Loading...
Publication
Implementação de microplaneamento e melhoria de desempenho de uma máquina CNC
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 5.67 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
A presente dissertação teve como objetivo o desenvolvimento de uma abordagem de
microplaneamento e melhoria do desempenho de uma máquina CNC da empresa Jofebar,
integrada num contexto industrial altamente competitivo. Partindo da análise detalhada do
processo produtivo, foram identificadas as principais limitações relacionadas com os tempos de
setup, paragens não planeadas e ineficiência no escalonamento da produção.
Para apoiar a melhoria do processo, recorreu-se à construção de um Value Stream Mapping, ao
levantamento dos tempos de setup e à análise de indicadores de desempenho.
Foi desenvolvida uma nova lógica de sequenciamento de perfis, implementada através de um
algoritmo baseado na heurística Iterated Greedy, programado em Python e aplicado à matriz
de tempos de setup entre famílias de perfis de caixilharia. A aplicação desta ferramenta
demonstrou elevada eficácia na definição de sequências de produção mais eficientes, em
comparação com a lógica anteriormente utilizada.
A validação em ambiente fabril evidenciou reduções significativas no tempo total de setup,
aumentos na estabilidade do planeamento e ganhos expressivos no tempo efetivo de trabalho
da máquina.
Com a nova lógica de sequenciamento, verificou-se um aumento de 82% do tempo em trabalho,
uma redução de 50% do tempo em espera e uma diminuição de 68% do tempo de alarme. Estes
resultados permitiram elevar a produção média por turno de 51 m² para 102 m², representando
79% da procura diária, face aos 44% registados seguindo a lógica de sequenciamento de ordens
de produção.
O trabalho desenvolvido comprova o potencial da integração de heurísticas no
microplaneamento industrial, abrindo caminho a futuras melhorias na gestão dos recursos
produtivos.
This dissertation aimed to develop a micro-planning approach and improve the performance of a CNC machine at Jofebar, within a highly competitive industrial context. Based on a detailed analysis of the production process, the main limitations were identified, namely setup times, unplanned downtime, and inefficiencies in production scheduling. To support process improvement, a Value Stream Mapping was constructed, setup tomes were recorded, and performance indicators were analyzed. A new sequencing logic was developed and implemented through an algorithm based on the Iterated Greedy heuristic, programmed in Python and applied to the setup time matrix across families of frame profiles. The application of this tool proved to be highly effective in defining more efficient production sequences compared to the previously used logic. Validation in a real manufacturing environment demonstrated significant reductions in total setup time, increased planning stability, and notable gains in effective machine operating time. With the new sequencing logic, working time increase by 82%, waiting time was reduced by 50% and alarm time decrease by 68%. These improvements raised the average production per shift from 51 m² to 102 m², representing 79% of daily demand compared with the 44% achieved using the previous production order sequencing logic. The work developed demonstrates the potential of integrating heuristics into industrial microplanning, paving the way for future improvements in the management of productive resources.
This dissertation aimed to develop a micro-planning approach and improve the performance of a CNC machine at Jofebar, within a highly competitive industrial context. Based on a detailed analysis of the production process, the main limitations were identified, namely setup times, unplanned downtime, and inefficiencies in production scheduling. To support process improvement, a Value Stream Mapping was constructed, setup tomes were recorded, and performance indicators were analyzed. A new sequencing logic was developed and implemented through an algorithm based on the Iterated Greedy heuristic, programmed in Python and applied to the setup time matrix across families of frame profiles. The application of this tool proved to be highly effective in defining more efficient production sequences compared to the previously used logic. Validation in a real manufacturing environment demonstrated significant reductions in total setup time, increased planning stability, and notable gains in effective machine operating time. With the new sequencing logic, working time increase by 82%, waiting time was reduced by 50% and alarm time decrease by 68%. These improvements raised the average production per shift from 51 m² to 102 m², representing 79% of daily demand compared with the 44% achieved using the previous production order sequencing logic. The work developed demonstrates the potential of integrating heuristics into industrial microplanning, paving the way for future improvements in the management of productive resources.
Description
Keywords
Micro-planning CNC Equipment Value Stream Mapping Setup Maintenance Heuristics Iterated Greedy Microplaneamento Equipamento CNC Manutenção