AI’s Growing Role in the Manufacturing Industry
Artificial intelligence (AI) is currently at the heart of the manufacturing business, and it’s just growing stronger. Advanced technologies are streamlining the way complicated operations are managed in the manufacturing business, which is undergoing a huge upheaval. Manufacturers can optimise their operations in particular by reducing machine downtime, anticipating maintenance requirements, and maximising manufacturing floor resources. It is these areas, the role of AI in manufacturing takes an important turn, as Artificial Intelligence and Machine Learning makes it simple to collect data on machine performance and quickly find solutions.
The impact of AI on manufacturing
A recent study shows that AI is a collective name for learning system skills that are thought to symbolise intelligence, including image and video recognition, prescriptive modelling, smart automation, etc. AI use cases in manufacturing processes concentrate around the technologies listed below:
- Machine learning: the process of using algorithms and data to learn from underlying patterns without having to be explicitly programmed.
- Deep learning: The branch of machine learning that analyses images and movies using neural networks.
- Autonomous objects: collaborative robots or networked automobiles, are AI agents that manage tasks on their own.
AI for manufacturing is anticipated to expand from $1.1 billion in 2020 to $16.7 billion in 2026, representing a staggering 57 percent compound annual growth rate. The availability of big data, increased industrial automation, improved processing power, and higher capital investments are all contributing to the growth.
Heavy manufacturers have changed their tune on keeping operations efficient thanks to AI. They used to employ capital expenditures to fund upgrades (i.e. they spend lots of money on new equipment to replace malfunctioning equipment). Application of AI in manufacturing is a less expensive alternative that allows these organisations to:
- Analyze machine data more efficiently to develop preventative maintenance solutions,
- Replace manual monitoring operations by machine operators with automated AI decision-making on equipment state.
A cement factory devised an AI-driven “asset optimizer” to boost output from a vertical raw mill, according to a recent survey. Millions of lines of data from hundreds of process variables were swiftly gathered by the solution. It then used neural networks and analytical techniques to optimise process controls by mapping the data to automated workflows. The optimizer was able to run on autopilot, controlling the production process without the need for human interaction. The results: after eight months, the solution had improved by 11.6 percent above the manual method, allowing the manufacturer to increase earnings.
The importance of predictive maintenance is also to be highly noted in startup manufacturing business ideas, since Predictive maintenance is a concept that employs AI algorithms to forecast when machinery and equipment may fail. Artificial intelligence (AI) may be trained to continuously monitor equipment sensors, forecast when they are likely to fail, and offer proactive, condition-based maintenance regimens. Deviations from material formulations, minor variations in equipment behavior, and modifications in raw materials are all examples. As a result, there is less downtime and the equipment’s remaining useful life is extended.
A predictive maintenance method is highlighted in a significant use case from General Motors. The automaker examines photos captured by cameras mounted on assembly robots to look for symptoms of robotic component failure. The AI-powered technology discovered 72 instances of component failure in 7,000 robots in a pilot test, catching the problem before it impacted production with an unscheduled outage.
To understand better how the role of AI in manufacturing is becoming a highly regarded advantage, below explains the improving product safety:
Automobile tyres are critical safety components, thus companies like Bridgestone strive for perfection in terms of quality. To do this, the company developed a machine learning system that automates quality assurance, which is typically controlled by humans. The machine is controlled in real time by the system, which employs sensors to measure 480 quality items. By eliminating bottlenecks in the moulding process, product uniformity was increased by 15% over the human-controlled process, and productivity was doubled.