Cutter System Early Wear Detection
Improves Product Quality,
Reduces Expensive Scrap and
Avoids Production Loss
Shoreline AI delivers $2M in savings for Global Performance Material provider
Customer Background
Based in Houston, Texas, and with regional offices in Shanghai, Brussels and Detroit, the customer is a performance material solutions provider with global manufacturing facilities in North America, Europe and Asia. Their global workforce makes the materials used to make safer vehicles, cleaner energy, better medical devices, smarter appliances and longer-lasting apparel and consumer goods. They are committed to safety,
sustainability and the success of their customers and our communities.
The company uses a variety of machines in the cutting production process, including electric motors, gearboxes, extruders, pumps, blowers, fans, conveyers, presses, balers, HPUs and chillers. They deployed Shoreline AI’s solution across a variety of assets to gain real-time visibility into machine performance and prevent downtime, scrap and reduced productivity.
Customer’s assets being monitored by Shoreline AI
Shoreline AI’s predictive maintenance analytics prevents cutter system failure
Shoreline AI’s custom cutter system AI model was deployed for real time condition monitoring of the entire power train’s behavior, including smart sensors-integrated process data from several critical components of the cutter system:
- Feed roll to detect any blade problems
- Quench table water flow, temperature and pressure
- Material ID, viscosity and extruder exit temperature
- Water nozzle distribution, loss of pressure/flow
- Dryer system performance/plugging
- Rotary airlock performance and material flow
- Pellet conveyer blower to monitor for plugging
Shoreline AI’s Anomaly Detection with adept Vibration Analysis
Normal vibration levels
Vibration-level anomalies
Increased loading on the motor dampens vibration levels, a few hours before wrapping
Shoreline AI’s predictive maintenance analytics prevents cutter system failure
Data ingestion and processing into Shoreline’s AWS environment consists of several AWS services that include AWS IoT Core which handles device authentication/authorization, data encryption. IoT core triggers AWS Lambda for processing. Structured data is stored in Amazon Aurora while unstructured data and asset manuals in stored in Amazon S3. Shoreline also leverages Amazon SageMaker for training its ML models and Amazon Bedrock LLM Models for Gen AI. This combination of ML models with physics-based models enable auto-configuration of assets from Shoreline’s proprietary pre-built library of more than 30,000 assets. Initial machine baselines established from the auto-configuration process provides insights in days, not months.
Results
As a result of implementing Shoreline AI’s solution, the customer estimated the substantial ROI from avoiding wraps, dryer blockages and conveyer jams at $250,000-$300,000 in a 3-month period. This estimate was based on just 2 events per cutter system per month, saving 10-20 hours of production downtime per year. This estimate did not include avoided repair costs. For a plant with 10 cutter systems producing Nylon-66 for example, this avoidance of up to 200 hours of downtime and lost production combined with lost material costs generated an estimated savings of $2.2M, not including maintenance costs.
About Shoreline AI
Shoreline AI’s plug-and-play asset performance management delivers breakthrough simplicity and cost efficiency. Completely self-installed by non-experts, smart sensors automatically connect to the cloud and are auto-provisioned via a rich library of 30,000+ pre-built asset physics models.
This cloud-native approach requires no new CapEx, on-site experts or data scientists, operationalizing in days and delivering powerful machine-specific analytics. This highly secure, 100% APM subscription approach creates unprecedented industrial economics and scales easily for new applications such as emissions monitoring.
Shoreline AI helps clients in asset-intensive industries maximize the performance and profitability of their operations, create a proactive and predictive maintenance approach to asset management, and accelerate sustainability initiatives. The company’s solutions are designed for machinery serving the energy, manufacturing, pharma and data-center cooling industries.
