Additive manufacturing (AM) technologies, commonly called 3D printing, provide a good prospect for medical applications because complex geometries and customized parts can be fabricated to meet individual patient needs. Orthopedic implants are a group of medical parts with high relevance for AM. Today, most metal 3D-printed implants are manufactured through metal powder bed fusion technologies, which include direct metal laser sintering (DMLS), selective laser melting (SLM), and electron beam melting (EBM). Common materials used for implants are titanium alloys, cobalt chromium (CoCr), and stainless steel. These materials are used because of their mechanical properties and biocompatibility. Surface finishing is often required for 3D-printed implants to meet the desired surface quality for patient needs. My research focuses on optimizing abrasive machining of additively manufactured CoCr veterinary orthopedic implants considering human-in-the-loop. The literature review shows a knowledge gap in controlling the surface finishing parameters for medical grade CoCr materials. My research aims to investigate the grinding parameters for CoCr (grinding force, feed rate, cutting speed, and material removal rate), relating surface quality parameters to grinding parameters for AM CoCr, and designing a specialized platen/ black plate for the abrasive belt grinding process, as well as considering human knowledge during grinding operations. Experiments were conducted on AM CoCr samples. In a manual grinding process, the user feels and adjusts the normal force based on the condition of belt wear and machine vibration. How the user controls these parameters in a manual grinding process is poorly understood. One of the downsides of manual grinding is that the depth of cut is difficult to set as an input parameter compared to the semi-automated grinding process. A semi-automated grinding machine was used to experiment by setting a constant or varying feed rate, constant depth of cut, and spindle speed. Furthermore, when complex parts are made, abrasive manufacturing relies heavily on a manual process and human skilled knowledge. Most implant manufacturers do not disclose how the implants are finished to meet the desired surface requirement. This research also aims to close the loop in considering human skilled knowledge and its effect on grinding. To fully understand and optimize the abrasive machining process, the sample geometry was simplified to rectangular (3.0 in x1.5 in) and curved (3.0 in x 2.0 in radius) samples. An electrical discharge machine (EDM) cut the samples into smaller sizes. Input parameters such as spindle speed (1000 rpm, 2000rpm, and 3000 rpm) and feed rate (2.75mm/min, 5.5 mm/min, and 8.25 mm/min), Silicon carbide (SiC) abrasive grinding belt type (grit size: #60, #180, #400) were changed in the grinding experiments. The experiments were conducted in a randomized fashion with at least three repetitions. The grinding force (normal and tangential) was measured. The output parameters were analyzed, such as grinding ratio, material removal rate, grinding energy, and surface quality parameters. Based on the analysis and results, the 3000 rpm spindle speed, the 2.75 mm/min feed rate, and the #180 grit belt provided the most optimized data value for the grinding normal and tangential force (10.7 N and 5.7 N), grinding ratio (0.5), average surface roughness (Ra is 0.1µm), specific grinding energy (9.9 J/mm^3), and specific material removal rate ( 0.4 mm^3/mm*s) as they met the targeted Ra (0.3 µm) for surface finishing requirement for CoCr implants and have low grinding energy. After each experiment, the grinding swarf was collected and analyzed using a scanning electron microscope (SEM). The SEM images were processed to measure the undeformed chip/swarf thickness (hcu). The swarf shapes were classified, such as Flow, Shear, Peeling, Built-Up Edge, Spherical, and Melted chips, and manually counted. The total number of undeformed chips/swarf was approximately 3162. Based on the manual count, 40% were flow, 14% were shear, 11% were peeling, 7% were build-up edges, 22% were melted, and 6% were spherical/uncut chips.