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Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.

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The perception of angular head position with respect to a visual object was investigated using three different methods: Pointer indication (P); in the dark, subjects' (Ss') heads were horizontally turned to various positions (range +/- 54 degrees); Ss then rotated a pointer carrying a light emitting diode (LED) so as to align it with head position. Active head pointing (A); again in darkness, the pointer was rotated to various positions; Ss then turned their heads so as to align them with the pointer. Reading from visible scale (V); Ss viewed a degrees scale on a circular screen; Ss' heads were turned as in P, and Ss reported head position in terms of this scale. The results obtained with all three methods indicate that head position is overestimated with respect to the visual object (LED, scale mark): object position exceeded head position by 6, 18, and 7% when measured with the P-, A-, and V-methods, respectively (median values). The observed misalignment between head and object suggests that subjective primary eye position is shifted in the direction of head rotation by a cross-talk of head position. Whether a functional advantage or merely a tolerated side-effect of other constraints, this behavior confers the impression of looking "straight ahead" in the literal sense when gaze is shifted by coordinated eye-head movements with a 10% eye and a 90% head share in total lateral displacement. In an attempt to probe a hypothesized internal representation of head position implied in head-to-object alignment, Ss were also to estimate head position in space using only neck proprioceptive information. In complete darkness, responses were often non-linear functions of head position with overestimation of large eccentricities. When a head-centered LED was added (which conveyed no position information), responses became more linear, suggesting that the mere presence of visual afferents may improve the perceptual interpretation of proprioceptive information.