Prediction of Board Level Pad Cratering Strength with the Pre-Defined Failure Criteria From Joint Level Testing

Conventional reliability tests for the evaluation of pad cratering resistance are mainly classified into two categories: the board level test and the joint level test. The board level test is to imitate the loading conditions during normal operation. However, this type of test is expensive and not flexible. The joint level test is used extensively in the industry because it has the advantages of lower cost, higher throughput, and more quantitative results. It also allows the elimination of confounding factors such as PCB and component stiffness. Therefore, it is always desirable to predict the board level performance by a joint level test. In order to achieve this objective, the correlation between the joint level and the board level tests must be fully understood. Nevertheless, a precise correlation between the two types of tests for pad cratering evaluation is yet to be defined. This study investigates the pad cratering failure mode for the correlation of critical failure factors between joint and board level tests. An intermediate critical failure factor could be taken as a failure criterion in board level testing for failure detection. For verifying the validity of such a failure criterion, an experimental study should be performed. The 4-point bending test is chosen as the board level test for critical failure factor validation. In addition, an innovative pin shear test method is developed as the joint level test for failure factor detection. Both test methods are assessed by a series of parametric studies with an optimized process to ensure the accuracy of the results. From the results of the experimental study and simulation, the critical failure factor correlation is established between the board level 4-point bending and the joint level pin shear test. Using finite element analysis (FEA), the critical failure strain is identified from the pin shear test model and will be employed as the board level failure criterion. Subsequently the obtained failure criterion is verified by a 4-point bending model. As a result, this indirect correlation method can predict the board level failure with various geometric parameters.

Musculoskeletal Adaptation of Young and Older Adults in Response to Environmental, Physical, and Cognitive Conditions

Accidental falls present a large functional and financial burden among people aged 65 years and older. Falls, injuries associated with falls, and the fear of falling decrease quality of life, physical function, and independence for older adults. To prevent falls, improve stability, and protect joints from damage or injury, the typical response to "challenging" conditions include cautious gait, increase muscle co-contraction, and decreased range of motion. These compensatory strategies are more pronounced in the older adult population with apprehensive "cautious" gait at slower speeds, decreased knee flexion, and increased muscle activation around the knee and ankle. The underlying mechanisms and driving forces behind accidental falls are not well investigated. Additionally, the effects of aging on the ability of the musculoskeletal system to adapt to changing and challenging conditions is poorly understood. There exists a gap in knowledge regarding the relationship between accidental fall risk factors, knee joint stability, adaptation mechanisms, and whole-body function. Establishing these relationships between stability and musculoskeletal adaptation may have far reaching implications on improving whole-body function through targeted joint- and muscle-level interventions. The purpose of this study was to compare neuromechanics (whole-body function) of young and older adults walking across various external challenging conditions, quantifying adaptation strategies for both cohorts. This was accomplished through two objectives. In the first objective, joint kinematics, ground reaction force loading and impulse, and lower-limb muscle activation strategies for ten young and ten older adults walking on normal, slick, and uneven surfaces were compared to assess how musculoskeletal adaptation strategies change with age. For the second objective, a pipeline to create subject-specific lower-limb finite element models was developed to investigate joint-level behavior across cohorts. Proof-of-concept for the model development and analysis process was demonstrated for an older and a young adult to implement a novel metric for functional stability and dynamic laxity of the knee joint during the stance phase of gait. Kinematic, force, and muscle activation analysis showed that an uneven surface reduced sagittal joint kinematics during the first 25% of stance, indicating a surface-specific compensatory strategy. Additionally, older adults tended to prepare for and step onto the uneven surfaces in a more conservative manner with joints more flexed or bent. This anticipatory or cautious musculoskeletal adaptation of older adults was also seen in reduced magnitude of initial vertical loading during the loading response of stance (0-25% stance). Results of this research study provide insight into the differences that exist in joint stiffening and other musculoskeletal adaption strategies for young and older adults during external challenging conditions. Specifically, understanding the relationships between joint-level stability and whole-body musculoskeletal function has the potential to inform targeted muscle training programs and joint-level interventions to improve whole-body musculoskeletal function and reduce risk of injuries.

Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator

Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control for serial multi-DOF robot manipulators. In this paper, the contouring control of multi-DOF serial manipulators is developed for the first time and a new cross-coupled PD (CC-PD) control law is proposed, based on contour errors of the end-effector and tracking errors of the joints. It is a combination of PD control for trajectory tracking at joint level and PD control for contour tracking at the end-effector level. The contour error of the end-effector is transformed to the equivalent tracking errors of the joints using the Jacobian regulation, and the CC-PD control law is implemented in the joint level. Stability analysis of the proposed CC-PD control system is conducted using the Lyapunov method, followed by some simulation studies for linear and nonlinear contour tracking to verify the effectiveness of the proposed CC-PD control system.

Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator

Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control for serial multi-DOF robot manipulators. In this paper, the contouring control of multi-DOF serial manipulators is developed for the first time and a new cross-coupled PD (CC-PD) control law is proposed, based on contour errors of the end-effector and tracking errors of the joints. It is a combination of PD control for trajectory tracking at joint level and PD control for contour tracking at the end-effector level. The contour error of the end-effector is transformed to the equivalent tracking errors of the joints using the Jacobian regulation, and the CC-PD control law is implemented in the joint level. Stability analysis of the proposed CC-PD control system is conducted using the Lyapunov method, followed by some simulation studies for linear and nonlinear contour tracking to verify the effectiveness of the proposed CC-PD control system.

CT-like images of the sacroiliac joint generated from MRI using susceptibility-weighted imaging (SWI) in patients with axial spondyloarthritis

BackgroundTo analyse the added value of susceptibility-weighted imaging (SWI) compared with standard T1-weighted (T1) MRI for detecting structural lesions of the sacroiliac joint (SIJ) in patients with axial spondyloarthritis (axSpA) using CT as reference standard.Material and methodsSixty-eight patients with suspected or proven axSpA underwent both MRI and CT of the SIJ on the same day. Two readers separately scored CT, T1 and SWI for the presence of erosions, sclerosis and joint space changes using an established 24-region SIJ model. Disagreement was resolved by a third reader. Diagnostic accuracy (McNemar test), Cohen’s kappa (k), sensitivity (SE) and specificity were calculated on the joint level using CT as reference.ResultsIn CT, 38 joints showed erosions, 67 sclerosis and 37 joint space changes. Agreement with CT for erosions was 92.6% (k=0.811 (0.7–0.92)) in SWI and 87.5% (k=0.682 (0.54–0.82)) in T1 (p=0.143) and agreement for sclerosis 84.6% (k=0.69 (0.57–0.81)) and 62.5% (k=0.241 (0.13–0.35)) (p<0.001), respectively. This resulted in superior SE of SWI (81.6% vs 73.7%) for erosions and sclerosis (74.6% vs 23.9%) at a minor expense of SP. No differences were detected for joint space changes.ConclusionIn patients with axSpA, SWI depicts erosions and sclerosis more accurately than T1 spin echo MRI at 1.5 T.

Detailed Study on the Behavior of Improved Beam T-Junctions Modeling for the Characterization of Tubular Structures, Based on Artificial Neural Networks Trained with Finite Element Models

The actual behavior of welded T-junctions in tubular structures depends strongly on the topology of the junction at the joint level. In finite element analysis, beam-type elements are usually employed due to their simplicity and low computational cost, even though they cannot reproduce the joints topologies and characteristics. To adjust their behavior to a more realistic situation, elastic elements can be introduced at the joint level, whose characteristics must be determined through costly validations. This paper studies the optimization and implementation of the validation data, through the creation of an optimal surrogate model based on neural networks, leading to a model that predicts the stiffness of elastic elements, introduced at the joint level based on available data. The paper focuses on how the neural network should be chosen, when training data is very limited and, more importantly, which of the available data should be used for training and which for verification. The methodology used is based on a Monte Carlo analysis that allows an exhaustive study of both the network parameters and the distribution and choice of the limited data in the training set to optimize its performance. The results obtained indicate that the use of neural networks without a careful methodology in this type of problems could lead to inaccurate results. It is also shown that a conscientious choice of training data, among the data available in the problem of choice of elastic parameters for T-junctions in finite elements, is fundamental to achieve functional surrogate models.