Application of the Taguchi Method to the Optimization of the Submerged Arc Welding Process

Application of the Taguchi method to the optimization of the submerged arc welding process is reported. The Taguchi method is used to formulate the experimental layout, to analyze the effect of each welding parameter on welding performance, and to predict the optimal setting for each welding parameter. Experimental results are provided to confirm the effectiveness of this approach.     

ELEVATED TEMPERATURE FRACTURE OF PARTICULATE-REINFORCED ALUMINUM PART I: FRACTURE TOUGHNESS

Abstract—The plane-strain initiation and growth fracture toughnesses of powder-metallurgy-processed, SiC particulate-reinforced 2009 plate were measured at temperatures from 25°C to 316°C. Initiation toughness from electrical potential monitoring ( K _JICi) is 18 MPa°m at 25°C, and is nearly constant to 220°C before decreasing sharply to 6 MPa°m at 316°C. Growth toughness, given by the tearing modulus ( T _R), is less than 3 from 25°C to 125°C, and increases dramatically above 200°C. The magnitude and temperature dependence of initiation toughness depend on detection of the critical fracture event. Standard measures of toughness K _IC and K _JIC exceed K _JICi and increase to a plateau with increasing temperature. The fracture mode for the composite is microvoid nucleation, growth and coalescence at all temperatures. Void nucleation is associated with SiC; such particles both crack and create stress and plastic strain concentrations that rupture the interface or adjacent matrix, particularly at corners. Matrix plasticity and cavitation increase with increasing temperature. Void growth is regular at all temperatures, but limited by adjacent SiC particles. Both K _JICi and T _R are governed by the temperature-dependent crack-tip plastic stress and strain fields, and the intrinsic damage resistance of the composite microstructure.     

一种新型无突变自动高压电源

本文介绍一种新型高压直流电源，它的输出高压的提升、下降均自动、无级、缓慢地变化，无突变现象，尤其在电网供电突然中断时，也能使用高压自动、缓慢地下降到０。它用于半导体放射性射线探测器，可以避免由于操作的粗心或电网供电突然中继引起高压快速变化或而产生的对该类昂贵探测器及其附件的损害，使探测器的优良性能得以保持。     

PC／ABS合金的研制及其在汽车上的应用

对ＰＣ／ＡＢＳ合金开发和研制及其在汽车上的应用作了评述。介绍了上海杰事公司在该领域的进展。     

Stiffness of trabecular bone of the tibial plateau in patients with rheumatoid arthritis of the knee

Stiffness of subchondral proximal tibial trabecular bone is a factor in the stability of prostheses implanted into that bone. The stiffness of trabecular bone in osteoarthritis (OA) has been documented. Trabecular bone in rheumatoid arthritis (RA) is osteopenic in numerous sites and morphologically abnormal in the proximal tibia. Reliable data on proximal tibial bone in RA are lacking, although 1 study failed to identify abnormalities. The purposes of this study were (1) to document the stiffness of the proximal tibial cancellous bone in patients with RA, (2) to determine the effect of angular deformity on bone stiffness in rheumatoid patients, and (3) to compare RA stiffness values with those in published reports for OA. Fifteen tibial plateau were obtained from patients with RA during surgery. Each plateau was horizontally seated in a mold and covered with cement. The plateau was divided into 6 regions, which were used to facilitate comparison between specimens and the existing literature. Indentation tests were conducted with a 4-mm-diameter cylindrical indentor controlled by an MTS machine. The indentor descended at a rate of 2 mm/min to a maximum depth of 1.0 mm; load and displacement data were digitally recorded. Stiffness was calculated from the slope of the linear region of the curve using best-fit linear regression. Where varus deformity was present, stiffness in the medial plateau was higher overall than for the other compartment; whereas in the case of valgus deformity, stiffness of the lateral side was significantly higher (P < .05 for each observation). In comparison to older normal specimens, both the medial compartment of the varus RA specimens (P < .01) and the posterolateral compartment of the valgus RA specimens (P < .01) had significantly lower stiffness. Comparison with OA specimens showed that in varus RA, the posteromedial region had significantly lower stiffness than in varus OA at the same site (P < .01). In valgus RA, the lateral region had significantly lower stiffness than in valgus OA at the same site (P < .01). The mean stiffness ratio of the valgus RA was significantly (P < .01) altered from normal, and for the varus RA, it was significantly (P < .01) different from normal posteriorly. The stiffness ratios for the varus RA were significantly (P < .01) different from those for varus OA; there was no difference between valgus RA and valgus OA. It is concluded that RA affected bone has significantly lower stiffness than normal and osteoarthritic bone. The loaded plateau is stiffer than the unloaded plateau in angular deformity, but is still less stiff than normal bone and osteoarthritic plateaus with corresponding deformities.     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

浅谈混凝土重力坝上游止水铜片断裂修复技术

以月潭水库上游面止水铜片修复为例,具体介绍了混凝土重力坝止水铜片缺陷产生的原因、修复方案、质量控制等,修复后达到了良好的效果,其方案可行,质量可靠,可以为其他类似工程提供借鉴。     

A conditional classification recurrent RBM for improved series mid-term forecasting

In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.

     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...