The Improvement of an APEX Machine involved in the Tire Manufacturing Process

Abstract

Over the years, there has been a huge expansion in the automotive component industry and its consequent innovation, which has evolved along with automation and robotics. Therefore, in order to ensure component quality, as well as the quality of the entire production process, it is crucial to enhance competitiveness in the sector. Since quality is a key feature in this industry, all manufacturers and suppliers are scrutinized by systematic audits to ensure constant improvements. This work was developed at a multinational industry and focuses on tire production for the automotive sector. Tires present a complex production process, which covers a wide range of activities from mixing, preparation, construction and vulcanization to the inspection departments, all of which are greatly predisposed to process improvement. In one of these departments (Preparation - APEX machines, responsible for the tire bead production), one of the main problems detected was directly related to a large number of failures in the pneumatic systems. These were found to be difficult to control, both in terms of speed and positioning. Some of the failures in automation were generating delays in certain processes, leading to lengthy setup times and culminating in higher production costs. In order to achieve greater quality, reliability and accuracy, a higher level of automation was applied to these kinds of machines by resorting to 5S methodology. A safety upgrade of the equipment was also undertaken, which will allow for the improvement of workplace safety.The performance improvement of the APEX machines was reached by implementing the following strategies: the automation of conveyor and tray movements between the cutting and application subprocesses; the implementation of 5S methodology; the automation of the cutting process; the updating of safety devices; and the automated control of the separation subprocess. A strong decrease in the breakdown time was recorded (-62%) resulting of the project implementation. The APEXmachine performance was also improved in 9%.