Farplas is a well-recognized Tier 1 supplier in the automotive industry, specializing in injection-molded parts. The i4Q project offers solutions that enhance quality and operational efficiency. The i4Q Analytical Dashboard enables the monitoring and analysis of sensor-based data via EUROMAP modules and a big data architecture. By training this data using machine learning algorithms, the most critical setup parameters that directly affect quality issues can be identified. This provides production engineers and operators with accurate and valuable insights to make decisions and recalibrate the process when needed, in a proactive manner, ultimately leading to efficient, scrap-free production. Furthermore, fluctuations in sensor value graphs not only help predict defective parts but can also suggest predictive maintenance strategies to minimize malfunctions and downtime, ensuring seamless production.
Considering that painted parts represent the final and most value-added version of injection-molded components, eliminating scrap risks before applying paint is crucial. In this regard, i4Q offers an innovative approach to digitalized quality management. Through graphical analysis of sensor values, along with its key feature of parameter suggestions, potential problems can be addressed even before they occur. This will strengthen the relationship and trust between Farplas and OEMs, further enhancing collaboration.
Generally, i4Q is a Project focusing on zero-defect manufacturing which has a mission about improving data accuracy, reducing waste and enhance productivity for factories. The main impact of i4Q Project is to help manufacturing integrating IoT Technologies, reduce production costs analyzing real-time data, and ensure production quality identifying potential defects early. In addition, the impacts of the Project for Farplas also consist above features beside improving machine performance, reducing downtime by extending the life of parts.
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