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Five-axis flank milling is the most commonly used processing method in the aviation industry for the machining of thin-walled parts with complex ruled surfaces. During machining, the tool/workpiece deformations caused by the cutting force often lead to surface errors on the machined components that severely affect the accuracy of the machining results. This article presents an iterative compensation strategy to reduce the tool/workpiece deformation-induced surface error during the five-axis flank milling of thin-walled workpieces by modifying the tool tip position and tool axis orientation. This approach can be implemented in four steps. First, a highly integrated cutter-workpiece engagement extraction method is developed for the construction of a flexible cutting force model that can follow changes in the process geometry. Second, the tool/workpiece deformations are predicted by the cantilever beam model and finite element model, respectively. Third, an off-line error compensation scheme is performed at each cutting location of the tool path to obtain the modified tool position. Fourth, the machined surface of the workpiece model is reconstructed, and the compensated machining code, which can be used directly for actual machining, is generated. A case study is presented at the end of this article, and the effectiveness of the present compensation strategy is verified by machining experiments.

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Baihe Zhimu decoction (BZD) has significant antidepressant properties and is widely used to treat mental diseases. However, the multitarget mechanism of BZD in postpartum depression (PPD) remains to be elucidated. Therefore, the aim of this study was to explore the molecular mechanisms of BDZ in treating PPD using network pharmacology and molecular docking. Active components and their target proteins were screened from the traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The PPD-related targets were obtained from the OMIM, CTD, and GeneCards databases. After overlap, the targets of BZD against PPD were collected. Protein-protein interaction (PPI) network and core target analyses were conducted using the STRING network platform and Cytoscape software. Moreover, molecular docking methods were used to confirm the high affinity between BZD and targets. Finally, the DAVID online tool was used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapping targets. The TCMSP database showed that BZD contained 23 active ingredients in PPD. KEGG analysis showed that overlapping genes were mainly enriched in HIF-1, dopaminergic synapses, estrogen, and serotonergic synaptic signalling pathways. Combining the PPI network and KEGG enrichment analysis, we found that ESR1, MAOA, NR3C1, VEGFA, and mTOR were the key targets of PPD. In addition, molecular docking confirmed the high affinity between BZD and the PPD target. Verified by a network pharmacology approach based on data mining and molecular docking methods, the multi-target drug BZD may serve as a promising therapeutic candidate for PPD, but further in vivo/in vitro experiments are needed.Abbreviations: BZD = Baihe Zhimu decoction, DAVID = visualization and integrated discovery, DL = drug-likeness, GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, OB = oral bioavailability, PPD = postpartum depression, PPI = protein-protein interaction, TCM = traditional Chinese medicine.