An empirical study to evaluate machine tool production readiness and performance

Deshpande, Amit

doi:10.1007/s00170-012-4086-3

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…NC machine tool is an important foundation for modern manufacturing, which directly impacts the economic development [1,2]. With the continuous development of modern manufacturing technology, NC machine tool has been widely used in aerospace, automobile, shipbuilding, and other industries [3,4]. In the manufacturing process, the continuous degradation of ball screw directly leads to the decrease of NC machine tool's static geometric accuracy and dynamic precision, which means that enterprises should increase their production and maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

Zhang

Gao

Huang

2015

Shock and Vibration

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To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA) and dynamic fuzzy neural network (DFNN) is studied. The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object. Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance. First, screw vibration signal features are extracted both in time domain and frequency domain. Then the feature vectors can be obtained by principal component analysis (PCA). Second, the initialization parameters of the DFNN are optimized by means of QGA. Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model. The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.

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