Micro structures, tensile properties and corrosion behavior of die-cast Mg-4Al-based alloys containing La and/or Ce

In order to study the properties of Mg-Al-RE (AE) series alloys, the Mg-4Al-4RE-0.4Mn (RE= La, Ce/La mischmetal or Ce) alloys were developed. Their microstructures, tensile properties and corrosion behavior have been investigated. The results show that the phase compositions of Mg-4Al-4La-0.4Mn alloy consist of alpha-Mg and Al11La3 phases. While two binary Al-RE (RE = Ce/La) phases, Al11RE3 and Al2RE, are formed in Mg-4Al-4Ce/La-0.4Mn alloy, and Al11Ce3 and Al2Ce are formed in Mg-4Al-4Ce-0.4Mn alloy.

Preparation and characterization of As-cast and hot-rolled Mg-3Al-0.5Mn-0.5Zn-1MM alloy

Mg-3Al-0.5Mn-0.5Zn-1MM alloy was prepared by metal mould casting method. The as-cast ingot was homogenized and then hot-rolled at 673 K with total thickness reduction of 65%. Microstructure and mechanical properties of the as-cast and hot-rolled samples were investigated. The results showed that the as-cast sample mainly consisted of alpha-Mg, beta-Mg17Al12, Al10Ce2Mn7, and Al11RE3 (RE = La and Ce) phases. The average grain size of the sample homogenized at 673 K was about 240 gm, and it was greatly refined to about 7 mu m by dynamic recrystallization for the hot-rolled sample.

Microstructure and mechanical properties of high performance Mg-Gd based alloys

The Mg-12Gd-4Y-2Nd-0.3Zn-0.6Zr (wt.%) alloy was prepared by casting technology, and the structure, age hardening behavior and mechanical properties of the alloy have been investigated. The results demonstrated that the alloy was composed of alpha-Mg matrix, a lot of dispersed Mg24RE5 (RE = Gd/Y/Nd) and Mg5RE precipitates in the as-cast and the T6 state alloys. The alloy exhibited remarkable age hardening response and excellent mechanical properties from room temperature (RT) to 300 degrees C by optimum solid solution and aging conditions. The ultimate tensile strength.

Microstructures and mechanical properties of as-cast Mg-5Y-3Nd-Zr-xGd (x=0, 2 and 4 wt.%) alloys

Mg-5Y-3Nd-0.6Zr-xGd (x = 0, 2 and 4 wt.%) alloys were prepared by metal mould casting technique, the structures and mechanical properties were investigated. The alloys were mainly composed of alpha-Mg solid solution and beta-phase. With increasing Gd content, Mg5RE phase increased and the grain was refined. The Mg-5Y-3Nd-2Gd-0.6Zr alloy exhibited highest ultimate tensile strength and Mg-5Y-3Nd-0.6Zr alloy showed highest yield strength at room temperature. With increasing amount of Gd, the thermal resistance was improved. The Mg-5Y-3Nd-4Gd-0.6Zr alloy exhibited highest UTS and YS at 250 degrees C, they were about 1.27 times higher than those of Gd-free alloy, which was mainly attributed to the increase of the beta-phase and Mg5RE strengthening phase.

Investigations of the properties of Mg-5Al-0.3Mn-xCe (x=0-3, wt.%) alloys

Mg-5Al-0.3Mn-xCe (x = 0-3, wt.%) alloys were prepared by metal mould casting method. The microstructures and mechanical properties were investigated. The results revealed that the main phases of as-cast Mg-5Al-0.3Mn alloy consist of alpha-Mg matrix and beta-Mg17Al12 phase. With the addition of Ce element, Al11Ce3 precipitates were formed and mainly aggregated along the grain boundaries. The amount of the Al11Ce3 precipitates increased with increasing addition of Ce, but the amount of beta-Mg17Al12 phase decreased. The highest tensile strength was obtained in Mg-5Al-0.3Mn-1.5Ce alloy. The ultimate tensile strength (UTS), yield strength (YS) and elongation at room temperature are 203 MPa, 88 MPa and 20%, separately.

Effect of substituting cerium-rich mischmetal with lanthanum on microstructure and mechanical properties of die-cast Mg-Al-RE alloys

Die-cast Mg-4Al-4RE-0.4Mn (RE = Ce-rich mischmetal) and Mg-4Al-4La-0.4Mn magnesium alloys were prepared successfully and their microstructure, tensile and creep properties have been investigated. The results show that two binary Al-RE phases, Al11RE3 and Al2RE, are formed along grain boundaries in Mg-4Al-4RE-0.4Mn alloy, while the phase compositions of Mg-4Al-4La-0.4Mn alloy mainly consist of alpha-Mg phase and Al11La3 phase. And in Mg-4Al-4La-0.4Mn alloy the Al11La3 phase occupies a large grain boundary area and grows with complicated morphologies, which is characterized by scanning electron microscopy in detail. Changing the rare earth content of the alloy from Ce-rich mischmetal to lanthanum gives a further improvement in the tensile and creep properties, and the later could be attributed to the better thermal stability of Al11La3 phase in Mg-4Al-4La-0.4Mn alloy than that of Al11RE3 phase in Mg-4Al-4RE-0.4Mn alloy.

