Proof in the power of data
20th Mar 2020 | In News | By Michael Tyrrell
Jon Stammers, AMRC technical fellow in the process monitoring and control team, says: “Data is available on every shopfloor but a lot of time it isn’t being captured due to lack of connectivity, and therefore cannot be analysed. If the cloud can capture and analyse that data then the possibilities are massive.”
Engineers in the AMRC’s Machining Group have researched the use of the cloud to capture data from machine tools with new Tier Two member Amido, an independent technical consultancy specialising in assembling, integrating and building cloud-native solutions.
Stammers adds: “Typically we would have a laptop sat next to a machine tool capturing its data; a researcher might do some analysis on that laptop and share the data on our internal file system or on a USB stick. There is a lot of data generated on the shopfloor and it is our job to capture it, but there are plenty of unanswered questions about the analysis process and the cloud has a lot to bring to that.”
In the trial, data from two CNC machines in the AMRC’s Factory of the Future: a Starrag STC 1250 and a DMG Mori DMU 40 eVo, was transferred to the Microsoft Azure Data Lake cloud service and converted into a parquet format, which allowed Amido to run a series of complex queries over a long period of time.
Steve Jones, engagement director at Amido, explains handling those high volumes of data is exactly what the cloud was designed for: “Moving the data from the manufacturing process into the cloud means it can be stored securely and then structured for analysis. The data can’t be intercepted in transit and it is immediately encrypted by Microsoft Azure.”
“Security is one of the huge benefits of cloud technology,” Stammers comments. “When we ask companies to share their data for a project, it is usually rejected because they don’t want their data going offsite. Part of the work we’re doing with Amido is to demonstrate that we can anonymise data and move it off site securely.”
In addition to the security of the cloud, Jones says transferring data into a data lake means large amounts can be stored for faster querying and machine learning.
“One of the problems of a traditional database is when you add more data, you impact the ability for the query to return the answers to the questions you put in; by restructuring into a parquet format you limit that reduction in performance. Some of the queries that were taking one of the engineers up to 12 minutes to run on the local database, took us just 12 seconds using Microsoft Azure.
“It was always our intention to run machine learning against this data to detect anomalies. A reading in the event data that stands out may help predict maintenance of a machine tool or prevent the failure of a part.”
Storing data in the cloud is extremely inexpensive and that is why, according to software engineer in the process monitoring and control team Seun Ojo, cloud technology is a viable option for SMEs working with the AMRC, part of the High Value Manufacturing (HVM) Catapult.
He says: “SMEs are typically aware of Industry 4.0 but concerned about the return on investment. Fortunately, cloud infrastructure is hosted externally and provided on a pay-per-use basis. Therefore, businesses may now access data capture, storage and analytics tools at a reduced cost.”
Jones adds: “Businesses can easily hire a graphics processing unit (GPU) for an hour or a quantum computer for a day to do some really complicated processing and you can do all this on a pay-as-you-go basis.
“The bar to entry to doing machine learning has never been lower. Ten years ago, only data scientists had the skills to do this kind of analysis but the tools available from cloud platforms like Microsoft Azure and Google Cloud now put a lot of power into the hands of inexpert users.”
Jones says the trials being done with Amido could feed into research being done by the AMRC into non-geometric validation.
He concludes: “Rather than measuring the length and breadth of a finished part to validate that it has been machined correctly; I want to see engineers use data to determine the quality of a job.
“That could be really powerful and if successful would make the process of manufacturing much quicker. That shows the value of data in manufacturing today.”
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