INNOVATION | TECHNOLOGY
Artificial intelligence makes
its way into compounding Faster and better data collection is growing in importance as AI tools find use in optimising plastics compound formulas and processes. Jennifer Markarian investigates
Artificial intelligence (AI) and machine learning (ML) are finding new uses in plastics compounding. Earlier applications included predictive maintenance and digital tools to automate tasks for more efficient and effective operations. Newer, user-friendly applications include natural language interfaces and ML models that translate data from in-line testing into product and process intelligence. With the rise of these technologies, the importance of data collection and data analysis is growing. Take, for example, automated testing instruments for lab measurements. These instruments reduce manual labour and human error, make labs more efficient and enable easier data collection, which is critical for using data to improve processes. “Automated tensile testing
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generates a vast amount of precise data. This data is the foundation for effective AI/ML applications in process control and quality assurance,” said Khaled Boqaileh, CEO at LabsCubed. LabsCubed, headquartered in Ontario, Canada,
offers a suite of automated physical property testing systems, including the CubeTen automated physical property testing system for plastics. These systems enable automated measurement of tensile strength, tear resistance, flexural modulus, and hardness. The capability to run these different tests without switching machines or setups reduces downtime. The software can be configured to automatically generate reports and flag anomalies in test data, enabling rapid response to out-of-spec materials. The modern testing system aligns with
Main image: Compound formulation is set to benefit from the growing use of AI
August 2025 | COMPOUNDING WORLD 13
IMAGE: SHUTTERSTOCK
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