Artificial intelligence (AI) has been playing a crucial role in the growth of manufacturing industries by boosting productivity and efficiency. The article below explores some of the trends that AI technology is bringing in the modern manufacturing industries like smart factories, 3D printing and blockchain. These trends ensure more flexibility and improve speed and quality of products

A smart manufacturing unit is different from a traditional manufacturing unit in many ways. A typical modern manufacturing unit is not a clumsy industrial space with a few or thousands of human workers or robotised production units under one roof. With the advancement in technologies, such as artificial intelligence (AI), machine learning and the Internet of Things (IoT), there is much more than the traditional manufacturing processes and setups. AI is being employed in manufacturing to make businesses more efficient by extending human capabilities. There are many areas where AI is helping to improve the processes of manufacturing industries and boost productivity and efficiency. This article covers some of the AI trends that have revolutionised the modern manufacturing industry including smart factories, 3D printing, AI-robots, generative designs and blockchain.

1. Smart factory
Automation in manufacturing or factory is not a new thing. A smart factory is based on the modern industrial automation concept. It is a highly digitised and connected production facility, considered as an outcome of the fourth industrial revolution or Industry 4.0. The smart factory employs modern technologies such as AI, robotics, analytics, Big Data and the IoT. With the use of modern technologies, the smart factory systems can learn and adapt in real-time, enable more flexibility in operation and run without much human intervention. The characteristics of the smart factory include visibility, connectivity, and autonomy. Asset management, supply chain management and inventory management are some of the hottest areas of AI, machine learning and IoT adoption in a smart factory.

2. AI and 3D printing
3D printing is mainly used in rapid prototyping, design iteration, and small-scale production. This technology is constantly evolving and improving in the manufacturing industry. Improvements in speed, quality and materials could make 3D printing useful in mass production and mass customisation. The combination of AI and 3D printing brings lots of new excitement and flexibility in additive manufacturing technology. The role of AI in 3D printing technology is in the software. In other words, the intelligence of a 3D printer lies in the software program. AI algorithms are being incorporated into 3D modeling programs to help you optimise the 3D printing process and also create the best 3D printable models.

3. AI and IoT
The emerging technologies like AI and IoT have been going on well especially in the manufacturing industry. These technologies have been revolutionising the mass production of goods and boosting the output of different industries.
In the manufacturing industry, the IoT involves the use of devices/sensors, cloud computing and the Internet for communication, and AI software to compile massive amounts of data for intelligent machine learning. Through IoT technology, machines can seamlessly talk to each other and react to any problems that arise. IoT system can quickly alert other machines and human workers—allowing the problem to be addressed in real-time.

4. AI-robot
When we talk about robots in manufacturing industries, there are two technology terms that come into the picture: cobots and robotic process automation (RPA). Cobots were first coined by professors at Northwestern University in America. Unlike traditional industrial robots, cobots or collaborative robots are the latest generations of robotic systems intended to work alongside humans. While cobots are robots intended to interact with humans in a shared space or to work safely in close proximity, RPA is a form of business process automation technology based on metaphorical software bots. That is, unlike the physical robotic machines, RPA is software that acts as an additional employee in various disciplines throughout the supply chain, automating certain activities, minimising human errors, and maximising productivity. Actually, RPA works behind the scenes, replicating human minds, accelerating operations and transforming business processes.

The robotics market is growing at a great speed across the globe. There are millions of robots working in factories all over the world. With the emergence of AI, robots are becoming smarter, cheaper and easier to use. Cutting-edge AI techniques will help the industrial robots to improve the speed, accuracy and safety of production.

The most common robots are pick and place, packaging, welding and assembly robots. Other manufacturing robots include cutting, drilling, milling and painting robots. Assembly robots are used on automated assembly lines including automotive, consumer electronics and electrical appliances. AI-powered computer vision is one of the most important modern researches in robotics. Some assembly robots employ computer vision and sensors to identify, grasp and manipulate parts. Some assembly robots can also handle assembly of parts that are too small for human hands to handle them accurately.

5. AI and Quality 4.0
The core concept of Quality 4.0 aligns quality management with Industry 4.0 techniques. Technological advances in Industry 4.0 such as advanced analytics, AI and machine learning can help organisations achieve real-time visibility of vital quality metrics, supplier performance, and customer services.

