Machine Learning for Object Recognition in Manufacturing Applications

Yun, Huitaek; Kim, Eunseob; Kim, Dong Min; Park, Hyung Wook; Jun, Martin Byung‐Guk

doi:10.1007/s12541-022-00764-6

Cited by 17 publications

(3 citation statements)

References 163 publications

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“…Deep learning is able to learn high-level abstract representations of data through multi-level nonlinear transformations. ML and DL share many common applications, such as image recognition, [1][2][3][4] object detection, [5][6][7] face recognition [8][9][10][11][12] and other tasks. However, they differ in some ways.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Zhou,

Song,

et al. 2024

Zeitschrift anorg allge chemie

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Hydrogel flexible sensors are widely used in wearable devices, health care, intelligent robots and other fields due to their excellent flexibility, biocompatibility and high sensitivity. With the development of single sensor to multi‐channel and multi‐mode sensor network, the sensor data also presents the characteristics of multi‐dimension, complex and massive. Traditional data analysis methods can no longer meet the data analysis requirements of hydrogel flexible sensor networks. The introduction of machine learning (ML) technology optimizes the process of data analysis. With the continuous development of multi‐layer neural network technology and the improvement of computer performance, deep learning (DL) algorithm is increasingly used to achieve higher efficiency and accuracy, provides a powerful tool for data analysis of hydrogel flexible sensor, and accelerates the intelligent process of hydrogel flexible sensor equipment. This paper introduces the classification of hydrogel flexible sensors and the working mechanism and common algorithms of ML, and summarizes the application of ML technology to assist hydrogel flexible sensors in data analysis in the fields of health care and information recognition. This review will provide inspiration and reference for integrating ML technology into the field of hydrogel flexible sensors.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Zhou,

Song,

et al. 2024

Zeitschrift anorg allge chemie

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“…Over the past few decades, the technical recognition of surfaces of complex shapes has progressed significantly from the description of surfaces in a visualization of images to a quantitative defined numerical description of the technical specifications of objects. Image recognition, boundary and object recognition are widely used in various fields of industry [1][2][3][4], design [5][6][7][8], life and art [10][11].…”

Section: Introductionmentioning

confidence: 99%

New indicators and standards for measuring of the end mill's helical groove by image processing

Pivkin,

Ershov,

Grechishnikov

et al. 2023

Optical Metrology and Inspection for Industrial Applications X

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The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on image processing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.

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“…This workshop will be introduced in Spring 2023. Examples of the applications of machine learning to manufacturing include anomaly detection to send alarms to the operators or classification algorithms which can be used to develop a dashboard to monitor a tool or the factory floor operation [4], [5].…”

Section: Ideal Conditionmentioning

confidence: 99%

Data Analytics Short Courses for Reskilling and Upskilling Indiana's Manufacturing Workforce

Fiock,

Mohn,

Mack

et al.

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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The power of ML and AI has not been fully realized in the manufacturing sector. One of the major challenges is that the small and medium manufacturers, which account for 98% of the industry, lack the dedicated data analytic workforce. In Indiana, to address this need, partnerships have been established between industry and academia through Wabash Heartland Innovation Network (WHIN) at Purdue University. In collaboration with Ivy Tech Community College, a series of workshops were developed to introduce data analytics, the internet of things, and basic machine learning concepts to local small and large manufacturing companies. This study will describe the first of three short courses geared toward industry workers and professionals. The first short course was on the topic of energy savings and data analytics for Variable Frequency Drives (VFDs). The attendees consisted of 44 participants from 17 manufacturing companies. A final evaluation of the course reports on participants' levels of satisfaction with the course, the major learnings and takeaways, and their institutional support. The evaluation results indicate that the course was a good introduction to VFDs and the large number of applications where VFD data can be used for energy savings, diagnostics, and operations. However, workshop participants wanted more hands-on opportunities to see how the VFD data can be extracted for new motors and many legacy equipment still in use and how various settings can be adjusted.

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Machine Learning for Object Recognition in Manufacturing Applications

Cited by 17 publications

References 163 publications

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

New indicators and standards for measuring of the end mill's helical groove by image processing

Data Analytics Short Courses for Reskilling and Upskilling Indiana's Manufacturing Workforce

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