首次提出了用于汽车生产中分瓣模压印连接接头强度和失效形式的预测方法。根据接头静力学测试中的颈部断裂失效和上下板拉脱失效两种失效形式分别建立了压印接头的两个强度预测公式,2pπ2N N NF A R t t()和2p pπt b s F R,公式以接头...首次提出了用于汽车生产中分瓣模压印连接接头强度和失效形式的预测方法。根据接头静力学测试中的颈部断裂失效和上下板拉脱失效两种失效形式分别建立了压印接头的两个强度预测公式,2pπ2N N NF A R t t()和2p pπt b s F R,公式以接头颈部厚度Nt和镶嵌量Ut为重要的中间变量。强度预测公式表明:对于颈部断裂的压印接头,颈部厚度值tN越大,接头强度越高;对于拉脱失效的压印接头,接头强度取决于颈部厚度tN和镶嵌量tU,两者之和越大,接头强度越高,并且镶嵌量对接头强度的影响与颈部厚度相比更大。对颈部厚度变化范围为0.35mm^0.56mm、镶嵌量变化范围为0.045mm^0.45mm的15种组合接头,根据强度预测公式计算了接头强度,并进行了拉伸-剪切试验。将计算结果与试验结果进行对比,结果表明二者吻合较好,最大接头强度误差为8.9%。这说明本文建立的接头强度预测公式能够准确地预测压印接头拉伸-剪切过程的强度和破坏形式。展开更多
Purpose: The purpose of this study was to establish the relationship between various expressions of relative exercise intensity percentage of maximal oxygen uptake(%VO_(2max)), percentage of maximal heart rate(%HR_(ma...Purpose: The purpose of this study was to establish the relationship between various expressions of relative exercise intensity percentage of maximal oxygen uptake(%VO_(2max)), percentage of maximal heart rate(%HR_(max)), %VO_2 reserve(%VO_2R), and %HR reserve(%HRR)) in order to obtain the more appropriate method for exercise intensity prescription when using an immersible ergocycle(IE) and to propose a prediction equation to estimate oxygen consumption(VO_2) based on IE pedaling rate(rpm) for an individualized exercise training prescription.Methods: Thirty-three healthy participants performed incremental exercise tests on IE and dryland ergocycle(DE) at equal external power output(Pext). Exercise on IE began at 40 rpm and was increased by 10 rpm until exhaustion. Exercise on DE began with an initial load of 25 W and increased by 25 W/min until exhaustion. VO_2 was measured with a portable gas analyzer(COSMED K4b^2) during both incremental tests. On IE and DE, %VO_2R, %HRmax, and %HRR at equal Pext did not differ(p > 0.05).Results: The %HRR vs. %VO_2R regression for both IE and DE did not differ from the identity line %VO_2R IE = 0.99 × HRR IE(%) + 0.01(r^2= 0.91, SEE = 11%); %VO_2R DE = 0.94 × HRR DE(%) + 0.01(r^2= 0.94, SEE = 8%). Similar mean values for %HRmax, %VO_2R, and %HRR at equal Pext were observed on IE and DE. Predicted VO_2 obtained according to rpm on IE is represented by: VO_2(L/min) = 0.000542 × rpm2-0.026 × rpm + 0.739(r = 0.91, SEE = 0.319 L/min).Conclusion: The %HRR–%VO_2R relationship appears to be the most accurate for exercise training prescription on IE. This study offers new tools to better prescribe, control, and individualize exercise intensity on IE.展开更多
文摘首次提出了用于汽车生产中分瓣模压印连接接头强度和失效形式的预测方法。根据接头静力学测试中的颈部断裂失效和上下板拉脱失效两种失效形式分别建立了压印接头的两个强度预测公式,2pπ2N N NF A R t t()和2p pπt b s F R,公式以接头颈部厚度Nt和镶嵌量Ut为重要的中间变量。强度预测公式表明:对于颈部断裂的压印接头,颈部厚度值tN越大,接头强度越高;对于拉脱失效的压印接头,接头强度取决于颈部厚度tN和镶嵌量tU,两者之和越大,接头强度越高,并且镶嵌量对接头强度的影响与颈部厚度相比更大。对颈部厚度变化范围为0.35mm^0.56mm、镶嵌量变化范围为0.045mm^0.45mm的15种组合接头,根据强度预测公式计算了接头强度,并进行了拉伸-剪切试验。将计算结果与试验结果进行对比,结果表明二者吻合较好,最大接头强度误差为8.9%。这说明本文建立的接头强度预测公式能够准确地预测压印接头拉伸-剪切过程的强度和破坏形式。
基金provided by the éPIC Foundation and the Montreal Heart Institute Foundation
文摘Purpose: The purpose of this study was to establish the relationship between various expressions of relative exercise intensity percentage of maximal oxygen uptake(%VO_(2max)), percentage of maximal heart rate(%HR_(max)), %VO_2 reserve(%VO_2R), and %HR reserve(%HRR)) in order to obtain the more appropriate method for exercise intensity prescription when using an immersible ergocycle(IE) and to propose a prediction equation to estimate oxygen consumption(VO_2) based on IE pedaling rate(rpm) for an individualized exercise training prescription.Methods: Thirty-three healthy participants performed incremental exercise tests on IE and dryland ergocycle(DE) at equal external power output(Pext). Exercise on IE began at 40 rpm and was increased by 10 rpm until exhaustion. Exercise on DE began with an initial load of 25 W and increased by 25 W/min until exhaustion. VO_2 was measured with a portable gas analyzer(COSMED K4b^2) during both incremental tests. On IE and DE, %VO_2R, %HRmax, and %HRR at equal Pext did not differ(p > 0.05).Results: The %HRR vs. %VO_2R regression for both IE and DE did not differ from the identity line %VO_2R IE = 0.99 × HRR IE(%) + 0.01(r^2= 0.91, SEE = 11%); %VO_2R DE = 0.94 × HRR DE(%) + 0.01(r^2= 0.94, SEE = 8%). Similar mean values for %HRmax, %VO_2R, and %HRR at equal Pext were observed on IE and DE. Predicted VO_2 obtained according to rpm on IE is represented by: VO_2(L/min) = 0.000542 × rpm2-0.026 × rpm + 0.739(r = 0.91, SEE = 0.319 L/min).Conclusion: The %HRR–%VO_2R relationship appears to be the most accurate for exercise training prescription on IE. This study offers new tools to better prescribe, control, and individualize exercise intensity on IE.
