The Prediction of the Man-Hour in Aircraft Assembly Based on Support Vector Machine Particle Swarm Optimization

Abstract

As the representative of manufacturing industry, aircraft assembly lacks of effective method to forecast man-hour. The forecasting accuracy of existing methods is universally pretty low. On the basis of full analysis of aircraft assembly's feature, this study proposes a forecasting model based on support vector machine (SVM), which is optimized by particle swarm optimization. It can carry out quantitative prediction of the process' man-hour during aircraft's assembly. Firstly, we decompose aircraft's assembly work by the concept of work breakdown structure. Further, the process parameters related to man-hour were listed and we made necessary correlation analysis of these historical data. Parameters with high contribution are then used as input of forecasting model. A new forecasting model utilizing SVM is proposed, which carries out the process as the minimum research granularity. Its performance is compared with back propagation neural network. The process of automatic drilling & riveting is adopted as an example in order to present and validate the model. Experimental results reflect that SVM has high forecast precision and good fitness, so that it is suitable for small sample prediction. Through the optimization, it can effectively predict man-hour of assembly work in a short time while maintaining sufficient accuracy.

A Study on the Man-Hour Prediction in Structural Steel Fabrication

“…In recent years, thanks to widespread adoption such as big data, artificial intelligence, the Internet of Things,and general information technology infrastructure in manufacturing, scholars have conducted extensive research on the application of these technologies to man-hour prediction.Hur et al [3] constructed a man-hour prediction system based on multiple linear regression and classification regression tree for the shipbuilding industry, and the results showed that the prediction system has strong reliability. Based on this study, three types of plans have been established in man-hour prediction, they are quarter plan, month plan and day plan respectively.YU et al [8] conducted a study on the ML-based quantitative prediction of the process' man-hour during aircraft's assembly.The study proposed a forecasting model based on Support Vector Machine(SVM), which was optimized by particle swarm optimization.The authors showed that the improved model could effectively predict man-hour of assembly work in a short time while maintaining sufficient accuracy.Mohsenijam et al [9] proposed a framework for labour-hour prediction in structural steel fabrication.The research explored a data-driven approach which used Multiple Linear Regression (MLR) and available historical data from Building Information Models (BIM) to associate project labour-hours and project design features.IşıkS et al [10] explored the use of machine-learning techniques such as Support Vector Regression(SVR),Gaussian Process Regression(GPR) and Adaptive Neuro-Fuzzy Inference System(ANFIS) for predicting man-hours in Power Transformer manufacturing. The authors reported that these techniques,especially GPR are useful in the prediction of man-hours in Power Transformer production industry.…”

“…In formulas (7) and (8), n represents the number of evaluated samples, yi represents the true value of the samples, i.e. actual man-hour, and ŷi represents the predicted value of the samples, i.e.…”

A Study on the Man-Hour Prediction in Structural Steel Fabrication

“…Based on this study, three types of plans have been established in man-hour prediction, they are quarter plan, month plan and day plan, respectively. Yu et al [8] conducted a study on the MLbased quantitative prediction of the process' man-hour during aircraft's assembly. The study proposed a forecasting model based on a Support Vector Machine (SVM), which was optimized by particle swarm optimization.…”

Prediction of Standard Time of the Sewing Process using a Support Vector Machine with Particle Swarm Optimization

“…Therefore, standard time prediction has direct bearing on economic accounting, production schedule control, resource optimization, production cycle shortening, cost control, and product quotation. Additionally, it ultimately promotes the labor productivity of enterprises and enhances their market competitiveness [2]. Standard time is a common language between fashion brands and manufacturers for discussions on cost, time, and fl oor capacity.…”

“…[20] put forward an intelligent standard time forecast method and its relevant parameter selection algorithm based on kernel approximation and SVM [20]. Yu et al [2] established a standard time prediction model based on SVM in the aircraft assembly work and compared its performance with the back propagation neural network. However, there are few related studies using SVM for standard time prediction in the apparel production field.…”