Jophin Francis George

Highly motivated IIoT Engineer with a proven track record of continuously expanding knowledge and contributing to employer success. Passionate about staying up-to-date with latest engineering methodologies and techniques, consistently seeking opportunities to enhance development and production efforts. Known for exceeding job expectations through genuine enthusiasm for work, resulting in improved bottom lines for employers.

Operating Room conditions monitoring, product inspection & location tracking in investment casting foundry, This entails monitoring temperature and humidity parameters within a shell room while simultaneously capturing real-time surface temperatures of each shell along with their respective locations. Predictive Maintenance IIoT for CNC machine shopfloor, The project involves identifying critical parts and checkpoints within various machine elements, including hydraulic systems, spindle cooling systems, lubrication systems, etc. Subsequently, different sensors are installed to capture real-time data of critical parameters such as temperature, pressure, vibration, energy, flow, etc. This facilitates condition-based monitoring and prevents breakdowns through predictive maintenance strategies. Traceability with Production routing and process cycle time, This project entails marking DPM codes on castings after they have undergone heat treatment and shot blasting processes, and then retrieving this data into the software system.

Predictive Maintenance

Led predictive maintenance IIoT projects focused on enhancing the reliability of various machine types, including CNC machines, HPDC, plastic injection molding machines, sand foundry lines, and wax press machines. The project involved:

Identifying Critical Components:  Analyzed and pinpointed essential parts and checkpoints across machine elements such as hydraulic and pneumatic power pack systems, spindle cooling systems, and lubrication systems.

Real-Time Data Capture: Installed a range of sensors to monitor critical parameters, including temperature, pressure, vibration, energy consumption, oil quality, air purity, oil levels, and flow rates.

Condition-Based Monitoring: Implemented dashboard and software systems for continuous condition monitoring, enabling early detection of potential issues and facilitating timely interventions to prevent unexpected breakdowns.

Predictive Maintenance Strategies: Developed and applied predictive maintenance strategies customized for machine shop floors and foundries, aimed at optimizing equipment uptime and reducing maintenance costs.

Real-time Overall Equipment Effectiveness (OEE) monitoring:  Implemented advanced data capturing techniques to measure availability, performance, and quality metrics in real-time by collaborating with cross functional teams to design and integrate hardware, develop data processing algorithms, and establish communication protocols.

Traceability for production routing and process cycle time:  Using DPM codes on castings for tracking production processes such as heat treatment, shot blasting, etc., and utilizing traceability software for generating reports, calculating cycle time and throughput time, and enabling future tracking of data.

Product inspection & location tracking in an investment casting foundry:  This involves monitoring temperature and humidity parameters within the shell room while simultaneously capturing real-time surface temperatures of each shell on the shell conveyor. It also includes tracking cycle time, throughput time, and the number of cycles using RFID for effective loading and unloading of shells by the operator. The process utilizes interactive traceability software with live status updates and user input for the loading plan

Real-time inspection system for quality management:  This involves the use of Bluetooth enabled gauges—such as digital calipers, micrometers, and height gauges—along with Bluetooth gateways and inspection control software for data aggregation and report generation