My decision to choose industry-oriented research as a professional career in the long term is based on moral values, outlook, and personal experiences. I always had a passion for learning how machines work and the evolution of new technologies from school days. This motivated me to choose Mechanical Engineering as a discipline during undergraduate studies. The laboratories of the undergraduate program were the most exciting part and helped me to develop an interest towards manufacturing processes and systems. I spent considerable time appreciating automation trends and CNC machine tools during undergraduate and postgraduate studies. Considering long-term career interests in the domain of advanced manufacturing, I opted for a doctoral degree program in the area of CNC Machine Tools and CAD/CAM at Indian Institute of Technology Jodhpur, India after postgraduate studies. The doctoral degree program helped me to transform from an enthusiastic student to a thoughtful researcher and appreciate the multidisciplinary nature of current research trends. I have developed good knowledge base in the domain of advanced machining, geometric tolerances, and application of machine learning in manufacturing. In subsequent years of professional career, I look forward to contribute meaningfully in the areas integrating smart manufacturing and data sciences.

Stochastic Machining

The concept of stochastic machining aims to introduce stochastic nature to the toolpath. The toolpath strategy involves random movement of the tool over the surface. It is hypothesized that the random motion of the tool avoids resonance by continuously varying the direction of cutting force and thereby reduces chatter. Also, the tool often passes over already machined regions of the workpiece and enables rapid dissipation of heat. The stochastic toolpath strategy devised herein is applied to generate toolpath for 3-axis ball-end milling of 2-Dimensional (2D) and free form surfaces.

Machining of Thin-walled Components

• Predictive Framework for Estimation of Geometric Tolerance

A computational framework is realized to estimate static tool and workpiece deflection-induced geometric tolerances during end milling of thin-walled straight and constant curvature or circular components. The framework requires systematic integration of several computational models to predict cutting forces, estimate coordinates representing distorted machined surface due to the tool and workpiece deflections, and a mechanism to transform distorted coordinates into geometric tolerances for straight and circular thin-walled components.

• Control of Geometric Tolerances

The rigidity of the thin-walled component varies considerably with the change of workpiece curvature and reduces as machining progresses due to material removal. This variation leads to a violation of geometric tolerances envisaged by the designer. The research work devised a Rigidity Regulation Approach (RRA) to obtain the semifinished geometry at the end of roughing operation. The finish cutting sequence is performed subsequently on the geometry for achieving optimal geometric tolerances.

Journal

• Ankit Agarwal, K.A. Desai, Effect of component configuration on geometric tolerances during end milling of thin-walled parts, The International Journal of Advanced Manufacturing Technology, 2021.
• Ankit Agarwal, K.A. Desai, Rigidity regulation approach for geometric tolerance optimization in end milling of thin-walled components, ASME Journal of Manufacturing Science and Engineering, 143(11), 111006, 2021.
• Ankit Agarwal, K.A. Desai, ‘Modeling of flatness error in end milling of thin-walled components’, Proceedings of the IMechE, Part B: Journal of Engineering Manufacture, 235(3), 543-554, 2021.
• Ankit Agarwal, K.A. Desai, ‘Predictive framework for cutting force induced cylindricity error estimation in end milling of thin-walled components’, Precision Engineering, 66, 209-219, 2020.
• Ankit Agarwal, K.A. Desai, ‘Importance of bottom and flank edges in cutting force models for end milling operation’, The International Journal of Advanced Manufacturing Technology, 107(3), 1437-1449, 2020.
• Shubham Vaishnav, Ankit Agarwal, K.A. Desai, ‘Machine learning based instantaneous cutting force model for end milling operation’, Journal of Intelligent Manufacturing, 31(6), 1353-1366, 2020.
• Neha Arora, Ankit Agarwal, K.A. Desai, ‘Modeling of static surface error in end milling of thin-walled geometries’, International Journal of Precision Technology, 8(2-4), 107-123, 2019.

Conferences

• Ankit Agarwal, K.A. Desai, ‘Amalgamation of physics-based cutting force model and machine learning approach for end milling operation’, 53rd CIRP Conference on Manufacturing Systems (CIRP CMS), Chicago, USA, July 2020 Procedia CIRP, 93, 1405-1410.
• Ankit Agarwal, K.A. Desai, ‘Tool and workpiece deflection induced flatness errors in milling of thin-walled components’, 53rd CIRP Conference on Manufacturing Systems (CIRP CMS), Chicago, USA, July 2020 Procedia CIRP, 93, 1411-1416.
• Ankit Agarwal, K.A. Desai, ‘Effect of workpiece curvature on axial surface error profile in flat end milling of thin-walled components’, 48th SME North American Manufacturing Research Conference, (NAMRC 48), Cincinnati, USA, June 2020 Procedia manufacturing, 48, 498-507.
• Neha Arora, Ankit Agarwal, K.A. Desai, ‘Modeling of static surface error in end milling of thin-walled geometries’, 10th International Conference on Precision, Meso, Micro and Nano Engineering (COPEN 10), IIT Madras, December 2017.