AI and Industry 4.0: Top Trends to look out for in 2020
Industry 4.0 and artificial intelligence are constantly redefining the limits of what is possible for scientists and engineers. 2020 will certainly be a critical year; expect the unexpected, as the possibilities are limitless.
Scientists and engineers are gaining more access to deep-learning models and Artificial Intelligence-related research as access to such information and technology is immensely improving. In general, AI models have been image-based for the most part, but next year they will incorporate a wider range of data: from sensor to time-series, to text and radar data.
The following are some key trends to look out for within the next 12 months:
- Model-Based Design Tools
- As more design-complex, AI-powered systems arise, the demand for more rigorous testing processes will increase due to the significant impact of the AI model behavior on overall system performance. In 2020, we can expect to see more adoption of model-based design tools that provide integration, simulation, and continuous testing.
- Through simulation, designers will be able to test how AI interacts with a system. Integration allows for trial designs in complete system context, while continuous testing enables designers to easily identify weaknesses in AI training datasets and design flaws in its components.
- Flexible Production Lines: Collaborative Robots (Cobots) and AI
- Collaborative Robots, also known as Cobots, which work alongside humans, will demonstrate the flexibility of production lines within the next year. For the past couple of years now there has been talk of a new vision for automation on the factory floor, which involves production lines producing single items to avoid inefficiencies and long changeover times.
- In order for this idea of full individualization in production to materialize and become part of Industry 4.0, it is imperative that production lines become flexible. It’s also important that they feature multiple mechatronic modules that have the possibility of being rearranged quickly and with ease, and also with more cobots available that can be tuned by AI according to a manufacture’s next individualized product.
- Predictive Maintenance Evolution with Edge Computing
- Paving the way for new functionality of software on production systems are cloud systems, greater calculation power of industrial controllers, along with edge computing devices.
- Data will be sourced across multiple sites and different vendor equipment, as opposed to an individual machine, thus improving predictive maintenance.
- AI algorithms will be able to optimize entire production line throughput while maintaining minimum energy costs, therefore enhancing overall efficiency in factories.
- Reinforcement Learning- Industrial Applications
- Reinforcement learning is where through repeated trial-and-error interactions, a computer learns to perform a task within a dynamic environment. This stems beyond beating human players in games such as chess, and will become a major support to engineers. Reinforcement learning will be utilized to implement controllers and decision-making algorithms for complex systems, such as robots and autonomous devices.
- The development of simulation data for training along with access to easier tools for engineers to create and train reinforcement learning policies are key drivers for the deployment of reinforcement learning as a way of improving large industrial systems.
- Easier integration of reinforcement learning agents into system simulation tools and code generation for embedded hardware are some other enablers for reinforcement learning. For instance, adding an RL (reinforcement learning) agent in an autonomous driving system can improve and optimise driver performance, increasing speed, reducing fuel consumption and response time.
According to Industry 4.0 Market, Technologies & Industry: 2019-2023 market report, The Industry 4.0 transformation will change long-held dynamics in commerce and global economic balance of power. Industry 4.0 Market Research forecasts that the market will undergo a major transformation in 2019-2023 via the following drivers:
- Fast growing market, expected to reach $1 Trillion by the early 2030.
- Global competition in the manufacturing sector is becoming fiercer and fiercer.
- Unprecedented opportunities to optimize production processes.
- Governments and the private sector of high labor costs economies invest in Industry 4.0 to increase their industrial base taken by low labor cost countries.
- The private sector and governments of low labor costs economies invest in Industry 4.0 to maintain their industrial base taken by high labor cost countries Industry 4.0 investments.
- Government-funded Industry 4.0 projects, R&D, subsidies and tax incentives.
- Industry 4.0 offers start-ups and SMEs the opportunity to develop and provide downstream services.
- Industry 4.0 dynamic business and engineering processes enable last-minute changes to production and deliver the ability to respond flexibly to disruptions and failures on behalf of suppliers and customers.
- End-to-end transparency provided over the manufacturing process, facilitating optimized decision-making.
- Industry 4.0 provides the link to the consumer, and can forecast consumer demand.
The Industry 4.0 competition is not only about technology or offering the best products; it is also, and in particular, about the companies that gather the appropriate data, combine them to provide the best digital services, and in addition, utilize the data for their own benefit. Those who know what the customer wants, and can forecast consumer demand, will provide the information to develop an unfair competitive advantage.
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