The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and otherwireless devices is capable of sensing the activities being carried around Industrialenvironment so as to automate industrial processes. In almo...The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and otherwireless devices is capable of sensing the activities being carried around Industrialenvironment so as to automate industrial processes. In almost every industry, employeeperformance appraisal is done manually which may lead to favoritisms. This paperproposes a framework to perform automatic employee performance appraisal based ondata sensed from IoT. The framework classifies raw IoT data into three activities (Positive,Negative, Neutral), co-locates employee and activity in order to calculate employeeimplication and then performs cognitive decision making using fuzzy logic. From theexperiments carried out it is observed that automatic system has improved performance ofemployees. Also, the impact of the proposed system leads to motivation among employees.The simulation results show how fuzzy approach can be exploited to reward or penalizeemployees based on their performance.展开更多
Fused deposition modeling （FDM） is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This arti...Fused deposition modeling （FDM） is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empiri- cally reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thicknes, of acrylonitrile-butadiene- styrene （ABSP 400） parts fabricated using the FDM tech- nique. It was found that contraction prevailed along the directions of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi＇s parameter design. Because Taguchi＇s philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The pre- dicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.展开更多
文摘The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and otherwireless devices is capable of sensing the activities being carried around Industrialenvironment so as to automate industrial processes. In almost every industry, employeeperformance appraisal is done manually which may lead to favoritisms. This paperproposes a framework to perform automatic employee performance appraisal based ondata sensed from IoT. The framework classifies raw IoT data into three activities (Positive,Negative, Neutral), co-locates employee and activity in order to calculate employeeimplication and then performs cognitive decision making using fuzzy logic. From theexperiments carried out it is observed that automatic system has improved performance ofemployees. Also, the impact of the proposed system leads to motivation among employees.The simulation results show how fuzzy approach can be exploited to reward or penalizeemployees based on their performance.
文摘Fused deposition modeling （FDM） is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empiri- cally reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thicknes, of acrylonitrile-butadiene- styrene （ABSP 400） parts fabricated using the FDM tech- nique. It was found that contraction prevailed along the directions of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi＇s parameter design. Because Taguchi＇s philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The pre- dicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.
