Joyam Jayakumar

1) Topic: - Development of a data-driven process monitoring model with real-time capabilities for precise electrochemical machining (PECM) 2) Preprocessing of process signals 3) Performed feature extraction and feature selection 4) Development of a machine learning models for the prediction of occurrence of short-circuit 5) Validation of the model by using data from additional experimental trials

1) Performing failure mode analysis of industrial machinery using machine learning models to improve predictive maintenance strategies. 2) Implemented condition monitoring through anomaly detection algorithms to improve early fault detection and reduce downtime. 3) Executed feature extraction and feature selection processes using time series Python libraries such as Tsfresh. 4) Development of anomaly detection models 5) Co-authored a research paper for demonstration purposes to potential clients

1) Topic: - Use of supervised learning methods for the process evaluation of PECM 2) Carrying out experimental trials in the PECM machine 3) Pre-processing of raw data for the extraction of pulses 4) Performing feature extraction and feature selection using the Tsfresh library for time series data 5) Visualization of pulses and features to facilitate analysis 6) Development of a machine learning model to classify impulses based on processing stability

1) Generating leads for potential customers through targeted contact. 2) Providing comprehensive information about products and services to potential customers, responding to inquiries and encouraging engagement.

Designing components and working with a team to solve problems in the engine assembly department

Areas of focus: Artificial intelligence and data analytics for engineers, machine learning, deep learning and computer vision.

Areas of focus: Statistics, Mathematics and Mechatronics