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ARTIFICIAL INTELLIGENCE
alongside machine vision systems for quality control. Visual quality control systems can facilitate data collection to build the datasets required to train AI models. The same systems could also become visual input sources for analysis and decision-making, feeding directly into AI models to extract insights. Machine metrics: Robust data production equipment will be vital in enabling AI for predictive maintenance. Manufacturers can collect valuable insights on machine performance and diagnostics via cloud- based monitoring solutions. Historical data can be used to train AI models, while AI algorithms can analyse real-time machine
SOUND DATA COLLECTION IS THE ROUTE TO AI
Mohammed Salifu, Group Data and Analytics Director, Domino Printing Sciences, outlines the enormous potential of AI – but says its successful adoption demands careful planning around data collection and curation
rtificial intelligence (AI) is anything but new, yet it has gained both public and corporate traction in the last year or so, spearheaded by the launch of Open AI’s generative AI platform, Chat GPT. AI describes any application of computer software that allows machines to mimic human intelligence to enable problem-solving - be this with vision, speech, or interpretation of data. It’s an umbrella term that describes several methodologies, including robotics, image analysis, language processing, machine learning, and artificial neural networks.
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On a basic level, artificially intelligent systems identify patterns through algorithmic data analysis. More complex systems can learn from experiences, solve problems, and make decisions without human intervention. Today, AI applications are in use across a wide range of industries: Food and beverages: Campbell Soup Company uses AI to analyse consumer preference data and agile design methodology to accelerate the development of new products. Waste and recovery: Greyparrot, a company specialising in AI-generated waste analytics has developed computer vision systems for waste identification at materials recovery facilities. Coding and marking: Domino, incorporates aspects of AI to target values for new formulations and automate testing to speed
38 MARCH 2024 | PROCESS & CONTROL up the ink development process.
There are three key areas where AI is proving valuable in the manufacturing industry: 1. Error reduction: AI systems can be developed to understand and analyse all types of visual data, including data from quality control systems on production lines, to identify patterns that could indicate wider production issues, and avoid waste and errors. 2. Predictive maintenance: Data from maintenance logs and production line performance can be used to predict machinery performance and when parts need replacing, or maintenance is required. 3. Forecasting: With a thorough dataset including information on plant operations, production performance, and sales and feedback, AI systems can forecast demand, helping manufacturers streamline inventory and pre-plan production runs. Any use case for AI – whether in
manufacturing or any other sector – requires a dataset large enough to train an AI model. Indeed, data is the first step in any AI journey and is arguably the essential part of the process, without which attempts to implement AI are destined to fail. As such, reliable data collection must be established across all necessary production activities before getting started. The existence of a joined-up governed data architecture, is a prerequisite to a successful AI implementation. Quality control: AI applications lend themselves particularly well when used
data to predict when maintenance is needed. Production data: Wider production data will be required for AI in performance optimisation, predictive maintenance, and forecasting. Upstream and downstream data: When combined with other production monitoring systems, variable data coding, at the batch or item level, can be used to tie individual products back to the production line. A serialised product code will allow the identification of products if required, providing a route to trace back and uncover precisely when and where they were made. The value of variable data codes can extend far beyond the factory as products move through the wider supply chain and into the hands of consumers. A scannable code with a unique serial number can be used to gather customer feedback and associate it back to the product’s unique production history. This traceability not only helps with identifying where issues arise but can also help brands to collect data on consumer preferences and trends.
Collecting this information during production and beyond is another part of a complex toolkit to help manufacturers get to a point where their data is robust enough to consider investigating AI applications. Preparing for an AI manufacturing project will require sufficient resource allocation to implement new systems, develop datasets, train AI models, and monitor and analyse progress. While AI raises concerns around the replacement of human workers for the short term at least, the opposite is true. As Forbes suggests, AI will enable workers to focus on more meaningful and high-value activities. Preparing a workforce for AI will be ongoing. Businesses must invest in learning and development to ensure employees have the skills necessary to progress.
Domino Printing Sciences
www.domino-printing.com
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