The Research Of Process Quality Control In Complex Equipment Manufacturing

Manufacturing process quality control is the key measure to guarantee the quality of the product.It is also the core implement of total quality management.With the proposal of Intelligent Manufacturing and Made in China 2025 Strategy,there are higher requirements on manufacturing process quality control.Under the background of modernization and intellectualization,technological process is becoming more complicated and manufacturing process develops to a higher level of automation.It is hard to satisfy the current situation only by depending on the Statistical Process Control to manage the quality during the manufacturing process.Recently,there are some machine learning methods coming up such as Support Vector Machine,Least Square Support Vector Regression,and some adaptive data preprocessing method such as Empirical Mode Decomposition,which have certain improvements than before.Due to the characters of complex manufacturing industry such as big batch,fast pace,complicated manufacturing process,uncertain abnormal factors,manufacturing data which have high sampling rate,high updating rate,large sample volume and are easily to be changed,this paper studies the theory and methods about the manufacturing process quality control of complex manufacturing industry based on EMD and SVM.Firstly,this paper introduces process quality control model,analyzes Statistical Process Control which is widely used in manufacturing process quality control and applies control chart as a basic tool for many years,and points out the limits in the control chart of SPC.Then,this paper summarizes the developing artificial intelligence technology in the study area of process quality control,highlights the research situation of artificial intelligence technology in recognizing the model of SPC control chart and forecasting the quality parameter in the process,and also introduces the applying situation of the data preprocessing technology when it is used to manage the process quality data.Secondly,this paper puts forward a process quality control model which combines manufacturing process quality pattern recognition,manufacturing process quality parameter forecasting and deduction discipline.Based on the previous models and methods of quality control and quality improvement,and directed at the Characteristics and Actual Situation of Process in Complex Equipment Manufacturing.Thirdly,according to data preprocessing technology and machine learning algorithm,this paper puts forward two critical models to realize process quality control.They are quality pattern recognition model in manufacturing process based on mixed characters and Parameter Prediction Model of Process Quality Abnormal Pattern Based on EMD Clustering Feature.Lastly,this paper takes a diesel engine camshaft manufacturing process as an example,then using the process quality control model to control the process quality of diesel engine camshaft.And it proves the efficiency and feasibility of the process quality control model.