We are currently beholden to a renaissance of innovation, with breakthroughs in disruptive technologies that are so profound they have initiated what has been dubbed a “Fourth Industrial Revolution”.
These technologies, such as AI and machine learning (ML), robotics, biotechnology, and the Internet of Things (IoT), aren’t limited to autonomous processes and systems: They’re leading the way to global environmental sustainability built on improvements in efficiency.
We’ll discuss how AI is used in seven manufacturing industries and many of the specific benefits it offers.
How Is AI Used to Improve Manufacturing?
Manufacturing is a complex orchestration of elements requiring coordination of various different roles and expertise in multiple disciplines. It necessitates managing tight production times, taxing equipment standards, a workforce trained in specialized tasks, and challenging process optimizations.
AI, ML, and their auxilliary technologies are vital to modern manufacturing facilities and their ability to contend with their competition. The following are several ways AI is used to improve performance across manufacturing operations.
Predictive Maintenance
AI is used to predict potential signs of equipment failure based on patterns observed from real-time data. Early preventative maintenance is significantly more affordable than replacing damaged equipment. Using AI, manufacturers save on equipment costs and retain hours of productivity that would otherwise be unnecessarily expended.
Quality Control
AI and ML assist in identifying potential defects and contaminants in materials. ML is trained to recognize anomalies and store them for future reference while updating recognition capabilities based on new data, continuously perfecting its accuracy.
Logistics
The efficiency of factory operations depends largely on how accurately an influx of products is correlated to shipping and delivery. Using AI to connect products and materials with shipping routes and supplier data helps optimize inventory and product shipping to avoid downtime and save on resources.
Optimizing the Supply Chain
A supply chain is comprised of an intricate network of well-timed, carefully plotted coordinations between suppliers, shipping, management, and procurement. AI optimizes each individual node in the supply chain structure to ensure that supplies remain aligned with demand, which prevents commonly-encountered but unnecessary downtime.
Cybersecurity
Cyberattacks can cripple factory operations, compromise equipment, and cause significant damage to organizations due to infiltrations conducted by rogue actors. AI-backed cybersecurity systems track threats and vulnerabilities 24/7, and are able to recognize aberrations in materials and equipment that could indicate tampering, hermetically sealing sensitive information.
A Closer Look at Ways 7 Industries are Using AI to Optimize Manufacturing Processes
The aforementioned AI implementations are universal applications in manufacturing facilities currently being adopted within every industry. Now, we’ll focus on seven specific examples of industries where AI is used for efficiency, sustainability, cost-efficacy, and product development.
1. AI in Automotive Manufacturing
As the world shifts towards a greater demand for electric vehicles, AI is being deployed in car manufacturing processes to scale operations while reducing costs.
Engineers implement AI for generative design tasks to resolve challenges that may arise in optimizing designs based on given constraints and objectives. With a repository of supporting data, AI provides the deep analytics needed to develop components with improved performance.
AI is used in tandem with robotics to automate repetitive, labor-intensive tasks such as part assembly, welding, and painting. AI-powered collaborative robots, called cobots, assist humans with their tasks throughout development and at the assembly line, where greater efficiency is generated by the cobots’ added strength, precision, and endurance.
2. AI in Food Manufacturing
AI is implemented in food manufacturing for multiple crucial purposes. As observed in other industries, food manufacturers seek ways to reduce costs, increase efficiency, and produce less waste.
AI’s contributions to supply chain infrastructure impact a factory’s connection to the food chain, where suppliers, restaurants, grocery stores, and manufacturers all share software built on AI platforms that use historical data to streamline supply chain connectivity.
Food processing equipment uses ML, sensors, and other AI-powered monitoring systems for overall equipment effectiveness (OEE).
3. AI in Biotech Manufacturing
AI is fundamental in automated optical inspection (AOI) for the biotechnology and genomics industries. It is used to detect errors and improve data integrity across all stages of manufacturing.
AI brings greater efficiency of production through quality control and is implemented to increase reliability in laboratory inspections and bioinformatics.
4. AI in Chemical Manufacturing
The global market for AI in chemical manufacturing is projected to grow from a value of $1.07 billion in 2022 to $16.94 billion by 2032. https://www.precedenceresearch.com/artificial-intelligence-in-the-chemical-market
Deep learning and ML are used to generate new product formulas based on forecasting from real-world experiments in order to predict better chemical combinations. AI-based robotics accelerate testing with around-the-clock experimentation.
Generative modeling guides chemists in discovering new molecules with potential therapeutic benefits. Simultaneously, AI is used throughout chemical manufacturing protocols to uncover ways to produce more environmentally friendly products using greener processes.
5. AI in Drug Manufacturing
According to the FDA’s evaluation and research, AI’s primary roles in pharmaceutical manufacturing include:
Process design
Advanced Process Control (APC)
Process monitoring and fault detection
Trend monitoring
AI is useful in maintaining quality standards, aligning instruments, preventative maintenance, and managing data integrity for regulatory oversight.
6. AI in Aerospace
Aerospace is one of the most technically challenging fields in terms of design, testing, and production. Leading manufacturing facilities use AI for non-destructive inspection (NDI) to recognize and isolate potential contaminants and errors in process or anomalies in foundational materials.
Aerospace facilities are often building aircraft of a highly sensitive nature that involves delicate matters of national intelligence. AI is used to provide added security with remote monitoring to identify the slightest variation in process that could indicate a sophisticated security breach or an attempt at sabotage.
7. AI in Textile Manufacturing
The use of AI in the textile industry provides additional evidence of its diverse use cases. AI-backed robotics are used in textile manufacturing for physical labor such as material cutting and handling.
AI is also used in R&D for developing better dyes and color matching, helping experts better predict how dyes will fade and improving the manufacturing process overall. Generative AI aids designers in pattern creation, fabric textures, and matching creative decisions to analytics collected from consumer preferences.
Optimize Your Manufacturing with Nanotronics
Nanotronics develops a flexible, full-factory AI solution with our AI-powered autonomous factory control platform, AIPC™. nControl™ brings AI optimization to smart factories by increasing yield without compromising quality.
nControl™ brings DLC Edge™ computing to manufacturing with integrated electronics, hardware, and software connected to facility-wide sensors. Our deep learning AI system creates predictive models based on continuous real-time data to protect your equipment, maximize up-time, and enhance efficiency.
Reach out to one of our experts to learn how we can assist in facilitating your operations.