Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
In this paper, the Box-Jenkins methodology representing the Autoregressive Integrated Moving Average ARIMA time series model has been used to study the patterns of injured people caused by road traffic accidents in Erbil city as well as making a monthly forecast. A monthly accident data of injured people from January 2013 to June 2018 were obtained from the General Directorate of Traffic in Erbil Governorate. The results showed that the series has features of seasonality and the number of injuries due to traffic accidents is decreasing in most months of the year. Some suitable models were developed and SARIMA (0,1,1)(1,0,1)12 was stated as adequate and the best model depending on some performance measures. A monthly forecast was made using the best model and it showed that the number of injured cases due to traffic accidents would continue to decrease in Erbil city to the end of December 2020
This paper presents the study on the surface roughness model of workpiece in grinding. Based on the analysis of some previous studies, this research identifies the most outstanding model. After analyzing and evaluating its advantages, that model is considered in order to improve. The development of the model is implemented by putting into the roughness model two parameters that significantly affect the surface roughness in grinding, namely the elastic module of grinding wheel and workpiece material. The results of the application of the improved model to the prediction of surface roughness are compared with the roughness values when using CBN grinding wheel to grind C45 steel. It shows that the predictive roughness values are greatly close to the experiment results. The average discrepancy between predictive results with experiment results is approximately 8.11%. This study offers a promising ability to predict the surface roughness of a detail in grinding