Abstract—Many manufacturers, in particular machine tool builders, aim for service innovation in order to improve their market competitiveness. Machine tool trading companies expect to create a value-added process through shifting from just selling product i.e. machine tools to selling an integrated combination of product and services, so called product-service system (PSS) which in turn competes with other developing companies. This is the process of servitization. The purpose of this work is to explore the benefits of servitizing machine tool industry and identify the effect of such an approach on the performance of engineering companies. As important, is to evaluate the accuracy of components as part of predictive maintenance, to support machine tool sevitization. This paper proposed a case study of ballscrew performance assessment. The purpose was monitoring the degradation of two parallel ball screws in a 5-Axis gantry machine tool, based on data from an acoustic emission sensor and machine learning technology. The significance of this work is that ballscrew performance as part of machine tool accuracy is critical for high value manufacturing. Machine tool users would prefer their machines to be maintained to the required tolerance by the predictive maintenance service warranty offered by the machine trader and keep their machines up to date to reduce unnecessary downtime.
 

Index Terms—Servitization, Ballscrew, machine tools, manufacturing, sensors, machine learning, temperature

Cite: Nurudeen Alegeh, Abubaker Shagluf, Andrew P. Longstaff, and Simon Fletcher, "Accuracy in Detecting Failure in Ballscrew Assessment towards Machine Tool Servitization" International Journal of Mechanical Engineering and Robotics Research, Vol. 8, No. 5, pp. 667-673, September 2019. DOI: 10.18178/ijmerr.8.5.667-673