This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics.

Gears are integral components of industrial machines that are used for converting power and motion in different industrial processes. Effective monitoring of gear health and early detection of their faults is essential. It assists in averting sudden shutdowns and, in addition, reduces the costs of maintenance while at the same time increasing the efficiency of the production process. This paper proposes an approach for gear fault prognostics and condition monitoring under variable speed and flexible load conditions using vibration data. The approach involves the utilization of Artificial Neural Network (ANN) in combination with signal processing techniques to analyze the gear vibrations in induction motors (IMs) with the aim of establishing the efficiency of the gear system in different conditions of variable loads and speeds. This real-time data is recorded from the two sensors that are fixed on the exterior of the induction motor horizontally and vertically. In addition, this methodology consists of data collection, data cleaning, and data preprocessing, enhancing the ability of deep learning models, leading to accurate predictions and classification of gear faults. The models are tested and evaluated for different evaluation metrics and prove to be very effective in monitoring the overall health of gears and detecting their faults. The variable load and variable speed models were able to achieve accuracies of 96.44% and 93.82% respectively. The precision, recall, and F1 score are 0.97, 0.96, and 0.96 for the variable load model, while 0.96, 0.94, and 0.94 for the variable speed models.

Genetic Algorithm is an optimization technique inspired by nature. The technique has been used by scientists and engineers for real-life search and optimization problems. This work makes use of the genetic algorithm for the solution of the traveling salesman problem. This work focuses on real-time problems; the algorithm is used to find the optimum path for sales travelers inside the Khyber Pakhtunkhwa Province of Pakistan. The solution provides the shortest distance between the cities to be traveled and gives the optimal route. The coding is done in Python-3 and software is developed for the traveling salesmen, where the salesmen can select the cities, they want to travel, and the software will provide the optimal path and the distance.