DescriptionThe miniaturization of devices has been under high demand since they offer added benefits such as high mobility and portability, better accessibility and functionality, and lower energy consumption. Specific applications include energy devices such as heat sinks and exchangers, biomedical devices such as microfluidic devices, microneedles, and implants, automotive and aircraft components, and sensory devices. As the demand to produce such miniature products continue to increase, an imminent need for advanced manufacturing processes that can fabricate very small parts directly, cost effectively, and with high productivity arises. Micro-end milling is one of the most promising manufacturing processes capable of fabricating discrete parts with complex features in micro-scale (feature size < 1000 µm) due to its high flexibility for processing a wide range of materials with a low setup cost. However, micro-end milling process possesses several difficulties in precision fabrication of such products due to size effect, rapid tool wear, burr formation, tool and workpiece deflection, and premature tool breakage. In addition, these micro-products require tighter geometrical tolerances and better surface quality. These difficulties and requirements make the selection of process parameters for high performance micro-end milling very challenging. In this research, we conducted experimental and numerical modeling studies and multi-objective process optimization for micro-end milling. An extensive study of process parameters such as tool coatings, cutting velocity, feed rate, and axial depth of cut was performed in order to understand the effects of these parameters on the performance of micro-end milling process. Novel finite element based process models in 2-D and 3-D have been developed. Both experimental models and finite element based process simulations were utilized to construct various predictive models for the process outputs. These predictive models include physics-based outputs such as chip deformations, tool forces and temperatures, tool wear rate and depth, as well as performance related measures such as surface finish, burr formation, and tool life. Furthermore, we developed a comprehensive decision support system by using the predictive models which can facilitate a selection of process parameters and toolpath strategies based on desired performances. Multi-objective optimization studies were conducted by utilizing predictive models for obtaining optimal decision variable sets. Moreover, this research also demonstrates the current capabilities of micro-end milling in fabricating micro-products such as heat sinks in brass and implants in titanium alloys, and micro-needles in polymers.