Microstructure, tensile properties, and creep behavior of high-pressure die-cast Mg-4Al-4RE-0.4Mn (RE = La, Ce) alloys

High-pressure die-cast (HPDC) Mg-4Al-4RE-0.4Mn (RE = La, Ce) magnesium alloys were prepared and their microstructures, tensile properties, and creep behavior have been investigated in detail. The results show that two binary Al-Ce phases, Al11Ce3 and Al2Ce, are formed mainly along grain boundaries in Mg-4Al-4Ce-0.4Mn alloy, while the phase composition of Mg-4Al-4La-0.4Mn alloy contains only alpha-Mg and Al11La3. The Al11La3 phase comprises large coverage of the grain boundary region and complicated morphologies. Compared with Al11Ce3 phase, the higher volume fraction and better thermal stability of Al11La3 have resulted in better-fortified grain boundaries of the Mg-4Al-4La-0.4Mn alloy. Thus higher tensile strength and creep resistance could be obtained in Mg-4Al-4La-0.4Mn alloy in comparison with that of Mg-4Al-4Ce-0.4Mn. Results of the theoretical calculation that the stability of Al11La3 is the highest among four Al-RE intermetallic compounds supports the experimental results further.

Microstructures and mechanical properties of Mg-8Gd-0.6Zr-xNd (x=0, 1, 2 and 3 mass%) alloys

Mg-8Gd-0.6Zr-xNd (x = 0, 1, 2 and 3 mass%) alloys were prepared by metal mould casting method, and the microstructures, age hardening responses and mechanical properties have been investigated. The microhardness of the as-cast alloys is increased with increasing Nd content. The age hardening behavior and mechanical properties are enhanced significantly by adding Nd element. The peak ageing hardness of the Mg-8Gd-0.6Zr-3Nd alloy is 103, it is about 1.3 times more than that of the Mg-8Gd-0.6Zr alloy. The aged Mg-8Gd-0.6Zr-3Nd alloy exhibits maximum ultimate tensile strength and yield strength, and the values are 271 and 205 MPa at room temperature, 205 MPa and 150 MPa at 250 degrees C, respectively. Which are about 2 times higher than those of Mg-8Gd-0.6Zr alloy. The improved hardness and strength are mainly attributed to the fine dispersiveness Of Mg5RE and Mg12RE precipitates in the alloy.

Structure and mechanical properties of Mg-Zn-RE system alloys

The lightest density of Mg has stimulated renewed interest in Mg based alloys for applications in the automotive, aerospace and communications industries. However, Mg in the pure form has relatively low strength, limited ductility and is susceptible to corrosion. Great efforts have been made to improve the mechanical properties of Mg alloys. Alloying Mg with other elements is one of the most important methods. An important class of Mg alloys is the Mg-Zn-RE system (RE = rare earth elements). In recent few decades, a series of new Mg-Zn-RE system alloys have been obtained, and detailed the structure and mechanical properties of the alloys. In this paper, the structure and mechanical properties of the Mg-Zn-RE alloys have been summarized. It showed that these alloys have high strength and they are prospected to be widely used in the future.

Microstructures and properties of melt-spun and as-cast Mg-20Gd binary alloy

Mg-20Gd(%, mass fraction) samples were prepared using melt-spinning and copper mold casting techniques. Microstructures and properties of the Mg-20Gd were investigated. Results show that the melt-spun ribbon is mainly composed of supersaturated alpha-Mg solid solution phase and the as-east ingot mainly contains alpha-Mg solid solution and Mg5Gd phase. The differential scanning calorimeter (DSC) curve of the ribbon exhibits a small exothermic peak in the temperature range from 630 to 680 K, which indicates that the ribbon contains a metastable phase (amorphous). Tensile strength at room temperature of the melt-spun ribbon and as-cast specimen are 308 and 254 MPa, respectively. The elongations of the two samples are less than 2%. The fracture surfaces demonstrate that the fracture mode of the as-cast Mg-20Gd is a typical cleavage fracture and that of the melt-spun sample is a combination of brittle fracture and ductile fracture.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Learning Unions of Rectangles with Queries

We investigate the efficient learnability of unions of k rectangles in the discrete plane (1,...,n)[2] with equivalence and membership queries. We exhibit a learning algorithm that learns any union of k rectangles with O(k^3log n) queries, while the time complexity of this algorithm is bounded by O(k^5log n). We design our learning algorithm by finding "corners" and "edges" for rectangles contained in the target concept and then constructing the target concept from those "corners" and "edges". Our result provides a first approach to on-line learning of nontrivial subclasses of unions of intersections of halfspaces with equivalence and membership queries.

A Quantitative Evaluation of the AVITEWRITE Model of Handwriting Learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.