Now more than ever, manufacturers are integrating new technologies, such as IoT, cloud computing and analytics, and AI and machine learning into their production facilities and operations.
Manufacturers are adopting AI/ML to increase efficiencies and cut costs. Using AI/ML to monitor and analyze plant operations reveals production chokepoints, potential equipment failures, and areas where automation can streamline operations.
Simple rule-based algorithms have been used in the visual inspection market for many years, but as their limits have become more apparent, the need for more sophisticated software has grown.
Predictive Quality Analytics in Manufacturing: How AI and Machine Learning are Transforming the Industry
With predictive analytics quality management, manufacturers may now identify and avoid quality issues before they arise by utilizing cutting-edge technology like AI and machine learning (ML), which has significantly improved productivity and profitability.
The pandemic brought supply chain to the forefront, leading companies to seek AI, analytics, and automation solutions for efficiency and meeting demand. Machine Learning predicts demand and AI/automation boosts quality and monitors events.
The conventional deep learning model is a supervised model. It takes months of time to develop and train the model before it is ready for the production line.
Purpose-built solution AI and machine vision solution on Intel® edge processors are helping manufacturers successfully adapt the defect detection technology and expand it to address broader Industry 4.0 use-cases.
Swedish philosopher Nick Bostrom once said, "machine intelligence is the last invention that humanity will ever need to make". AI decision making, with real time communication and data analytics, has the ability to transform the way manufacturers understand machines.
Without making wholesale production changes, factories can improve process efficiency with machine learning (ML). ML can increase production capacity by up to 20% while lowering material consumption rates by 4%.
Artificial intelligence (AI) is one of the fastest-growing technologies in the business world today. AI is a broad umbrella, covering many specific technologies, and machine learning is one of the most promising of these.
New data- and ML-based business models will become more commonplace, so early adopters will have a competitive advantage. Machine learning will happen both in the cloud and on the edge, i.e., directly on or next to production machines and sensors.
There are a number of ways to maintain industrial machinery. If you search around, you will most likely come across these terms: preventive maintenance and predictive maintenance.
We are entering a new manufacturing era defined by AI, machine learning and vision, enabling us to teach machines to adapt and learn plans and move us toward a true "Lot Size 1" utopia. Automation's next era will be defined not around volume, but around intelligence.
Autonomous Maintenance is one of eight pillars that make up Total Productive Maintenance (TPM). It's also one of the most important activities that eventually determine the success of any TPM implementation project.
Iota has developed a framework called the Sustainability Process Blueprint that allows businesses to easily monitor, measure, and curate data pertaining to their sustainability performance.
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Our retention knobs are manufactured above international standards or to machine builder specifications. Retention knobs are manufactured utilizing AMS-6274/AISI-8620 alloy steel drawn in the United States. Threads are single-pointed on our lathes while manufacturing all other retention knob features to ensure high concentricity. Our process ensures that our threads are balanced (lead in/lead out at 180 degrees.) Each retention knob is carburized (hardened) to 58-62HRC, and case depth is .020-.030. Core hardness 40HRC. Each retention knob is coated utilizing a hot black oxide coating to military specifications. Our retention knobs are 100% covered in black oxide to prevent rust. All retention knob surfaces (not just mating surfaces) have a precision finish of 32 RMA micro or better: ISO grade 6N. Each retention knob is magnetic particle tested and tested at 2.5 times the pulling force of the drawbar. Certifications are maintained for each step in the manufacturing process for traceability.