Artificial Intelligence: Prospect in Mechanical Engineering Field—A Review

Patel, A. R.; Ramaiya, Kashyap K.; Bhatia, Chandrakant V.; Shah, Hetalkumar; Bhavsar, Sanket N.

doi:10.1007/978-981-15-4474-3_31

Cited by 31 publications

(14 citation statements)

References 30 publications

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“…To date, neither FIJI nor TILIA can rapidly and automatically process a huge amount of complicated imaging data, such as luminous signaling from a sympatric population. In recent years, artificial intelligence (AI) on image pattern recognition has been intensively developed for various academic and industrial applications [ 47 , 48 ]. The use of AI in flash signal analysis and firefly species recognition is in development [ 27 ], and we anticipate that it will become a future trend.…”

Section: Discussionmentioning

confidence: 99%

Species-Specific Flash Patterns Track the Nocturnal Behavior of Sympatric Taiwanese Fireflies

Goh

Lee

Wang

2022

Biology

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It is highly challenging to evaluate the species’ content and behavior changes in wild fireflies, especially for a sympatric population. Here, the flash interval (FI) and flash duration (FD) of flying males from three sympatric species (Abscondita cerata, Luciola kagiana, and Luciola curtithorax) were investigated for their potentials in assessing species composition and nocturnal behaviors during the A. cerata mating season. Both FI and FD were quantified from the continuous flashes of adult fireflies (lasting 5–30 s) via spatiotemporal analyses of video recorded along the Genliao hiking trail in Taipei, Taiwan. Compared to FD patterns and flash colors, FI patterns exhibited the highest species specificity, making them a suitable reference for differentiating firefly species. Through the case study of a massive occurrence of A. cerata (21 April 2018), the species contents (~85% of the flying population) and active periods of a sympatric population comprising A. cerata and L. kagiana were successfully evaluated by FI pattern matching, as well as field specimen collections. Our study suggests that FI patterns may be a reliable species-specific luminous marker for monitoring the behavioral changes in a sympatric firefly population in the field, and has implication values for firefly conservation.

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Section: Discussionmentioning

confidence: 99%

Species-Specific Flash Patterns Track the Nocturnal Behavior of Sympatric Taiwanese Fireflies

Goh

Lee

Wang

2022

Biology

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Section: Methodsmentioning

confidence: 99%

“…ANN is a subsystem of ML computing systems. The concept of ML is the ability of the computer to understand the structure of data by using a mathematical or statistical model (Patel et al, 2021). Details for ANN are well established and are provided in a number of references (Oreški and Androc ˇec, 2020;Abdolrasol et al, 2021;Kotyrba et al, 2022).…”

Section: Modeling and Optimizationmentioning

confidence: 99%

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Hashemi Baghi,

Mansour

2024

RPJ

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Purpose Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

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“…The main components of Industry 4.0 related to engineering are the internet of things (IoT), data analytics, engineering simulation using virtual reality and augmented reality, manufacturing optimization and sustainable processes to reduce energy consumption [1]- [3]. Through IoT systems, an engineer may be able to gather large amounts of data from sensors and processes, which can be used for the optimization of manufacturing processes, and for real-time monitoring and control applications [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Configuration of an IoT microhydraulic power generation system for education

Rico

Cruz-Alcantar

Pineda-Rico

et al. 2022

IJEECS

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Internet of things (IoT) involves the communication of all kinds of things embedded with sensors, electronics, software and people connected to the internet. Knowledge of IoT in the classroom provides an experience for engineering students to explore different career options. Under this scope, an IoT platform on the Arduino UNO and Raspberry Pi 3 development boards was built for academic purposes. The IoT platform was configured to monitor a microhydraulic power generation system used for the study of small-scale hydraulic power production, using a hydraulic head provided by a system of hydraulic pumps in series and/or parallel connection. The platform was designed considering a monitoring station for the acquisition of analog, digital, SPI and PWM data; a control station that receives data from the monitoring station and sends data to the cloud. The communication between modules was established using a publication/subscription system. The platform allows to registrate, visualize and process data directly in the cloud. Meaning that the IoT systems connected to this platform can be monitored from a cell phone, tablet or PC with internet access, promoting immediate access to the emerging information generated in the operating system.

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Artificial Intelligence: Prospect in Mechanical Engineering Field—A Review

Cited by 31 publications

References 30 publications

Species-Specific Flash Patterns Track the Nocturnal Behavior of Sympatric Taiwanese Fireflies

Species-Specific Flash Patterns Track the Nocturnal Behavior of Sympatric Taiwanese Fireflies

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Configuration of an IoT microhydraulic power generation system for education

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