Metal additive manufacturing (AM) has recently attracted attention due to its potential for batch/mass production of metal parts. This process, however, currently suffers from problems including low productivity, inconsistency in the properties of the printed parts, and defects such as lack of fusion, keyholing, and un-melted powders. Finite Element (FE) modeling cannot accurately model the metal AM process and has a high computational cost. Empirical models based on experiments are time-consuming and expensive. This paper improves a previously developed framework that takes advantages of both empirical and FE models. The validity and accuracy of the metamodel developed in the previous framework depend on the initial assumption of parameter uncertainties. This causes a problem when the assumed uncertainties are far from the actual values. The proposed framework introduces an iterative calibration process to overcome this limitation. In addition, the u_pooling metric used as the calibration metric in the previous framework is found not as good as the second-order statistical moment-based metric (SMM), after comparing several calibration metrics. The proposed framework is then applied to a four-variable porosity modeling problem. The obtained model is more accurate than using other approaches with only 10 available experimental data points for calibration and validation.

No abstract

The present study examined the effects of auxetic shoes on the biomechanics of the spine, as compared to barefoot and conventional shoe conditions, during gait and drop vertical jump (DVJ) activities using a combined in vivo and musculoskeletal modeling approach. Motion and force-plate data as well as electromyographic (EMG) activities of select trunk muscles of 11 individuals were collected during foregoing activities. In DVJ activity, two main phases of first landing (FL) and second landing (SL) were studied. In the FL phase of DVJ noticeable alternations were observed when auxetic shoes were used. That is, compared to the conventional footwear condition, smaller EMG activities in extensor muscles (by ~ 16–29%, p < 0.001), smaller anterior–posterior (AP) distance between the center of pressure of ground reaction force and heel (by ~ 19%, p = 0.002), generally larger maximal hip, knee, and ankle flexion angles (p < 0.005) and finally smaller maximal L5-S1 compression force and maximal external moment (by ~ 12 and 8%, respectively, p < 0.001) were obtained by wearing auxetic shoes. Our results, therefore, indicate that using auxetic shoes can reduce load on the lumbar spine during high-demanding activities such as vertical jump and thus may decrease the musculoskeletal risk of injuries during these activities.

Metal additive manufacturing (AM) has recently attracted attention due to its potential for batch/mass production of metal parts. This process, however, currently suffers from problems including low productivity, inconsistency in the properties of the printed parts, and defects such as lack of fusion, keyholing, and un-melted powders. Finite Element (FE) modeling cannot accurately model the metal AM process and has a high computational cost. Empirical models based on experiments are time-consuming and expensive. This paper improves a previously developed framework that takes advantages of both empirical and FE models. The validity and accuracy of the metamodel developed in the previous framework depend on the initial assumption of parameter uncertainties. This causes a problem when the assumed uncertainties are far from the actual values. The proposed framework introduces an iterative calibration process to overcome this limitation. In addition, the u_pooling metric used as the calibration metric in the previous framework is found not as good as the second-order statistical moment-based metric (SMM), after comparing several calibration metrics. The proposed framework is then applied to a four-variable porosity modeling problem. The obtained model is more accurate than using other approaches with only 10 available experimental data points for calibration and validation.