Elaboração de plano de manutenção para o setor de manufatura aditiva na indústria

Abstract

Additive Manufacturing has played a significant role in advancing the maintenance sector as a competitive differentiator within the industry. It enables the in-house manufacturing of spare parts while simultaneously reducing shipping and inventory expenses, thereby facilitating the adoption of the Just-in-Time methodology. However, machinery incorporating additive manufacturing technology is often not considered an asset within maintenance management. This research gap overlooks the critical aspect of maintenance costs and their relevance for Additive Manufacturing machines. Moreover, there is a lack of understanding regarding diagnostic routines and their optimal frequency to maximize the useful life and utilization rate of these devices. This study addresses the development of preventive conservation plans tailored for the Additive Manufacturing sector, with the aim of standardizing keeping activities in the routines of maintainers. To achieve this, a methodology is implemented focusing on a machine utilizing Fused Filament Manufacturing (FFF) technology, a method that has significantly contributed to the democratization of 3D printing. The implementation utilizes SAP software (Systeme, Anwendungen und Produkte in der Datenverarbeitung) to generate Service Orders periodically. Consequently, systematic preventive plans have been devised: I. Visual inspection plans: Maintenance personnel and operators employ subjective assessments using the five senses to detect changes in characteristics. They then complete a checklist detailing compliant or non-compliant situations. II. Lubrication routes: These are established to reduce friction and wear between mechanical elements with relative movement. III. Preventive exchange plans: This involves executing the replacement of worn components and sacrificial items to preserve the integrity of the machinery's critical pieces. Recording the data obtained from the execution of these plans in SAP enables future comparison and evaluation of the costs associated with a planned corrective, emergency, and preventive maintenance on additive manufacturing machines. Additionally, the historical data derived from notification records will facilitate assessing the necessity for creating preventive intervention plans for each equipment set and determining the optimal frequency to prevent breakdowns or malfunctions.