Portable products as well as some larger products may see failures by a high strain rate mechanical loading like that seen in a high or low level drop/shock event. Within the portable product industry there is a wide range of product design, usage and loading conditions. Because of this, standards such as JEDEC, which is meant to generate comparative results addressing component reliability, do little or nothing to generalize the reliability of specific assemblies. To do this we need to consider both failure rates and failure mechanisms to better understand how specific loading conditions affect the failure of an assembly.A standard JEDEC style test board was assembled with BGA components and then drop tested with an input of various energy levels. Pad cratering was the failure mode for all assemblies. This allowed for an independent investigation into this one failure mode. During the drop test the energy from the impact event is transferred into the board, which causes a response. A comparison between the response energy and the input energy was made and, for this particular test assembly, the input correlated to the response reasonably well. Upon further investigation it was found that the failure rates correlated well to the energy levels at which it was tested. This allowed for the development of a predictive model based on these empirical results. With the failure mode of all assemblies being pad cratering on the PCB side of the solder joint it allowed for an investigation into the response of individual pads. The fatigue resistance and strength of the PCB pads was measured using pad-level testing techniques. By measuring the fatigue resistance as a function of a fixed load, it was possible to develop an S-N curve for this particular test vehicle. From this it was found that the pad lifetime is also highly dependent on the input level, as was seen in the drop testing of this test vehicle.This study also proposes a method to compare the input energy and the response energy of the board computed using the finite element method. Good correlation between the measured response energy and the simulated response energy has been achieved. A lifetime predictive model has been developed using the simulated response energy. Furthermore, 3D FEA model has been established using ANSYS implicit solver with consideration of detailed copper pad, trace and solder joint design to describe the dynamic behavior of PCB assembly and to predict the location of failure and failure mode after impact. For correlation studies, the maximum volume average Von Mises stress of PCB-pad interface at critical solder joint location is calculated and correlated well with the N 63 impact lives measured during the drop test. This was used to develop an impact life prediction model. Nanoindentation technique is used to measure the material properties of copper trace. In addition to solder joint dimensions and material properties, many other factors, including trace dimensions, component size and PCB thickness were also simulated to investigate th...

Intelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration amongst robots to maximize throughput and safety on the shop floor. Moreover, we observe nuanced behavior, including the ability to autonomously compensate for processing delays, and machine and robot failures in real time.

No abstract

No abstract

The elicitation of customer pain points is a crucial early step in the design or redesign of successful products and services. Online, user-generated data contains rich, real-time information about customer experience, requirements, and preferences. However, it is a nontrivial task to retrieve useful information from these sources because of the sheer amount of data, often unstructured. In this work, we build on previous efforts that used natural language processing techniques to extract meaning from online data and facilitate experiential redesign and extend them by integrating a sentiment analysis. As a use case, we explore the airline industry. A considerable portion of potential passengers opt out of traveling by airplane due to aviophobia, a fear of flying. This causes a market loss to the industry and inconvenience for those who experience aviophobia. The potential contributors to aviophobia are complex and diverse, involving physical, psychological and emotional reactions to the air travel experience. A methodology that is capable of accommodating the complexity and diversity of the commercial airline industry user-generated data is necessary to effectively mine customer pain points. To address the demand, we propose a novel methodology in this study. Using passenger commentary data posted on Reddit, the method implements topic modeling to extract common themes from the commentaries and employs sentiment analysis to elicit and interpret the salient information contained in the extracted themes. This paper ends by providing specific recommendations that are germane to the use case as well as suggesting future research directions.

No abstract

No abstract

Reinforcement learning algorithms can autonomously learn to search a design space for high-performance solutions. However, modern engineering often entails the use of computationally-intensive simulation, which can lead to slower design timelines with highly iterative approaches such as RL. This work provides a reinforcement learning framework that leverages models of varying fidelity to enable an effective solution search while reducing overall computational needs. Specifically, it utilizes models of varying fidelity while training the agent, iteratively progressing from low- to high-fidelity. To demonstrate the effectiveness of the proposed framework, we apply it to two multimodal multi-objective constrained mixed integer nonlinear design problems involving the components of a ground and aerial vehicle. Specifically, for each problem we utilize a high-fidelity and a low-fidelity deep neural network surrogate model, trained on performance data generated from underlying ground truth models. A tradeoff between solution quality and the proportion of low-fidelity surrogate model usage is observed. Specifically, high quality solutions are achieved with substantial reductions in computational expense, showcasing the effectiveness of the framework for design problems where the use of just a high-fidelity model is infeasible. This solution quality-computational efficiency tradeoff is contextualized by visualizing the exploration behavior of the design agents.