The Determination of Optimum Forging Conditions for the Production of High Strength-High Impact Toughness Automotive Parts

The influences of various reheating and forging temperatures as well as cooling rates on the microstructure and mechanical properties, particularly impact energy, during the forging of a Nb-V microalloyed steel to be used for automotive safety parts were investigated. Increasing the prior austenite grain size increased the volume percent of acicular ferrite and reduced pearlite content in the microstructure even for very low post-forging cooling rates, resulting in improved toughness and tensile strength values. Increasing the cooling rate enhanced the acicular ferrite content, thereby increasing the impact energy properties. At lower reheating temperatures the yield strength and impact energy levels are determined by the percentage of pearlite present in the microstructure; while as the cooling rate is increased the amount of acicular ferrite and retained austenite are increased, improving the toughness and tensile strength of the forged part. This effect is more pronounced for the parts solutionized at 1250°C and is related to the presence of very fine carbonitride precipitates under these conditions, which contributes to improved yield strength, particularly at higher cooling rates. An optimized forging process was determined and adapted to a 25 MN production forging press to validate the experimental results on semi-industrial production scale. By adequate control of the above parameters, high-strength, high-toughness parts (T.S. = 800 MPa, CVN = 35 J) were forged and optimum mechanical properties were achieved without the need for any additional heat treatment.     

Correction to: Novel dynamic load balancing algorithm for cloud‑based big data analytics

The Journal of Supercomputing -     

Novel dynamic load balancing algorithm for cloud-based big data analytics

Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements.