Performance Analysis of a Flexible Manufacturing System (FMS)

Mahmood, Kashif; Karaulova, Tatjana; Otto, Tauno; Shevtshenko, Eduard

doi:10.1016/j.procir.2017.03.123

Cited by 39 publications

(16 citation statements)

References 6 publications

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“…A machine tool integrates three essential elements: the machine and controllers, the part to be processed, and the tool. Their basic elements are the machine base, parts handling devices, tools and automatic tool changer, drive units and controls, and logic-programmable commands and sensors (Mahmood et al, 2017). FMS workstations include at least one CNC machine tool equipped with a tool magazine, performing multiple sequential operations (milling, drilling, etching, etc.).…”

Section: Components Of An Fmsmentioning

confidence: 99%

Implementation of a Flexible Manufacturing System in a production cell of the automotive industry: decision and choice

Sellitto

Mancio

2019

Prod.

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Section: Components Of An Fmsmentioning

confidence: 99%

Implementation of a Flexible Manufacturing System in a production cell of the automotive industry: decision and choice

Sellitto

Mancio

2019

Prod.

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“…The storage system, such as an intermediate buffer, is not considered in this article since the trend of FMS is to reduce or even cancel the cache area [45]. An FMS is capable of manufacturing various kinds of product families through CNC machine tools, which are loaded and unloaded by robots [46]. Therefore, the first step in the multistate modeling of FMS is to establish a multistate model of CNC machine tools.…”

Section: Multistate Model Of the Flexible Manufacturing Systemmentioning

confidence: 99%

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

et al. 2019

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This article presents a performance evaluation of a flexible manufacturing system (FMS), in which the system and its components can have different performance levels ranging from perfect functioning to total failure. An original multistate transition process for the FMS’s main component, Computerized Numerical Control (CNC) machine tools, proposed in the current article and treated as an aging process because of its increasing failure rate is investigated. The Markov reward approach is utilized to analyze the availability of CNC machine tools and sensitivity analysis of each non-operational state to the machine’s downtime. To overcome the “state explosion problem” in the subsequent FMS performance evaluation process, an application of the Lz-transform method is proposed to determine the performance evaluation and availability of such aging multistate FMS. An illustrated case is presented to demonstrate the performance evaluation process. The results show that the suggested methods can be implemented in engineering decision making and construction of various aging multistate FMS related to demands, availability, and output performance.

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“…Steffen [84] has presented a survey of AI based scheduling systems. Kusiak and Chen [85] have also reviewed a number of AI-based scheduling approaches.…”

Section: Artificial Intelligence Approachmentioning

confidence: 99%