Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

“…Among them, scouters are from producers and scroungers randomly select a number of individuals to serve. (2) The fitness values of the initial population were calculated, and the fitness values of the optimal and worst were sorted and selected. (3) After each iteration, the positions of the producers, scroungers, and scouters were updated by calculating the population fitness values.…”

“…The spindle speed was 1.04 × 10 4 r min −1 , the feed rate was 1.555 × 10 3 mm min −1 , the axial cut depth was 0.2 mm, and the radial cut depth was 0.125 mm. A threeway dynamometer (Kistler 9265B) and three piezoelectric sensors (Kistler 8636 C) installed on the machine tool were In addition, after finishing each machining stroke, the wear values of the milling cutters were measured offline using the LEICA MZ12 microscope and expressed as a flute (1,2,3) . To reflect the current wear condition of the milling tool more accurately, the average wear value of flute (1,2,3) was taken as the experimental target.…”

“…A threeway dynamometer (Kistler 9265B) and three piezoelectric sensors (Kistler 8636 C) installed on the machine tool were In addition, after finishing each machining stroke, the wear values of the milling cutters were measured offline using the LEICA MZ12 microscope and expressed as a flute (1,2,3) . To reflect the current wear condition of the milling tool more accurately, the average wear value of flute (1,2,3) was taken as the experimental target. The wear values and mean values of the flute (1,2,3) with changes in the cutting times are shown in figure 4.…”

“…The cost and production cycle will rise with diligent tool changes [1]. Relevant research has demonstrated that suitable tool change intervals or countermeasures can successfully save downtime by 75% and boost production efficiency by 10% -60% [2]. Therefore, tool wear monitoring technology is an important guarantee to prevent excessive tool wear or fracture and other factors leading to lower workpiece quality and efficiency, and is a helpful way to reduce production costs.…”

Modulation signal bispectrum analysis of motor current signals for online monitoring of turning conditions

“…5,6 However, this approach necessitates interrupting the processing for image acquisition, limiting its applicability for online monitoring. 7 In contrast, the indirect method involves monitoring the state of the processing system by analyzing the interrelationships among various parameters, such as acoustic emission, 8,9 vibration, 10 and sound. 11 Vibration sensors, commonly installed on the workpiece, are widely used in this context, but their signal amplitude varies over time during processing, adding to the complexity of the analysis.…”