Journal of Management and Engineering Integration, v.13 no.1

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    Journal of Management & Engineering Integration, v.13, no.1 (Summer)
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-06) Association for Industry, Engineering and Management Systems (AIEMS)
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    A critical analysis of the AIAG-VDA FMEA; does the newly released AIAG-VDA method offer improvements over the former AIAG method?
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-06) Kluse, Christopher
    After several years of collaboration, The Automotive Industry Action Group (AIAG) and The German Association of the Automotive Industry (VDA) completed a harmonized version of their respective FMEA methods. Previous to the joint initiative, each organization had their own Failure Modes and Effects Analysis (FMEA) recommended methods & guidelines. The differing methods posed confusion among those automotive suppliers that supplied both markets. As a result, the AIAG & VDA FMEA 1st edition Handbook (2019) was created to mitigate the confusion, improve the overall FMEA development process, and to improve efficiency developing accurate, robust and effective FMEA's. The AIAG-VDA 1st edition (2019) may or may not have improved the method or offered efficiency gains to the FMEA development process. Early critique of the new method offered by Harpster (2018) stated "If the AIAG-VDA DFMEA methodology (which uses structure, function, and failure analyses to determine critical DFMEA content) is implemented, it will result in an automotive DFMEA process that is both considerably less effective and much more inefficient than the current methodology described in the AIAG 4th Edition FMEA manual". This result of this research is a critical, holistic review of the recently published (June 2019) AIAGVDA FMEA Handbook along with summarized advantages and disadvantages associated with the new automotive FMEA development process. While the new FMEA AIAG-VDA process offers several advantages, and results in a more robust FMEA, the increased complexity, need for dedicated software, and the learning curve associated with the new process may represent a significant barrier to improving the FMEA development process in the automotive community.
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    A probabilistic dynamic programing model for determining optimum inventories
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-06) Wang, John; Zhou, Steve Bin; Pei, Zhi
    The occurrence and size of snowfall, and therefore, the level of demand for salt, are uncertain for each week. Therefore, a probabilistic inventory model is required. This will be a periodic review model - the inventory of salt at the beginning of each week will determine the optimal quantity to be purchased to satisfy expected demand. Dynamic programming with a backward recursion is utilized to determine weekly optimal costs/inventory level.
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    The effectiveness of Smart Compose: An artificial intelligent system
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-06) Gnacek, Matt; Doran, Eric; Bommer, Sharon; Appiah-Kubi, Philip
    The growth of artificial intelligence (AI) technologies in everyday life and manufacturing are expected to reduce the mental workload of a user or human operator and increase their efficiency. In industrial systems, such as additive manufacturing (AM), as AM transitions from a technology of manufacturing prototypes to rapid manufacturing, it is important that these added technologies reduce an operator's mental workload, have high user satisfaction, and are easily implemented and incorporated into the operator's tasks. One growing AI technology is Smart Compose, an artificially intelligent system that provides writing suggestions when composing an email through Gmail. Like other AI technologies, the goal of Smart Compose is to enhance the performance of a user; in this case, typing an email. It is hypothesized that Smart Compose increases a user's performance when typing an email. Thus, the objective of this study is to test the capability of Smart Compose and whether it increases human performance and decreases mental workload for college students. The study found that Smart Compose does not significantly increase human performance and does not significantly decrease mental workload for college students.
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    Using tolerance interval method as an alternate approach for monitoring process performance (process capability) of surface roughness of gear tooth flanks to avoid grinding burns
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-06) Diam, Akaff S.; Zhang, Julie Z.; Kashef, Ali E.
    The process capability measures most commonly referred to as process capability indices (PCIs), Cp, Cpk, Cpm evaluate process yield, process consistency and statistical variation compared to the specification. This paper explores an alternate approach for monitoring the process performance of tooth flank surface roughness in gear tooth grinding to avoid grinding burn. The gear tooth flanks are susceptible to microstructural damage due to high thermal energy generated during the process of grind operation. The thermal damage in ground gears can cause catastrophic failure in gears' intended life. To avoid grinding burns the process parameters (feed, speed and depth of cut) are selected such that thermal damage should not occur, however, setting up such parameters could affect the process capability of surface roughness (finish) at a desired level. The surface roughness is commonly specified in design as unilateral tolerance and considered as a critical or key characteristic, therefore, most of the automotive or component manufacturing industry requires it to exhibit a process capability index (Cpk) of 1.33 or above. Achieving and maintaining process capability of surface roughness is difficult due to the nature of process, moreover, the thermal damage of the ground feature can impair the product life. In this paper, for gears that are made with ferrous materials (plain carbon steels or alloy steels), tolerance interval method is proposed for monitoring the process performance of roughness in gear tooth grinding process as compared to the conventional process capability.