Industrial Applications of Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) is revolutionizing many aspects of our lives, including how we work and even how we play. AI is driving emerging technologies like self-driving cars and facial recognition, among others, and vastly enhances established ones like board games, search engines, and medical diagnostics.

Machine Learning (ML) is a subset of artificial intelligence.

Machine Learning (ML) is the ability of a program to learn a task without being expressly designed to do so. Algorithms that use ML find patterns and learn to forecast and give advice. ML is typically much better at making such predictions than conventional techniques.

Depending on how they learn, ML algorithms are often categorized as supervised, unsupervised, and reinforcement learning. While reinforcement learns through feedback or “reinforcement,” supervised and unsupervised approaches do so through data.

Organizations are under pressure to streamline their processes for the new normal. ML has become a major priority across businesses since unpredictability and volatility persist throughout the value chain and global ‘shocks’ (such as trade disputes, natural catastrophes, cyber-attacks, etc.) are anticipated to become more frequent. Now, the industrial market is seeing a large return on their investment in data, analytics, artificial intelligence (AI), and ML.

Industrial Applications of Artificial Intelligence and Machine Learning

Although there are many different and varied use cases for AI and ML in industrial markets, their behavioral characteristics are generally similar. A good AI or ML program is a valued partner, anticipating requirements, handling tasks, and offering reliable advice and recommendations.

Below are a few illustrations of the kind of helpful support AI and ML may offer during the course of a product’s or asset’s lifecycle.

Predictive Maintenance and Field Operations

Predictive maintenance, such as the capability to foresee when an equipment failure will occur to minimize costly downtime expenses, is the most frequently mentioned industrial application of AI.

However, AI also has the potential to build and scale its advantages to all field activities, upstream, downstream, and beyond these specialized AI-powered prediction models.

Planning for maintenance, repairs, and operations; creating recommendations for preventive and predictive maintenance; analyzing quality problems; automating routine operations and maintenance tasks with robots, autonomous vehicles, and drones; interpreting and feeding operational data back to teams working on service design; and interpreting and sharing performance data, among other things, are all made easier by ML-enabled field support.

Design

ML is used in iterative design during the conceptual stage of product development in conjunction with simulation, virtual engineering models, and simulation.

With the use of these tools, millions of design possibilities may be quickly cycled through, with recommendations for the best options depending on a variety of factors such as cost, sustainability, time, regulatory constraints, etc.

Additionally, AI and ML are helpful in the first stages of brainstorming. To assist designers in exploring pre-existing design concepts through text and image searches, cognitive search tools can be deployed.

Furthermore, by analysis of data from sources like social media or internal consumer feedback systems, AI and ML can assist designers in comprehending customer demand.

Testing

ML may also be used to create extremely accurate digital replicas of real-world systems and objects. This makes it possible to create behavioral models with realistic behaviors that can be applied to performance simulations. Physical prototyping has all but been replaced in various industries due to the widespread adoption of digital models.

Manufacturing

Aside from planning production lines and systems, ML-powered digital modelling and simulation (including virtual reality systems) is also used to develop and integrate smart equipment, smart robots, and production-line drones, recommend and carry out proactive maintenance (preventive/predictive maintenance), and funnel crucial production data back to teams working on product design and specifications.

Sales and Marketing

Applications of AI and ML are being used in the commercialization phase to predict demand trends; deliver highly personalized/micro-targeted marketing; develop intelligent, multilingual bot assistants for self-service ordering and support; power sales and marketing-related virtual and augmented reality applications; and customize goods and services.

Artificial Intelligence and Machine Learning: Changing the Face of the Industrial Market

The most crucial benefits of applying AL and ML to industrial sectors include increased innovation, process industrialization and optimization, automation, and enhanced quality, the latter of which frequently produces results more quickly.

Increasing quality and lowering direct and indirect costs associated with rework, waste, warranty claims, and recalls have been made possible by real-time monitoring systems for early issue detection, advanced cognitive systems for issue investigation, predictive maintenance systems, and feedback loops for design teams.

AI and ML offer the constant digital continuity required for ongoing product and process innovation when they are implemented via a shared collaborative platform and share common digital models of items and assets. This makes immediate wins in quality improvement an important lever for exponential gains through accelerated innovation.

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If you would like to find out more about how AI, ML and our 3DEXPERIENCE platform can revolutionize your business, get in touch today – we would love to hear from you.