Due to the rising complexity in the products and the spare parts, manufacturers may face the challenge to maintain high-quality products. Quality 4.0 ensures improved quality of products and enhanced output efficiency by using AI algorithms. If any kind of issues are found at the initial stages, they can be taken care of immediately.

6. Predictive analytics
AI, machine learning and IoT are used in modern manufacturing industries. Here, data is one of the most important assets. Predictive analytics tools are used to analyse the incoming data to identify problems in advance. AI-powered predictive analytics promises to revolutionise analytics. Predictive tools give manufacturers the power to analyse massive amounts of data and discover correlations between critical variables, enabling them to address the root causes of problems before quality issues occur. Predictive quality is one of the most important use cases in manufacturing. Predictive maintenance makes use of AI algorithms to predict failures of a system or machine to prevent failures and prevent downtimes in the manufacturing units.
With advanced data analysis and real-time tracking, machinery maintenance can be scheduled before any problem arises. These technologies help drive operational efficiency, reduce the manufacturing operating expenditure and improve productivity.

7. AI and generative designs
A designer in the manufacturing industry first establishes a request-for-quotation, provides the design assets, indicates the processes required, such as dimensions, materials, volume and turnaround time. Depending on the product, the actual designing phase involves lots of imagination and creativity of the designer.

With the generative design concept, a designer is no longer limited by his own imaginations or past experience. The designer just needs to input design parameters (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software, and the AI algorithm explores all the possible combinations of a solution, generates many or thousands of design options. The designer can filter from the generated design options and select the best options that would meet his requirements.

8. AI and cost optimisation
One of the major benefits of AI in manufacturing is cost optimisation. AI enables manufacturers to cost-effectively create and maintain their own algorithm in-house, which is cheaper, more versatile, and more adaptive to constantly changing market conditions. AI can fully automate complex tasks, provide consistent and precise optimum set points. It requires less manpower to maintain and adjust quickly when management revises manufacturing strategy and production plans.

In some cases, AI can partially or even fully replace certain employees and you need not include their salaries in your budget. AI is used to cut maintenance and repair costs.

9. Inventory optimisation
Inventory management plays an important role in manufacturing because it can make or break a business. One item out of stock can bring a business to its knees. Many companies run into losses due to bad inventory management. Unlike inventory management in retailers and wholesalers, which consists items ready for sale, a manufacturing company’s inventory will include goods in various stages of production, from raw materials to products ready to ship to customers. A typical manufacturer uses three types of inventories: raw materials, work in process and finished goods. Raw materials including steel, wood, plastic, chemicals, etc, are the main inputs of production. Work in process represents goods that are still in the production process. Finished goods are those that are awaiting sale or ready to ship to customers.

AI in inventory provides many benefits for the companies that are utilising it. For example, in the car manufacturing industry, smart warehouses are inventory systems, where the inventory process is partially or entirely automated using modern technologies such as AI, machine learning and Industrial Internet of Things (IIoT). These technologies enable inventory systems to connect each process, collect data at each of the nodes, continuously learn and optimise the process and increase productivity. AI and machine learning models are able to scale inventory optimisation across all distribution locations and independent variables that affect demand and supply performance.

10. AI and blockchain
Many products are being manufactured globally but do you know from where these products originated, how and when they were produced and shipped to the buyers? The demand for transparency in the manufacturing supply chain has significantly increased in today’s global market. Blockchain can be used in manufacturing and perfectly fits for this solution to track manufactured goods through the supply chain.

Blockchain involves a digital ledger that is continuously updated to record and track transactions, accounting and asset movement. It is new digital information, which stores data in an encrypted, distributed ledger format. AI can manage blockchain efficiently because AI and encryption work very well together. In manufacturing, blockchain can be used to establish an organised digital thread, track the history of a part, from its digital design to production all the way to end of life. This digital data can be shared with multiple parties that get access to the same information.
Some of the advantages of blockchain include monitoring supply chain for greater transparency, materials provenance and counterfeit detection, engineering design, identity management, asset tracking, quality assurance and regulatory compliance.

—Sani Theo