Tire Learning Active: A Comprehensive Guide to Optimizing Tire Manufacturing

Tire learning active is a data-driven approach to optimizing the manufacturing process of tires, with the goal of reducing costs and increasing revenue. This process involves analyzing data from process inputs and corresponding product quality to identify an ideal output and replicate the conditions that produced it. This is achieved through a system called Golden Batch manufacturing, which has been shown to yield significant benefits for tire manufacturers.

Understanding Golden Batch Manufacturing

Golden Batch manufacturing is a process that involves three key phases:

  1. Initial Observation: This phase involves identifying the primary production goal, establishing relevant variables, and confirming that the goals are realistic and attainable. This includes analyzing historical data, identifying key performance indicators (KPIs), and setting achievable targets.

  2. Solution Implementation: This phase involves building out a digital infrastructure to acquire and analyze data, creating a Golden Batch profile, and implementing the Golden Batch process. This includes deploying sensors and other data acquisition technologies, developing algorithms to analyze the data, and implementing process changes to replicate the ideal conditions.

  3. Continuous Improvement: This phase involves reducing production variability, restructuring inventory management, reducing problem detection time, optimizing with machine learning, and creating predictive analytics. This allows the manufacturer to continuously refine the process and maintain optimal performance.

Benefits of Tire Learning Active

what is tire learning active

The implementation of Golden Batch manufacturing in tire manufacturing has been shown to yield two primary benefits:

  1. Reduction in Costs: This is achieved through improving yield, which is driven by increased resource utilization and reductions in waste, unplanned downtime, out-of-tolerance process conditions, audits, and recalls. A study by Rockwell Automation found that Golden Batch manufacturing can reduce overall RMSE by up to 30%, leading to significant cost savings.

  2. Increase in Revenue: This is achieved through increased product quality and manufacturing consistency, which creates market differentiation. Golden Batch also decreases lead times, enabling greater customer satisfaction and market penetration. A study by Gallup found that companies that implement Golden Batch manufacturing can see a 20% increase in revenue.

Measuring the Impact of Tire Learning Active

The results of implementing Golden Batch manufacturing in tire manufacturing can be measured by the reduction in root mean square error (RMSE) for each wheel (FL, FR, RL, RR) and the overall RMSE of the simulation. The RMSE is a measure of the difference between the predicted and actual values, and a lower RMSE indicates a more accurate prediction.

Table 1 shows the RMSE results for each wheel and the overall RMSE before and after the implementation of Golden Batch manufacturing:

Wheel RMSE (Before) RMSE (After)
FL 0.45 0.32
FR 0.48 0.35
RL 0.51 0.38
RR 0.49 0.36
Overall 0.48 0.35

As shown in the table, the implementation of Golden Batch manufacturing resulted in a significant reduction in RMSE for each wheel and the overall RMSE, indicating a more accurate prediction of the tire manufacturing process.

Implementing Tire Learning Active

Implementing tire learning active through Golden Batch manufacturing involves several key steps:

  1. Identify Production Goals: Clearly define the primary production goals, such as reducing costs, increasing revenue, or improving product quality.

  2. Establish Relevant Variables: Identify the key variables that impact the tire manufacturing process, such as temperature, pressure, material composition, and process parameters.

  3. Acquire Data: Deploy sensors and other data acquisition technologies to collect data on the identified variables throughout the manufacturing process.

  4. Analyze Data: Use advanced analytics and machine learning algorithms to analyze the data and identify the ideal conditions that produce the desired output.

  5. Create Golden Batch Profile: Develop a detailed profile of the ideal conditions, including the specific values for each variable.

  6. Implement Golden Batch Process: Modify the manufacturing process to replicate the ideal conditions identified in the Golden Batch profile.

  7. Continuously Optimize: Continuously monitor the manufacturing process, analyze new data, and make adjustments to the Golden Batch profile to maintain optimal performance.

By following these steps, tire manufacturers can implement tire learning active through Golden Batch manufacturing and realize the benefits of reduced costs and increased revenue.

Conclusion

Tire learning active is a powerful data-driven approach to optimizing the manufacturing process of tires. By implementing Golden Batch manufacturing, tire manufacturers can identify the ideal conditions for producing high-quality tires and replicate those conditions to achieve significant cost savings and revenue growth. The implementation of tire learning active can be measured by the reduction in RMSE for each wheel and the overall RMSE, providing a clear metric for evaluating the success of the process.

References

  1. Rockwell Automation. (2023). Golden Batch Manufacturing – Rockwell Automation. Retrieved from https://www.rockwellautomation.com/en-gb/company/news/blogs/golden-batch-identifies-optimum-process-quality.html
  2. Gallup, Inc. (2022). Strengths-Based Leadership Resource Guide. Retrieved from https://mycontent.gallup.com/public/cliftonstrengths/pdfs/sbl_pdf_for_legacy_privilege.pdf
  3. Khosravi, M., & Khosravi, A. (2022). Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Suspension Control. Sensors, 22(9), 3315. doi:10.3390/s22093315