# Optimizing Replaced Nozzle Diameter of Abrasive Blasting Systems Using Experiment Technique Design
## 1 Introduction  
Abrasive blasting systems have been widely utilized for cleaning surfaces which could be needed for the further steps such as repairing, painting, welding, engraving, coating, etc. In addition, they are extensively used with various types of abrasive materials such as copper slag, steel shot, sand, etc. Although the blasting technology exhibits weaknesses like the emission of dust which can be inhaled by the operators [1], it is still used in industry due to its huge advantages. According to [2], the world's demand for abrasives increased from $8.6 billion in 2014 to $11.4 billion in 2019. This proves that the demand for abrasive blasting is huge. Accordingly, the reduction of the blasting cost is crucially important. However, there has been little research related to this topic. [PERSON] et al. [3] investigated the influences of six parameters (the initial nozzle diameter, the changing time of the nozzle, the fixed cost, the abrasive cost, the nozzle cost, and the nozzle wear rate) on the cleaning cost of the sand blasting system. It was observed that the cleaning time and the cleaning cost can be strongly reduced when applying this method. The same author [4] documented a study on optimizing the profit of sand blasting systems, in which the authors presented the effects of some parameters such as theinitial nozzle diameter, the air pressure, the nozzle wear, etc., on the objective function. The results showed that both the profit rate and the blasting time could be crucially decreased. [PERSON] et al. [5] adopted some input parameters such as the maximum nozzle diameter, the nozzle wear and the cost component to inspect their impacts on the optimum replaced diameter of boron carbide nozzles based on the multi-objective function. In addition, a regression model was established to determine the optimum initial nozzle diameter. Regarding the steel shot blasting system, [PERSON] et al. [6] studied the cost optimization of steel shot blasting systems in which the influences of the blasting process parameters on the cleaning cost were investigated. These parameters included the air pressure, the nozzle diameter, the cost components, and the nozzle wear. Moreover, a proposed model for calculating the optimum initial nozzle diameter was introduced. Their results revealed that the cleaning time and the cleaning cost which were resulting from the proposed model greatly decreased. Based on the above analyses, it is realized that the previously mentioned studies have not utilized the design of experiment technique to investigate the influences of the blasting process parameters on the optimum responses. The application of a main effects plot may be able to compute the higher influent input process parameters in any manufacturing process [7,8]. It was found that regression analysis could provide better predictions about the empirical relationships between input and response parameters in the electrical discharge machining process [9,10]. The optimization and prediction of the influence of process parameters could enhance the process mechanism followed by improvement of performance measures in electro chemical micro machining process [11,12] or determination of the optimum replaced grinding wheel diameter in grinding process [13,14,15]. It could be proposed that the regression analysis and main effects plot could provide better predictions of empirical relations among the factors through the machining experiments in the wire electrical discharge machining process [16,17]. Hence it is necessary to implement regression analysis and main effects plot-based analysis in the manufacturing processes. In this study, seven blasting process parameters (the initial nozzle diameter, the nozzle wear rate per hour, the time for changing a nozzle, the compressor power, the machine cost per hour, the nozzle cost per piece, and the cost of sand) have been selected to investigate their impact on the optimum nozzle diameter based on the cost objective function. The optimum problem is solved by a computer program.