Al-Si Alloys

Fluidity tests of pure aluminum 1070 and Al-Si alloys with Si contents of up to 25% were conducted using a die cast machine equipped with a spiral die. The effects of the channel gap, die temperature, and injection speed on the fluidity were investigated. When the channel gap was small (0.5 mm), the flow length of the 1070 was minimized, and the fluidity increased monotonically at a gradual rate with increasing Si content. In contrast, larger gaps yielded convex fluidity–Si content curves. Additionally, heating the die had less of an influence on the fluidity of the 1070 than on that of the Al-Si alloy. These results are discussed in the context of the peeling of the solidification layer from the die based on the thicknesses of foils and strips cast by melt spinning and roll casting, respectively. At lower Si contents, heat shrinkage was greater and the latent heat was lower. When the heat shrinkage was greater, the solidification layer began to peel earlier, and the heat transfer between the solidification layer and the die became smaller. As a result, the fluidity of the 1070 was greatest when the channel gap was 0.8 mm.

Machine learning methods in finance

This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.

Microstructure and Mechanical Properties of Semi-Solid Die-Cast A356 Aluminum Alloy

The present work deals with the relationship between microstructure and mechanical properties of A356 aluminum alloy which was produced via thixocasting process under different casting conditions. Feedstock billets were heated to a target temperature to obtain a semi-solid slurry with the required solid fraction. Some billets were heated to a fully-melted condition. In order to obtain fine and spheroidized Al grains, some billets for the partially melting were compressed axially by 33% at a room temperature before heating. The completely-melted and partially-melted slurries were die-cast by using a die-cast machine, and hour glass-shaped rod-type tensile specimens and small-size plate-type tensile specimens were obtained. Small cubic specimens were also collected from the die-cast products for microstructural evaluation. They were polished, and etched by Weck’s reagent. The partially-melted specimen which was compressed before heating shows the spherical Al grains. But the grain of the strain-free partially-melted specimen exhibited complicated morphology. The fully-melted specimen shows the fine and dendrite structure.

Semisolid Casting of Product with Thin Fins by Using Al-25%Si

Low solid fraction simple rheocasting using Al-25%Si is proposed to cast a product with thin fins. The characteristics of this casting are presented. A conventional die-cast machine was used. The low solid fraction semisolid slurry was made in the sleeve of the die cast machine. Super cooling was used to reduce the casting temperature. The huge latent heat of the Si was utilized because of its excellent flow ability. A model of a heat sink, which was 0.5mm thick, 25mm high with 0.5 mm draft angle, was could be cast by the proposed process.

Relative Assessment of the Dynamic Behavior and Cutting Performance of a Bonded and a Cast-Iron Horizontal Milling Machine

The dynamic behavior and the cutting performance of a horizontal milling machine, the structure of which was fabricated by bonding, are compared with those of a machine of nominally identical specification but containing cast structural components. Theoretical work predicted the chatter performance of these two machines from vibration tests, using dynamic cutting coefficients characterizing material behavior. The relative performance of the two machines is discussed with the aid of the predicted stability charts, relating to specific cutting conditions, and in a more general form, through merit charts. The stability charts were obtained also by cutting tests, carried out with a helix and a slot cutter. Correspondence between the predicted and measured stability charts was satisfactory. The chatter performance, measured in terms of the maximum stable width of cut, of the bonded machine was substantially better than that of the cast machine for both types of cutters.