Artificial intelligence or AI remains one of the world’s top emerging technologies. GrandView Research predicts that the global artificial intelligence market will increase by 37.3% compound growth rate annually between 2023 and 2030 to attain a valuation of $1,811.8 billion.
The highly positive growth outlook for AI is partly driven by the emergence of AI turnkey solutions that allow enterprises and organisations to easily capture value from artificial intelligence applications. The prominence of AI, however, is attributed mainly to the launch of ChatGPT, a generative AI algorithm that made interacting with AI accessible and ‘human’, showcasing the advancements in generative AI development.
AI existed long before ChatGPT, of course; AlphaGo, DeepFace, Cimon, and Curial are just some AI iterations and systems that existed before ChatGPT. However, the launch of ChatGPT did highlight the vast potential of artificial intelligence, especially generative AI with natural language processing algorithms.
Many industries can (and have benefited) from AI applications, including manufacturing. The following are some of the AI use cases in the manufacturing industry.
1. Collaborative Robots
Collaborative robots or cobots are robots designed to perform tasks on shop floors alongside human workers. Cobots are not meant to replace human employees. Their primary purpose is to complement human work.
Manufacturing plants typically relegate cobots to:
- Repetitive tasks
- Jobs that might be too risky for humans
- Activities that require exactness and precision
For instance, cobots can perform:
- Sanding and polishing: Sanding, polishing and deburring surfaces are highly repetitive and require the precise application of force. They also pose a risk to human employees.
- Screwing: Drilling screws into parts is a highly repetitive task that requires concentration and precision. Humans can find it challenging to maintain their focus for hours, so human employees might apply varying amounts of force when putting screws in throughout their shift.
- Machining: Cobots can take over tasks that require placing materials on machining equipment, making the process safer for human workers.
However, don’t collaborative robots belong more in the robotics domain than AI? Well, AI makes cobots more effective as shop floor assistants.
There are AI programs that assist enterprises in deciding which tasks to delegate to cobots and humans.
Cobots must also be initially programmed and rely on advanced algorithms to accomplish the tasks they’ve been designed to perform and do them well.
Over time, cobots can become more efficient and productive with machine learning. Collecting data and information, they can exponentially improve their algorithms and, subsequently, their performance.
More importantly, AI makes cobots safer around humans. AI can imbue cobots with contextual awareness, allowing them to understand where they are in relation to human workers, other cobots and their environment.
Indeed, cobots fall within the purview of robotics, but AI can enhance their capabilities and safety. AI transforms cobots into cognitive robots, able to think, decide, and respond to known and new situations.
2. Digital Twin
A digital twin is a dynamic virtual or digital replica of a facility or system. It is not a static 3D model but a real-time digital representation of the object it is modelled after.
A digital twin of a shop floor gets its information or data from Internet of Things (IoT) sensors and metres connected through a low-power, wide-area network (LPWAN). These sensors relay information to edge computers that are equipped with applications to analyse data on the spot for a faster extraction of insights. These edge computers also transmit information to a central database in the cloud, where it is further processed to glean more business intelligence.
Digital twins provide feedback valuable in predictive maintenance(i.e., scheduling maintenance when failure is deemed imminent based on data rather than according to a preventive maintenance schedule). Sensors attached to machines and equipment can alert plant managers when pressure levels are climbing to hazardous levels. Digital twins can even help identify opportunities for improving efficiency and saving resources.
Manufacturing companies can also create digital twins of products or systems they’re developing. For instance, a company that makes high-density polyethylene (HDPE) pipes can conduct virtual HDPE piping systems testing through a digital twin of those systems.
As you can see from the above discussion, digital twins rely on IoT, communications technology (LPWAN, 5G), edge computing, the cloud, and data analytics. How does AI play with all these technologies to deliver exceptional digital twin solutions for manufacturers?
Without AI, digital twins diagnose and describe replicated physical systems using statistical data analytics. Since they are programmed to do specific things, their performance is limited.
AI allows digital twins to go past programmed restrictions. Through deep learning and machine learning, digital twins can use real-world data to:
- Schedule employees and factory processes.
- Tweak factory workflows to maintain overall equipment effectiveness at optimal levels.
- Provide insights on factory status, efficiency and productivity, allowing decision-makers to visualise interdependencies and constraints (e.g., raw materials, vendors) and how current stats affect stock levels, fulfilment and logistics.
- Proactively manage supply chains.
- Detect anomalies that can trigger predictive maintenance requests.
- Adjust operating conditions and schedule equipment use and asset maintenance for prescriptive maintenance.
- Make decisions or recommend workflows.
3. Material Pricing Forecast
Manufacturers can use an AI forecast engine to predict materials pricing with a high degree of accuracy. This ranges between 75% and 85% in the case of the AI pricing forecast tool developed by PricewaterhouseCoopers (PwC).
Raw materials are a significant expense. However, it can be challenging to plan for them when their prices are so volatile. Enterprises try to manage their risks with futures and options contracts, but with imperfect information, they sometimes do more harm than good, losing out on a deal rather than saving money on their raw materials purchases.
AI forecasting tools can significantly minimise the risks posed by the volatility and unpredictability of materials prices. Through AI, companies can predict pricing trends, so they can buy materials when they are most likely to save money and increase profit margins. They can also use futures and options contracts more effectively to improve their hedging and sourcing strategies.
Capture Value in Manufacturing With AI
Artificial intelligence unlocks productivity and efficiency in manufacturing. AI-driven applications include collaborative robots, digital twins and materials pricing forecasting engines.
It’s worth noting that the above examples, particularly cobots and digital twins, demonstrate that AI does not exist in a vacuum. This is why organisations that want to harness its power to extract value, improve productivity, and boost efficiency are much better off looking for custom-built turnkey and plug-and-play solutions instead of using a piecemeal-product approach to build their AI-powered systems.
Also Read: The Role of AI in the Evolution of Access Control Solutions
