Abstract
For decades scientists have leveraged computers to solve the complex chip-formation problem — the hope is that one day the computer will be able to solve every technological problem such as modeling of large plastic deformation occurring under friction conditions. Despite the significant number of modeling and simulation studies, there is still limited knowledge about the real nature of the chip formation process. Until today, better agreement with practical results is still being sought-after. Analyzing this situation, some scientists founded the industry 4.0 project to create new opportunities and benefits for industry. In manufacturing, this initiative requires data integration from CAD to CAM with associated Quality Management (QM), and leveraging advanced analytics such as artificial intelligence (AI) with machine learning (ML) to solve complicated problems. In metal cutting, one such complicated problem is modeling of chip formation. Prior to leveraging AI, or rather to create the premise for a physics-based approach, the phenomena occurring during chip formation need better and more realistic modeling. This paper is presenting advances in physics-based modeling of chip formation with particularization for AlMg5. Adequate process mechanics will be developed resulting in modeling of chip-formation and simulation of chip flow. Moreover, it could be shown that the theoretically developed cutting result is in good agreement with the existing experimental result. There is only one disadvantage during production in the machining cell — the generated chip is very long and it is blocking the production process. Further investigation was necessary. A chip breaker was developed as a function of the existing production spectrum with individual geometry and kinematics. For different cutting conditions and tool geometry considerations for a physics-based machine learning process are proposed.
