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The cutting parameters in the part machining process have a great impact on the energy efficiency and economy of the machining system. The cutting parameters in traditional machining are often selected according to the operator's experience, which lack attentions to energy saving and economy. Therefore, a milling process parameter optimization method based on deep reinforcement learning (DRL) is proposed in this paper. Taking the machining cost composed of cutting energy efficiency and machining time cost as the optimization goal, the spindle speed and feed speed under the combination of different cutting depth and cutting width parameters are optimized. Firstly, the machine tool energy consumption model is established by back propagation neural network (BPNN) regression method to realize the continuity of machine tool energy consumption state prediction, and the processing cost model is established as the optimization objective function. Then, the process parameter optimization problem is formally expressed as a Markov decision process (MDP), and the corresponding states, actions, reward functions and constraints are defined. Finally, combined with the machine tool power consumption model and machining cost model, the simulation environment is established, and the BP-TD3 method is proposed to solve the Markov decision problem of milling parameter optimization. Taking the machining center as an example, the aluminum alloy workpiece is milled. Compared with the classical optimization algorithm, the proposed method can save 95 % optimization calculation time, and ensure that the average processing cost after optimization is close to the minimum processing cost obtained by the classical optimization algorithm.

•A Bayesian ridge regression approach was proposed for modeling the natural frequencies variation of ball screw drive system.•Two novel criteria are employed as damage warning, which can reduce false alarm.•Experimental investigations were designed to demonstrate the effectiveness of the Bayesian ridge regression modeling method.•The proposed method was used to monitor the damage of the ball screw of a CNC machining center.

A high-speed reciprocating operating mechanism with fast running and frequent stopping produces a certain vibration due to inertia. The inherent vibration of this mechanism becomes a key factor affecting positioning accuracy and time. However, the existing vibration identification method is suitable only for static or low-speed structure operations. Therefore, a method of identification for operational modal parameters based on strain response is proposed. This method combines the strain mode with the operational modal analysis method, both of which are verified under the static and operational states, respectively. First, the theoretical background of the identification method is derived. Next, the proposed approach is used to identify the displacement mode shapes and the strain mode shapes of the beam structure with modal parameters that are obtained by experimental modal analysis. These results show that the beam structure presents different displacement mode shapes and strain mode shapes under different constraints. Finally, the displacement mode shapes of the sorting arm are obtained by the method under operation. The recognition results are compared with the computational modal analysis, and the two mode shapes are found to be highly consistent, which verifies that the method is equally reliable during operation. This method expands the application scope of the classic experimental modal analysis and reduces the error because of the additional mass of the actual sensor.

Heavy machine tool work under such high-load conditions that chatter vibrations are prone to occur, which significantly diminishes the machining efficiency and quality. Stability lobe diagrams are commonly used to select appropriate spindle speed and axial depth of cut to get rid of chatter and maximize the material removal rate. However, this needs precise identification of the dynamics of the entire machine tool structure, especially in the low-frequency range. Operational modal analysis has been the proven technique for estimating dynamic characteristics of machine tool structures in operation conditions. In this article, a complete methodology was presented for employing operational modal analysis for heavy machine tool in machining conditions. A random cutting exciting method originally presented by Minis is modified which generates pseudorandom impulse force to excite a heavy vertical lathe structure. And the excitation signal of random cutting force was modeled to analyze the effect of cutting parameters on energy and frequency band of the excitation. One operational modal analysis method, the pLSCF (referred to as PolyMAX) method, was employed to estimate modal parameters during machining. It was also observed in chatter tests that the operational modal analysis results are more accurate than the traditional impact test results in characterizing the dynamics of machine tool structure in machining.