Mrinal Redij

Background

About Mrinal Redij

Quality Engineer | Process Optimization Enthusiast | Data-Driven Problem Solver Enhancing Quality & Efficiency Through Innovation. I specialize in quality engineering, automated inspection, and process optimization, leveraging data analytics, machine vision, and continuous improvement techniques to drive efficiency and ensure high manufacturing standards and product reliability. With a dual background in Mechanical & Aerospace Engineering, I thrive at the intersection of quality control, automation, and operational excellence. Industry 4.0 & Smart Manufacturing: Passionate about integrating AI-driven quality control to enhance process automation and real-time decision-making in the manufacturing environment. Tech & Tools: Programming: Julia, Python, MATLAB, R, SQL. Software & Systems: AutoCAD, SolidWorks, HPC, VS Code, Power BI, Mathematica, Microsoft Office. Quality Techniques: Statistical Process Control (SPC), Failure Modes and Effects Analysis (FMEA), Root Cause Analysis (RCA), Corrective and Preventive Actions (CAPA), Quality Management Systems (QMS), 8D Problem-Solving. Recent Experience: Quality Engineering Co-op – Bobcat Optimized automated Pre-Paint ID & Hang Verification System, improving identification accuracy from 85% to 99.7% using Cognex PatMax geometric pattern matching. Designed real-time inspection dashboards in Power BI, enabling data-driven decision-making and process improvements through Root Cause Analysis (RCA) and 8D methodology. Led AI Smart Camera evaluation, comparing multiple vendors and collaborating with cross-functional teams, influencing vendor selection and automation strategy. Deployed Cognex Machine Vision for automated assembly monitoring, ensuring zero downstream defects and improving process efficiency. Developed an inspection station for auger housing seals, reducing manual inspection errors and improving overall quality control. Graduate Research Assistant – Iowa State University Developed Bioenergy with Carbon Capture and Sequestration (BECCS) supply chain optimization models, achieving 99% carbon sequestration potential. Conducted data analysis & visualization using Julia, High-Performance Computing (HPC) systems, and Mathematica. Always excited to connect with fellow engineers, innovators, and problem-solvers! Let’s talk quality systems, continuous improvement, and process optimization!