Detection And Analysis And Evaluation Of Cleaning Performance Of Machine Tool Processing Environment

The machinery manufacturing industry is an important part of my country's manufacturing industry,and it plays an important role in supporting and promoting my country's economic development.Compared with strong manufacturing countries,my country's mechanical processing methods are rougher,resource utilization is low,and environmental pollution is serious.In order to respond to the call for sustainable development and improve my country's ecological level,the concept of green manufacturing has been gradually promoted,and the traditional manufacturing model is in urgent need of transformation and upgrading.At present,the relationship between traditional processes and environmental loads is not clear,and it is difficult to improve traditional processes.In order to solve the above problems,this paper develops a corresponding detection system for the environmental emission pollutants generated during the machining process,studies the detection methods of the environmental cleanliness of the machine tool processing,collects the corresponding environmental emission data,selects process types,raw materials and other factors for analysis The law of its impact on environmental emissions is of great significance to reducing pollutant emissions during processing and optimizing the process.Analyzed the detection requirements in the process of machine tool processing,designed a detection system integrating multiple types of sensors such as dust,oil mist and noise,and developed a software platform with functions such as collection and display,curve drawing,database storage and historical data query,which can be realized Collection and acquisition of load data such as environmental emissions and occupational health during machine tool processing.Aiming at the problems of data loss,data abnormality,signal noise,insufficient sample size,etc.in the original data collected,combined with engineering practice,the mechanism of data loss and abnormality was analyzed,and methods of data cleaning and sampling were proposed and carried out.Algorithm test verifies the effectiveness of cleaning and sample augmentation methods.Data collection experiments were carried out for factors such as the orientation of the sampling point and the stable sampling time.Through the analysis and research of the dust and oil mist concentration under different sampling point orientations and sampling time conditions,a sampling method for the environmental load data of machine tool processing was proposed.It can provide data support for process evaluation and green promotion.Established a multi-input multi-output IPO model for machining processes such as turning and milling,divided the machining process scenes,selected process types,raw materials,etc.as factors affecting oil mist and dust emissions during the machining process,and designed parameters to influence the research experimental plan.Collected multiple sets of process scene data,through the comparative analysis of different process scene data,studied the influence law of each process scene element on the environmental emission data,and put forward specific suggestions for improving the green process.