Diagnostics for geometric performance of machine tool linear axes

Vogl, Gregory W.; Donmez, M A.; Archenti, Andreas

doi:10.1016/j.cirp.2016.04.117

Cited by 33 publications

(25 citation statements)

References 8 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, abrasive wear, pitting and cracks can cause the loss of preload of the ball screw's nut and lead to lower stiffness. This lowers position accuracy, which eventually causes scraped workpieces [5], [6], [7], [8].…”

Section: A Ball Screw Preload and Degradationmentioning

confidence: 99%

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Benker

Kleinwort

Zäh

2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

View full text Add to dashboard Cite

Ball screws are key components in machine tool linear feed drives since they translate the motors' rotary motion into linear motion. With usage over time, however, tribological degradation of ball screws and the successive loss in preload can cause imprecise position accuracy and loss in manufacturing precision. Therefore condition monitoring (CM) of ball screws is important since it enables just in time replacement as well as the prevention of production stoppages and wasted material. This paper proposes an idea based on a probabilistic classification approach to monitor a ball screw's preload condition with the help of modal parameters identified from vibration signals. It will be shown that by applying probabilistic classification models, uncertainties with respect to degradation can be quantified in an intuitive way and therefore can enhance the basis of decision making. Furthermore, it will be shown how a probabilistic classification approach allows the estimation of remaining useful life (RUL) for ball screws when the user only has access to discrete preload observations.

show abstract

Section: A Ball Screw Preload and Degradationmentioning

confidence: 99%

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Benker

Kleinwort

Zäh

2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

View full text Add to dashboard Cite

show abstract

“…Fig. 2a shows the configuration of the setup including an IMU on the carriage of the linear axis system [16]. As seen in Fig.…”

Section: Experiments For Localized Damagementioning

confidence: 99%

“…Previous work has shown the use of an IMU [15,16] that processes accelerometer and rate gyroscope data to detect changes in the translational and angular error motions due to axis degradation [16,17]. A small calibrated IMU can be permanently integrated within a machine tool; no setup change is required to measure the changing geometric performance.…”

Section: Introductionmentioning

confidence: 99%

“…A small calibrated IMU can be permanently integrated within a machine tool; no setup change is required to measure the changing geometric performance. During pauses in production, an axis is commanded to move back and forth at various speeds [16], and the IMU data can be processed to yield metrics and the root causes of performance degradation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Root‐cause analysis of wear‐induced error motion changes of machine tool linear axes

Vogl

Jameson

Archenti

et al. 2019

International Journal of Machine Tools and Manufacture

Self Cite

View full text Add to dashboard Cite

Manufacturers need online methods that give up-to-date information of system capabilities to know and predict the performance of their machine tools. Use of an inertial measurement unit (IMU) is attractive for on-machine condition monitoring, so methods based on spatial filters were developed to determine rail wear conditions of linear guideways of a carriage from its IMU-based error motion. Rail wear-induced changes in translational and angular error motions as small as 1.5 μm and 3.0 microradians, respectively, could be resolved. A corresponding two-part root-cause analysis procedure was developed to determine the rail locations of error motion degradation as well as the most probable physical location of damage that causes the detected error motion changes. Another analysis method determined the root cause of non-localized damage along each rail. These approaches support the development of smart machine tools that provide actionable intelligence to manufacturers for early warnings of system degradation.

show abstract

“…Smart tooling and smart machining have tremendous potential and are drawing attention as one of next generation precision machining technologies particularly in the Industry 4.0 context [1][2][3][4][5]. In modern advanced manufacturing, it is becoming an essential trend for machining components with an ever increasing dimensional/form accuracy and finer surface roughness, even surface functionality requirement.…”

Section: Introductionmentioning

confidence: 99%

Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

Cheng

Niu

Wang

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Smart machining has tremendous potential and is becoming one of new generation high value precision manufacturing technologies in line with the advance of Industry 4.0 concepts. This paper presents some innovative design concepts and, in particular, the development of four types of smart cutting tools, including a force-based smart cutting tool, a temperature-based internally-cooled cutting tool, a fast tool servo (FTS) and smart collets for ultraprecision and micro manufacturing purposes. Implementation and application perspectives of these smart cutting tools are explored and discussed particularly for smart machining against a number of industrial application requirements. They are contamination-free machining, machining of tool-wear-prone Si-based infra-red devices and medical applications, high speed micro milling and micro drilling, etc. Furthermore, implementation techniques are presented focusing on: (a) plug-and-produce design principle and the associated smart control algorithms, (b) piezoelectric film and surface acoustic wave transducers to measure cutting forces in process, (c) critical cutting temperature control in real-time machining, (d) inprocess calibration through machining trials, (e) FE-based design and analysis of smart cutting tools, and (f) application exemplars on adaptive smart machining.

show abstract

Diagnostics for geometric performance of machine tool linear axes

Cited by 33 publications

References 8 publications

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Root‐cause analysis of wear‐induced error motion changes of machine tool linear axes

Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

Contact Info

Product

Resources

About