• Miss Poonam N. Gedam
• Prof. U. W. Hore
• Prof. A. R. Pawade

Artificially intelligent robots have become increasingly important in Industrial Technology in recent years. The key thing that robots do today is to do difficult and time-consuming activities in an efficient manner. Many components or pieces of equipment are installed in any industry. Humans handle this equipment, and they frequently keep track of it by recognizing and classifying it for further action. This process takes a long time to complete because we have to visit each and every component, but it does not require human intervention. As a result, vision systems and Intelligent Robots are now used to perform these duties. This research focuses on finding a solution to this issue. This work gives a thorough view of these machine learning and image processing techniques in this research, which can be used to improve the intelligence and classification skills of numerous industrial elements. This paper presented a model which identifies various industrial elements with the help of Ensemble Bagging Supervised Classification Machine Learning algorithms with feature extraction using Resnet-101 pre-trained Deep Convolutional Neural Network Model. The proposed model provided a solution where the industry is hazardous and wants to evolve in automation field to make the process safer and easier.

- Artificial Intelligence, Image Processing, Industrial Component, Machine Learning, Object Detection

"Classification of Industrial Components using Image Processing and Machine Learning Techniques.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.e329-e336, May-2022, Available :http://www.jetir.org/papers/JETIR2205544.pdf

"Classification of Industrial Components using Image Processing and Machine Learning Techniques.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppe329-e336, May-2022, Available at : http://www.jetir.org/papers/JETIR2205544.pdf