Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations

Optimization of Cutting Parameters Based on Surface Roughness and Assistance of Workpiece Surface Temperature in Turning Process

“…Many life attributes can be also determined by how well the surface finish is maintained. Many surface roughness prediction systems were designed using a variety of sensors including dynamometers for force and torque (Lin et al, 2001;Azouzi and Guillot, 1996), accelerometers for mechanical vibrations (Abouelatta and Madl, 2001;Choudhury and Sharat, 1993;Jang et al, 1995;Kirby et al, 2006), acoustic emission for (AE) sensors (Sundaram et al, 2007;Sundaram et al, 2008;Collacott, 1975) and current probes for current/power measurement of spindle and feed motors (Sundaram et al, 2008). The purpose of using these sensors in machining processes is to increase part quality while decreasing cost and time of manufacture.…”

Indirect adaptive fuzzy control for industrial robots: A solution for contact applications

“…The design of an in-process surface roughness adaptive control system for a CNC turning operation, using fuzzy-nets modelling and tool vibrations measurements, is presented by Kirby, Chen, & Zhang (2006). Macías-Escrivá, Haber, del Toro, & Hernandez (2013) presented a survey about recent progress on self-adaptive systems.…”

An Experimental Study on Effect of Process Variables on Surface Roughness, Tool Vibrations and Elastic Spring Back in Milling of Aisi 316 Stainless Steel

“…Many authors reported that there is a correlation between surface roughness and vibrations of cutter in any machining process. Kirby et al [8] and Lin SC et al [9] found a strong correlation between vibrations and surface roughness in turning process. In metal cutting, there is a relative motion between work piece and cutting tool.…”