Advanced Materials Research
About: Advanced Materials Research is an academic journal. The journal publishes majorly in the area(s): Microstructure & Ultimate tensile strength. It has an ISSN identifier of 1022-6680. Over the lifetime, 125582 publication(s) have been published receiving 190272 citation(s).
Papers published on a yearly basis
25 Jan 2012-Advanced Materials Research
TL;DR: In this article, the authors compared a Dimensional Analysis (DA) model, an Artificial Neural Network (ANN) model and an experimental result for a low gap current of an Electrical Discharge Machining (EDM) process.
Abstract: This paper aims to compare the material removal rate, ν between a Dimensional Analysis (DA) model, an Artificial Neural Network (ANN) model and an experimental result for a low gap current of an Electrical Discharge Machining (EDM) process. The data analysis is based on a copper electrode and steel workpiece materials. The DA and ANN model that have been developed and reported earlier by authors are used to compare the material removal of EDM process. The result indicated that the ANN model provides better accuracy towards the experimental results.
01 Jan 1979-Advanced Materials Research
01 Sep 2013-Advanced Materials Research
TL;DR: In this article, the performance limitations, future prospects, and improvements of the common used dyes decolorization and decoloring with external voltage or current supply in Bioelectrochemical systems are reviewed.
Abstract: Bioelectrochemical systems or electrochemical reduction reactors have great potential for treating wastewater that contains dyes for decolorization. They are reported to enhance decolorization rate and degree with external energy supply and to help microorganisms or noble metal as catalysts. Till now literatures regarding dye decolorization with electron reduction using BESs or electrochemical reactors is deficient. This paper reviews the performance limitations, future prospects, and improvements of the common used dyes decolorization and decolorization with external voltage or current supply in Bioelectrochemical systems.
01 Sep 2013-Advanced Materials Research
TL;DR: In this paper, high cycle fatigue (HCF) tests were performed for as-built, polished and shot-peened samples to investigate the capability of selective laser melting (SLM) for these applications.
Abstract: Selective laser melting (SLM) is a relatively new additive manufacturing (AM) technology which uses laser energy for manufacturing in a layered pattern. The unique manufacturing process of SLM offers a competitive advantage in case of very complex and highly customized parts having quasi-static mechanical properties comparable to those of wrought materials. However, it is not currently being harnessed in dynamic applications due to the lack of reliable fatigue data. The manufacturing process shows competitive advantages particularly in the aerospace and medical industry in which Ti-6Al-4V is commonly used, especially for high performance and dynamic applications. Therefore, in this exploratory research, high cycle fatigue (HCF) tests were performed for as-built, polished and shot-peened samples to investigate the capability of SLM for these applications. As-built samples showed a drastic decrement of fatigue limit due to poor surface quality (Ra ≈ 13 µm) obtained from the SLM process. Polishing improved the fatigue limit to more than 500 MPa, the typical value for base material. The effect of shot-peening proved to be antithetical to the expected results. In this context, fractographic analysis showed that very small remnant porosity (less than 0.4%) played a critical role in fatigue performance.
01 Jan 2011-Advanced Materials Research
TL;DR: An improved particle swarm optimization (IPSO) was proposed in this paper to solve the problem that the linearly decreasing inertia weight (LDIW) of particle Swarm optimization algorithm cannot adapt to the complex and nonlinear optimization process.
Abstract: An improved particle swarm optimization (IPSO) was proposed in this paper to solve the problem that the linearly decreasing inertia weight (LDIW) of particle swarm optimization algorithm cannot adapt to the complex and nonlinear optimization process The strategy of nonlinear decreasing inertia weight based on the concave function was used in this algorithm The aggregation degree factor of the swarm was introduced in this new algorithm And in each iteration process, the weight is changed dynamically based on the current aggregation degree factor and the iteration times, which provides the algorithm with dynamic adaptability The experiments on the three classical functions show that the convergence speed of IPSO is significantly superior to LDIWPSO, and the convergence accuracy is increased
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