No abstract

Manual grinding is an abrasive manufacturing process commonly employed in the automotive, aerospace, and medical industries for deburring, finishing, and engraving operations. Unlike other manufacturing processes in which automation drives constant improvement, the operator’s skill set continues to play a pivotal role in manual grinding. Process parameters such as grinding force and feed rate are dependent on the pressure and manual feed rate provided by the operator as well as the operator’s tool movement during the process. Therefore, it is essential to quantify the manual skills involved in the process in order to develop a real-time feedback system, which can assist the operator for in-process corrective action. Manual operations such as manual grinding have not fully utilized the Industrial Internet of Things yet. This article focuses on developing a robust experimental setup to effectively monitor operator-controlled variables (tool feed rate and tool circumferential speed) and process information variables (grinding force, workpiece acceleration, and grinding power). Experiments are carried out to understand the relationships between the variables and their impacts on process outcomes (surface roughness and material removal rate). In addition, grinding energy is evaluated to improve grinding efficiency and sustainability. The developed test setup consists of a power tool, a piezoelectric force sensor, a motion-tracking–based feed rate sensor, and additional sensors. An alumina sanding band is used to grind aluminum 6061-T6 and hardened steel AISI 416 workpieces. Profilometer and confocal surface measurements are carried out for the test specimens to assess various two-dimensional and three-dimensional surface roughness parameters. Findings derived from the experimental results may lay a foundation for understanding and controlling manual grinding operations and enable their integration in smart and sustainable manufacturing systems.