Abstract—This paper reports the results of statistical analysis conducted on the weld data obtained from friction stir welding of aluminium and copper. The welds were produced by varying the process parameters; the rotational speed was varied between 600 to 1200 rpm and the welding speed varied between 50 and 300 mm/min. The Statistica (version 9.0) statistical analysis software package was used to generate the scatter and surface plots relative to the experimental results obtained from the tensile testing and the FSW data. Regression analysis was also done on the weld data. It was found that the downward vertical force has a significant effect on the Ultimate Tensile Strength of the weld and a strong relationship exist between the heat input into the welds and the measured electrical resistivities of the welds. Keywords— friction stir welding, dissimilar materials, statistical analysis. I. INTRODUCTION olid-state welding is the process whereby coalescence is produced at temperatures below the melting point of the base metal without the use of any filler metal. Examples of solid-state welding processes include friction welding, Friction Stir Welding (FSW), ultrasonic welding, resistance welding, explosive welding and diffusion welding. There are fewer defects in solid-state welding because the metals do not reach their melting temperatures during the welding process. However, the base metals being joined retain their original properties, and the Heat Affected Zone (HAZ) is small when compared with the fusion welding techniques [1]. Friction Stir Welding is a variant of friction welding that produces a weld between two or more work pieces by the heating and plastic material displacement caused by a rapidly rotating tool that traverses the weld joint [2]. The schematic diagram of the process is presented in Fig 1. [3] In FSW, the inter- relationship between the process parameters is complex; the two most important welding parameters being the tool rotational speed in a clockwise or anti-clockwise direction, and the tool traverse speed along the joint line [4]. The rotation of the tool results in the stirring and mixing of material around the rotating pin during the welding process which in turn affect the evolving properties of the weld. As such, understanding the relationship between the process parameters and the resulting properties of the welds is Dr E. T. Akinlabi is a lecturer in the Department of Mechanical Engineering Science, University of Johannesburg, South Africa, 2006. (Phone: +2711-559-2137; e-mail: etakinlabi@uj.ac.za ). S. A. Akinlabi is a doctorate candidate in the Department of Mechanical Engineering Science, University of Johannesburg, South Africa, 2006. (Phone: +277984-77095; e-mail: saakinlabi@uj.ac.za ). important. Fig. 1: Schematic diagram of friction stir welding process [3] Research studies on process-property relationship [4-7] reported that the input process parameters are found to exert significant effect on the resulting joint integrities. In attempting to further understand the process-property relationship in FSW, statistical analyses of the weld data have been conducted on similar joints of aluminium alloys. Rajamanickam and Balusamy [8] conducted statistical analysis on the weld data obtained in FSW of 2014 aluminium alloy and concluded that the weld speed has the highest statistical influence on the mechanical properties of the welds produced. Also, Benyounis and Olabi [9] conducted a literature survey on optimization of different welding processes using statistical and numerical approaches and concluded that modeling; control of the process parameters and optimization of different welding processes can be achieved using different statistical tools. The aim of this study is to conduct statistical analysis on the weld data obtained from dissimilar FSW of aluminium and copper in other to gain insight and understanding into the interaction between the process-properties of the resulting welds. II. EXPERIMENTAL SET-UP Friction Stir Welds of 5754 aluminium alloy (AA) and C11000 copper in butt joint configurations were produced on 600 mm x 120 mm x 3.175 mm thick sheets with an MTS Intelligent Stir Welding for Industry and Research Process Development System (I-STIR PDS) FSW platform at the Friction Processing Research Institute (FPRI) of Nelson Mandela Metropolitan University (NMMU), Port Elizabeth Esther T. Akinlabi Member, IAENG and Stephen A. Akinlabi, Member, IAENG Friction Stir Welding of Dissimilar Materials – Statistical Analysis of the Weld Data S Proceedings of the International MultiConference of Engineers and Computer Scientists 2012 Vol II, IMECS 2012, March 14 - 16, 2012, Hong Kong ISBN: 978-988-19251-9-0 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online) IMECS 2012