Objective : aimed to enhance machining processes and maximize efficiency. develop a comprehensive tool life monitoring system for Dies, with the objective of improving tool performance, reducing downtime, and ultimately, increasing productivity.

The industries core operations involved complex metalworking processes, relying heavily on Dies - crucial tools in their manufacturing line. Maintaining optimal performance and longevity of these Dies was critical to their operations. However, the lack of a systematic monitoring system often led to unforeseen tool failures, causing production delays and increased maintenance costs.

Challenges faced :

1) Tool Failure Prediction: The Client lacked a predictive tool monitoring system, leading to unexpected failures and disruptions in production.

2) Downtime Costs: Frequent tool failures resulted in increased downtime, impacting production schedules and causing revenue loss.

3) Resource Utilization: Inefficient tool usage led to premature replacements, raising maintenance costs and impacting profitability.

4) Performance Consistency: Inconsistencies in tool performance affected the quality of machined components, impacting overall product quality.

Solution

1) Development Phase

Monitoring System Development: We designed and implemented a bespoke tool life monitoring system, integrating IoT sensors and data analytics to track real-time tool performance.

Data Analytics Implementation: The system collected and analyzed various parameters such as temperature, vibration, and usage cycles to predict potential tool failures.

Predictive Maintenance Model: Leveraging machine learning algorithms, the system forecasted tool life expectancy, enabling proactive maintenance and replacement scheduling.

Custom Dashboard Interface: A user-friendly dashboard was developed to provide comprehensive insights into tool health, enabling operators to make informed decisions in real-time.

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2) Implementation Phase

Research and Analysis: Studied existing machining processes, identified critical parameters affecting tool life.

Sensor Integration: Installed IoT sensors on Dies to capture real-time data related to tool performance.

Algorithm Development: Created predictive models using machine learning algorithms to forecast tool life based on data analytics.

Dashboard Development: Designed an intuitive interface for easy visualization and interpretation of tool health metrics.

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Business Impact

Reduced Downtime: Predictive tool monitoring reduced unexpected failures, minimizing downtime by 20%.

Cost Savings: Optimized tool usage and proactive maintenance reduced overall maintenance costs by 15%.

Enhanced Productivity: Improved tool longevity increased production efficiency by 25%.

Improved Quality Control: Consistent tool performance led to better quality machined components, enhancing overall product quality.