Multi-index cutting parameters optimization for surface quality and cutting energy consumption of boring

Saving energy is one of the ways to achieve sustainable development. As an important equipment for manufacturing, machine tool has the characteristics of high energy consumption and high emission. In order to cope with reducing energy consumption and carbon emissions without reducing processing quality, the search for optimal cutting parameters requires balancing the contradiction between machining quality and cutting energy consumption, so that cutting parameters can both reduce energy consumption and ensure the quality of processing. It plays an important role in achieving energy saving and emission reduction. In this paper, the processing quality (residual stress, surface roughness) and cutting energy consumption are selected as the optimized multiple indicators, and the selected optimization indicators are analyzed. Weighted grey correlation analysis is used to obtain the multi-index gray correlation degree value, and the multi-index weight coefficient is determined. Based on weighted grey correlation analysis and multi-index orthogonal optimization method, the cutting parameters of the boring process are optimized, and the optimal parameter combination is that cutting depth of 0.05 mm, cutting speed of 120 m/min, and feed rate of 80 mm/min

Efficiency Assessment of the Complex Process of Variable Cutting Parameters

Saving energy and low carbon strategy has becoming the future trend of manufacturing industry. Cutting process in mechanical manufacturing has the characteristics of heavy energy consumption and complex process. The estimation and evaluation of energy consumption and energy efficiency of cutting process are hot topics in research groups and industry. The energy consumption of cutting process is determined by the load, which is related to the cutting parameters under fixed cutting systems, while the cutting parameter might change with the cutting process and material. Based on the relationship between energy consumption and cutting characteristics and the quality requirements, the cutting specific energy u(t) = P(t)/M(t), which is varies with time is proposed to evaluate the energy consumption of various cutting parameters under complex machining processes. The influence of cutting parameters on cutting energy consumption is studied by experiments and a calculation method is proposed to calculate cutting energy consumption. Finally, a case study of complex curve machining in mold manufacturing is illustrated to prove the effectiveness of the proposed evaluation method

Efficiency assessment of the complex process of variable cutting parameters

Saving energy and low carbon strategy has becoming the future trend of manufacturing industry. Cutting process in mechanical manufacturing has the characteristics of heavy energy consumption and complex process. The estimation and evaluation of energy consumption and energy efficiency of cutting process are hot topics in research groups and industry. The energy consumption of cutting process is determined by the load, which is related to the cutting parameters under fixed cutting systems, while the cutting parameter might change with the cutting process and material. Based on the relationship between energy consumption and cutting characteristics and the quality requirements, the cutting specific energy u(t) = P(t)/M(t), which is varies with time is proposed to evaluate the energy consumption of various cutting parameters under complex machining processes. The influence of cutting parameters on cutting energy consumption is studied by experiments and a calculation method is proposed to calculate cutting energy consumption. Finally, a case study of complex curve machining in mold manufacturing is illustrated to prove the effectiveness of the proposed evaluation method

Microbes translocation from oral cavity to nasopharyngeal carcinoma in patients

Abstract The presence of oral microbes in extra-oral sites is linked to gastrointestinal cancers. However, their potential ectopically colonization in the nasopharynx and impact on local cancer development remains uncertain. Our study involving paired nasopharyngeal-oral microbial samples from nasopharyngeal carcinoma (NPC) patients and controls unveils an aberrant oral-to-nasopharyngeal microbial translocation associated with increased NPC risk (OR = 4.51, P = 0.012). Thirteen species are classified as oral-translocated and enriched in NPC patients. Among these, Fusobacterium nucleatum and Prevotella intermedia are validated through culturomics and clonal strain identification. Nasopharyngeal biopsy meta-transcriptomes confirm these microbes within tumors, influencing local microenvironment and cytokine response. These microbes correlate significantly with the Epstein-Barr virus (EBV) loads in the nasopharynx, exhibiting an increased dose-response relationship. Collectively, our study identifies oral microbes migrating to the nasopharynx, infiltrating tumors, impacting microenvironments and linking with EBV infection. These results enhance our understanding of abnormal microbial communication and their roles in carcinogenesis