Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Abstract

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the denoised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

“…This paper presents the EMDNN model, which is mainly used to detect and reconstruct the weak signals in strong noise background, and reduce the problem of mode mixing [ 13 , 14 ] (when the signal is screened, some IMF components with different time scales will appear, which is called mode mixing). This method decomposes the modal function from high-frequency to low-frequency distribution [ 15 ], thereby reducing the loss of effective information. By applying this method to weak signal processing and reconstruction, we cannot only get rid of the constraints of weak signal linearity and stationarity but also achieve good accuracy in both time and frequency [ 16 ].…”

Synergy of computers, cognition, communication and control with industrial applications

“…The synergy of CCCC supports the increasingly demanding performance specifications of these applications and helps to face special situations like unexpected condition adaptations, human interaction challenges, and goal conflicts. Practical industrial applications of the synergy of CCCC are cyber-physical systems [1][2][3][4][5], networked control systems [6][7][8][9][10], mechatronics systems [11][12][13][14][15], online quality control of production items [16][17][18][19][20], supervision and failure analysis of dynamically changing machine states [21][22][23][24], decision support systems [25][26][27][28][29], prediction and control in dynamic production processes [30][31][32], welding processes [33,34], user profiling [35,36], process monitoring [37][38][39], web based control of information management flows [40,41], and resilient control architectures [42][43][44]…”