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Abstract(s)
Este projeto de mestrado foca-se na implementação de uma estratégia de manutenção preditiva aplicada na máquina de injeção ENGEL 1100 da Simoldes Plásticos, uma empresa do grupo Simoldes, com o objetivo de reduzir paragens produtivas não planeadas e otimizar a eficiência operacional. O projeto iniciou-se com uma revisão bibliográfica detalhada sobre os conceitos de manutenção preditiva e a sua importância na Indústria 4.0, incluindo uma análise das técnicas mais utilizadas, como a análise de vibrações e monitorização de parâmetros em tempo real. A metodologia adotada foi a Action Research, que permitiu realizar um plano de ação baseado na identificação de componentes críticos, nomeadamente as resistências elétricas do bico de
injeção e o acoplamento motor-bombas. Foram monitorizados sinais elétricos e implementados acelerómetros e sensores indutivos, conectados a uma ferramenta de diagnóstico avançada, que monitoriza continuamente a condição dos componentes. Os dados recolhidos são analisados em tempo real e o sistema foi integrado ao software de gestão da manutenção da empresa, através e uma aplicação de processamento de dados desenvolvida em linguagem Python, permitindo intervenções rápidas e precisas. Os resultados obtidos mostram que a aplicação da manutenção preditiva na Simoldes Plásticos pode levar a uma redução significativa das paragens produtivas e dos custos associados, melhorando a fiabilidade e a longevidade dos equipamentos. O trabalho contribui para a modernização dos processos de manutenção na indústria automotiva, posicionando a Simoldes como uma empresa inovadora e competitiva, capaz de atender às exigências do mercado com eficiência e qualidade.
This master's project focuses on the implementation of a predictive maintenance strategy applied to the ENGEL 1100 injection machine at Simoldes Plásticos, a Simoldes group company, with the aim of reducing unplanned production stoppages and optimizing operational efficiency. The project began with a detailed literature review on the concepts of predictive maintenance and its importance in Industry 4.0, including an analysis of the most commonly used techniques, such as vibration analysis and real-time parameter monitoring. The methodology adopted was Action Research, which made it possible to create an action plan based on the identification of critical components, specifically the electrical resistors in the injection nozzle and the motor-pump coupling. Electrical signals were monitored and accelerometers and inductive sensors were implemented, connected to an advanced diagnostic tool that continuously monitors the condition of the components. The data collected is analyzed in real time and the system has been integrated with the company's maintenance management software through a data processing application developed in Python, allowing for quick and precise interventions. The results obtained show that the application of predictive maintenance at Simoldes Plásticos can lead to a significant reduction in production stoppages and associated costs, improving the reliability and longevity of the equipment. The work contributes to the modernization of maintenance processes in the automotive industry, positioning Simoldes as an innovative and competitive company, capable of responding to market demands with efficiency and quality.
This master's project focuses on the implementation of a predictive maintenance strategy applied to the ENGEL 1100 injection machine at Simoldes Plásticos, a Simoldes group company, with the aim of reducing unplanned production stoppages and optimizing operational efficiency. The project began with a detailed literature review on the concepts of predictive maintenance and its importance in Industry 4.0, including an analysis of the most commonly used techniques, such as vibration analysis and real-time parameter monitoring. The methodology adopted was Action Research, which made it possible to create an action plan based on the identification of critical components, specifically the electrical resistors in the injection nozzle and the motor-pump coupling. Electrical signals were monitored and accelerometers and inductive sensors were implemented, connected to an advanced diagnostic tool that continuously monitors the condition of the components. The data collected is analyzed in real time and the system has been integrated with the company's maintenance management software through a data processing application developed in Python, allowing for quick and precise interventions. The results obtained show that the application of predictive maintenance at Simoldes Plásticos can lead to a significant reduction in production stoppages and associated costs, improving the reliability and longevity of the equipment. The work contributes to the modernization of maintenance processes in the automotive industry, positioning Simoldes as an innovative and competitive company, capable of responding to market demands with efficiency and quality.
Description
Keywords
Predictive maintenance Injection molding machine Condition indicators Vibration analysis Python Manutenção preditiva Máquina de injeção Indicadores de condição Análise vibrações