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:
The paper presents the method of determining the sensitivity coefficient of flow stress with strain rate m (the strain rate sensitivity) during superplastic forming process of AA7075 aluminum alloy. The bulging free forming test was performed under constant gas pressures from 0.6 MPa and 0.8 MPa with temperatures of 5000C and 5300C. The values of the strain rate sensitivity varied from 0.3 to 0.6 with strain rate in the range of 5.10-4 to 1.5.10-3 (s-1), which can evaluate the superplastic forming ability of alloy in different technological modes. Tests results are allowed to choose the process parameters in the superplastic forming of AA7075 aluminum alloy sheet.
This research work presents the multiply optimizing to minimize surface roughness and maximize cutting speed (Vc) when Electrical Discharge Machining (EDM) hardened 90CrSi steel. The input parameters used to investigate their effects on the responses are purposely selected such as Concentration of powder, Pulse on time, Pulse off time, Current, and Voltage. Grey Relational Analysis (GRA) is combined with Taguchi method to find the optimal set of machining parameters which can satisfy the responses. The results show that Pulse off time has the smallest effects (2,03%) but Current has the strongest influence (32,29%) on the grey grade value. The proposed model corfirmed by experiment and ANOVA exhibited the reability approche to predict surface roughness and material removal rate.