Newest Issue

Research Papers: Design Theory and Methodology

J. Mech. Des. 2018;140(5):051101-051101-11. doi:10.1115/1.4039119.

Complexity and modularity are important inherent properties of the system. Complexity is the property of the system that has to do with individual system elements and their connective relationship, while modularity is the degree to which a system is made up of relatively independent but interacting elements, with each module typically carrying an isolated set of functionality. Modularization is not necessarily a means of reducing intrinsic complexity of the system but is a mechanism for complexity redistribution that can be better managed by enabling design encapsulation. In this paper, the notion of integrative complexity (IC) is proposed, and the corresponding metric is proposed as an alternative metric for modularity from a complexity management viewpoint. It is also demonstrated using several engineered systems from different application domains that there is a strong negative correlation between the IC and system modularity. This leads to the conclusion that the IC can be used as an alternative metric for modularity assessment of system architectures.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2018;140(5):051401-051401-11. doi:10.1115/1.4039387.

Due to the uncertainties and the dynamic parameters from design, manufacturing, and working conditions, many engineering structures usually show uncertain and dynamic properties. This paper proposes a novel time-variant reliability analysis method using failure processes decomposition to transform the time-variant reliability problems to the time-invariant problems for dynamic structures under uncertainties. The transformation is achieved via a two-stage failure processes decomposition. First, the limit state function with high dimensional input variables and high order temporal parameters is transformed to a quadratic function of time based on the optimized time point in the first-stage failure processes decomposition. Second, based on the characteristics of the quadratic function and reliability criterion, the time-variant reliability problem is then transformed to a time-invariant system reliability problem in the second-stage failure processes decomposition. Then, the kernel density estimation (KDE) method is finally employed for the system reliability evaluation. Several examples are used to verify the effectiveness of the proposed method to demonstrate its efficiency and accuracy.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2018;140(5):051402-051402-9. doi:10.1115/1.4039339.

Strategies combining active learning Kriging (ALK) model and Monte Carlo simulation (MCS) method can accurately estimate the failure probability of a performance function with a minimal number of training points. That is because training points are close to the limit state surface and the size of approximation region can be minimized. However, the estimation of a rare event with very low failure probability remains an issue, because purely building the ALK model is time-demanding. This paper is intended to address this issue by researching the fusion of ALK model with kernel-density-estimation (KDE)-based importance sampling (IS) method. Two stages are involved in the proposed strategy. First, ALK model built in an approximation region as small as possible is utilized to recognize the most probable failure region(s) (MPFRs) of the performance function. Consequentially, the priori information for IS are obtained with as few training points as possible. In the second stage, the KDE method is utilized to build an instrumental density function for IS and the ALK model is continually updated by treating the important samples as candidate samples. The proposed method is termed as ALK-KDE-IS. The efficiency and accuracy of ALK-KDE-IS are compared with relevant methods by four complicated numerical examples.

Commentary by Dr. Valentin Fuster

Research Papers: Design for Manufacture and the Life Cycle

J. Mech. Des. 2018;140(5):051701-051701-13. doi:10.1115/1.4039201.

Design for additive manufacturing (DfAM) is gaining increasing attention because of the unique capabilities that additive manufacturing (AM) technologies provide. While they have the ability to produce more complex shapes at no additional cost, AM technologies introduce new constraints. A detailed knowledge of the AM process plays an important role in the design of parts in order to achieve the desired print result. However, research on knowledge management in this area is still limited. The large number of different AM processes, their individual sets of critical parameters and the variation in printing all contribute to a high level of uncertainty in this knowledge domain. Applying AM at the early stages of design projects introduces another source of uncertainty, as requirements are often not well defined at that point. In this paper, a knowledge management system using Bayesian networks (BNs) is proposed to model AM knowledge in cases where there is some uncertainty and fill the knowledge gap between designers and AM technologies. The structure of the proposed model is defined here by introducing the overview layer and detailed information layer. In each layer, different types of nodes and their causal relationships are defined. The system can learn conditional probabilities in the model from different sources of information and inferences can be conducted in both forward and backward directions. To verify the accuracy of the BNs, a sample model for dimensional accuracy in the fused deposition modeling (FDM) process is presented and the results are compared with other methods. A case study is provided to illustrate how the proposed system can help designers with different design questions understand the capabilities of AM processes and find appropriate design and printing solutions.

Commentary by Dr. Valentin Fuster

Research Papers: Design of Direct Contact Systems

J. Mech. Des. 2018;140(5):053301-053301-11. doi:10.1115/1.4039386.

Skidding is a typical behavior of high-speed angular contact ball bearings. Studies show that the effect of preload on bearing skidding and thermal characteristic is essential but insufficient. This paper proposed a comprehensive mathematic model to predict the skidding behavior of ball bearing, and the influences of the interactions between ball and raceways, cage and lubricant have been taken into consideration. Based on the proposed model, the bearing heating generation was calculated and the effects of skidding on bearing heating and temperature rise were analyzed. For validation proposes, a hydraulic variable preload experimental setup has been built, and the temperature rise of bearing outer rings under different preloads and speeds was collected and analyzed. The results indicated that at high speeds, the skidding has a significant negative effect on bearing temperature rise, and a proper preload can effectively prevent skidding and decrease temperature rise. Therefore, for a high-speed spindle-bearing system, an optimum preload that produced the minimum temperature rise can be obtained.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2018;140(5):053302-053302-14. doi:10.1115/1.4039337.

A load distribution model of planetary gear sets presented is capable of simulating planetary gear sets having component- and system-level design variations such as component supporting conditions, different kinds of gear modifications and planetary gear sets with different numbers of equally or unequally spaced planets as well as different gear set kinematic configurations while considering gear mesh phasing. It also accounts for classes of planetary gear set manufacturing and assembly related errors associated with the carrier or gears, i.e., pinhole position errors, run-out errors, and tooth thickness errors. Example analyses are provided to indicate the need for a model of this type when studying load distribution of planetary gear sets due to unique loading of the gear meshes associated with planetary gear sets. Comparisons to measurements existing in the literature are provided.

Commentary by Dr. Valentin Fuster

Design Innovation Paper

J. Mech. Des. 2018;140(5):055001-055001-8. doi:10.1115/1.4039385.

In this paper, a powered ankle-foot prosthesis with nonlinear parallel spring mechanism is developed. The parallel spring mechanism is used for reducing the energy consumption and power requirement of the motor, at the same time simplifying control of the prosthesis. To achieve that goal, the parallel spring mechanism is implemented as a compact cam-spring mechanism that is designed to imitate human ankle dorsiflexion stiffness. The parallel spring mechanism can store the negative mechanical energy in controlled dorsiflexion (CD) phase and release it to assist the motor in propelling a human body forward in a push-off phase (PP). Consequently, the energy consumption and power requirements of the motor are both decreased. To obtain this desired behavior, a new design method is proposed for generating the cam profile. Unlike the existing design methods, the friction force is considered here. The cam profile is decomposed into several segments, and each segment is fitted by a quadratic Bezier curve. Experimental results show that the cam-spring mechanism can mimic the desired torque characteristics in the CD phase (a loading process) more precisely. Finally, the developed prosthesis is tested on a unilateral below-knee amputee. Results indicate that, with the assistance of the parallel spring mechanism, the motor is powered off and control is not needed in the CD phase. In addition, the peak power and energy consumption of the motor are decreased by approximately 37.5% and 34.6%, respectively.

Commentary by Dr. Valentin Fuster

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In