Energy and emissions under review Seco explores IoT-driven machining solutions

Source: Seco Tools 3 min Reading Time

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Seco and the AMRC North West are collaborating on a data-driven research project to improve sustainability in machining. By combining IoT technologies with facility-wide monitoring, the initiative aims to identify energy inefficiencies, reduce CO2 emissions, and lay the groundwork for future AI-supported process optimization.

Seco is turning intelligent data analysis into a powerful tool for sustainable machining.(Source:  Seco)
Seco is turning intelligent data analysis into a powerful tool for sustainable machining.
(Source: Seco)

As manufacturers face increasing regulatory and environmental demands, the role of machine data in driving sustainable production is coming into sharper focus. In response, cutting tool specialist Seco is collaborating with the Advanced Manufacturing Research Centre North West (AMRC) in Samlesbury, UK, on a research initiative to improve energy and resource efficiency across machining operations.

The joint project addresses the growing need for reliable data collection and analysis throughout the production environment. By applying Internet of Things (IoT) technologies and industrial communication protocols, the partners aim to create a unified data source covering machining processes, machine surroundings, and facility-wide conditions. This comprehensive view is intended to support more accurate environmental impact assessments and process optimizations.

This project segments the manufacturing ecosystem into three interrelated domains: the machining process itself, the immediate machine environment, and the broader facility context. By monitoring each area individually and then analyzing how they influence one another, stakeholders gain both detailed diagnostics and a holistic perspective on sustainability challenges.

  • For the machine: The approach enables monitoring and hotspot analysis of the machining process directly. This includes information on the energy consumption of the machine, broken down by sub-process, cutting fluids and other material consumption. This can then be directly converted into real time costs and CO2 impact through live grid carbon intensity factors and cost per kilowatt hour.
  • For the machine environment: The monitor tracks the humidity and temperature, which can impact the energy required to ensure environment stability for the machine.
  • For the facility: The monitor tracks the entire manufacturing plant and identifies high level patterns that can negatively impact energy and cost.
  • The relationship between: Did the humidity around the machine increase the required energy for cooling? Are open windows requiring more energy? Or are closed windows not providing sufficient natural ventilation and thereby increasing cost to maintain the required temperature? Are the machines optimized?

This approach will help to identify patterns and factors that can help companies reduce their CO2 and costs most relevant to the customer’s specific context. Within this monitored environment, Seco UK can analyze a machining program and processes in fine detail — and further refine the programs by associating power consumption and CO2 emissions within the machine.

By harnessing independent monitoring of power consumption and facility usage, Seco can pinpoint the hidden costs of inefficiency and translate every kilowatt hour into real-time CO2e and expense metrics. This level of insight allows them to compare the energy demands and carbon footprints of any two products, programs or machines — empowering engineers to make data-driven decisions and embed sustainability at the heart of manufacturing design.

The test environment, complete with integrated monitoring for both Seco products and guest supplier machines, bridges Life Cycle Assessment of Seco with live machine data. By doing so, the engineers gain precise, component-level environmental scores and open the door to targeted innovation and validation of greener machining strategies.

“Looking ahead, we are building toward an AI-powered, automated feedback system that continuously optimizes processes in real time. This solution will balance productivity and cost imperatives with environmentally responsible best practices, ensuring every adjustment drives us closer to net-zero goals”, the company stated in a press release.

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