• March 3rd 2022
  • Technology
Cloud computing, industry 4.0, AWS, digital transformation

Artificial intelligence (AI) and machine learning (ML) are the primary change agents in the manufacturing industry today. AI and machine learning are allowing manufacturing to become more automated, increasing efficiency and driving down the cost of goods that consumers use every day.

AI uses computers and machines to mimic the human mind's problem-solving and decision-making abilities, allowing systems to detect objects and make predictions with incredible accuracy and speed. When combined with traditional CV, AI can improve manufacturing efficiency and lower production costs by accelerating anomaly detection in factories

However, implementing AI solutions for industrial automation has proven difficult. When compared to traditional CV, AI and ML techniques are still relatively new in industrial automation. Manufacturing automation engineers do not yet have the expertise to create effective AI algorithms. Several AI technology companies are removing these barriers by offering a complete inference solution, which includes high performance and low-power hardware in a small form factor, as well as ready-to-deploy AI algorithms. More investment will be made in this area as more factories seek to improve efficiency and workplace safety by utilizing powerful AI processing solutions.

The machine learning process is results-driven in that it provides team-agnostic, easy-to-digest insights. In larger facilities, where physical distance and operational breadth force teams to divide and conquer, it's all too easy for teams to become siloed. This isn't always the best approach, especially when problems affect multiple teams. Machine learning empowers teams to review centralized analytics. This centralized source of information makes it easier for stakeholders to reach a common understanding.

Transparency is built into modern, digital industrial equipment. It's much easier to keep track of multiple machines and their performance. Changes in efficiency, output, pressure, and even thermal activity can all be indicators of impending problems. Machine learning algorithms can be used to monitor these systems. Models can tell us when a machine is having problems or is in danger of having problems at regular intervals. Thus, continuous learning aids machine learning technologies in determining predictive maintenance schedules. Previously, technicians bore this burden. Even well-known brands are jumping on the IoT bandwagon. These machine learning models can be used throughout the factory to evaluate the health of customized machinery groupings.

The industry is reliant on obtaining raw materials, mechanized capital, and other resources required for production. The steps in the process are referred to as the supply chain. Machine learning models can assist businesses in identifying the lowest-hanging fruit — or the area's most ripe for improvement. By eliminating pesky delays and extraneous costs, tightening up the supply chain ensures higher production. Machine learning can show us where our expenses are coming from, how to improve scheduling, and how each provider's role in the chain changes on a daily basis.

The goal of industrial automation, AI and machine learning is the same: to optimize workflows and remove productivity barriers by automating time-consuming tasks. Machine learning algorithms are adaptable and ready to adapt to the changing industrial landscape. Because these algorithms have a proclivity for learning from data, they will not lose effectiveness — they will simply improve over time.

As the need for factory automation grows, factories will increasingly rely on AI-powered machines to improve the efficiency of day-to-day processes. This paves the way for even smarter applications to be introduced into today's factories, ranging from smart anomaly detection systems to autonomous robots and beyond.

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