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Manufacture-Artificial Intelligence and Machine Learning in Industry 4.0- B-AIM Pick Selects


What is Industry 4.0?

Success in any department can be achieved through practice, hard work, and planning. The industrialization has been a great success worldwide. Transformational effects appear and we live in a world that is interconnected. Here are the breakdowns of industrial development and the great changes in related categories.

Source: engineering.com

#Industry 1.0: The Industrial Age began due to rapid advances in science and mechanization, e.g. Power mills, steam engines, and railway lines.

#Industry 2.0: signified by the revolutionary Ford Company and automotive, e.g. Introduction of heavy production of assembly-line cars and electricity.

#Industry 3.0: The invention of semiconductor features and the popularity of computers, e.g. Automation in manufacturing, construction, steel, oil refineries and IT.

#Industry 4.0: Pattern change suggested and predicted by robotics, e.g. Human-machine interaction, cyber-physical systems, space tourism and exploring driverless cars.

Creative confluence:

Science is about precise principles, and technology is the goal of success. Business acumen should contribute to technology implementation. Above all, innovative creations should have effective applications. In recent years, there has been a lot of buzz around Artificial Intelligence (AI) and the Internet of Things (IoT) & AI SERVICES.

To read about: Top 10 best companies that use Artificial Intelligence (AI) to augment manufacturing processes in the era of Industry 4.0

IoT mainly deals with big data, predictive analytics, and cloud computing. Its mission is to revolutionize digital services using frameworks, platforms and connectivity architectures. The digitization of businesses and governments is expected to bring greater transparency and accountability. Future projections also include smart cities, adaptive cruise control and a brain/computer interface.

This leads to an exciting world of artificial intelligence, machine learning, cybernetics, neural networks, and deep learning. However, IoT does not stop with office automation and advanced communication.

Smartness is also being extended to the home, transportation, and industrial manufacturing. For example, humans and cyber-physical systems in a smart factory interact on the cloud. Remote monitoring of processes and decisions using big data analytics is also possible.

What is Artificial intelligence?

Artificial intelligence, as the name suggests, illustrates the ability of machines to simulate human mental prowess. However, this intelligence is not limited to machines — it also applies to software systems; Hence, the differentiation of the boundary between robotics and machine learning or AI & AI SOLUTIONS. The three main components are, therefore, machine or system, software and Internet connectivity (cloud and big data).

Machine:

The most popular example of this is undoubtedly robots and robotics. Thanks to science fiction and movies, everyone knows about them. Mechanical engineering techniques are used to convert metal into cars and human bodies. They are equipped with electrical circuits and electronic chips for control and command.

#Software: It is part of the AI that provides and manages the software mechanisms. Machines are not programmed solely for decision-making purposes. There is also the feedback or loop design so that the software can learn.

#IoT: In addition to sensory-motor functions, the system is also expected to interact. Stacking the system to the cloud is very useful for AI researchers. They can perform refined data analytics, custom research, and real-time communication.

Note: Robotics is not the only field of application for Artificial Intelligence (AI) and machine learning. The game-changing Industry 4.0 standard recognizes the role of humans and cyber-physical systems. Applications for manufacturing, health care, aerospace research, corporate sector, R&D and governance have been made.

#Machine learning: A subset of AI

Hardware, software including control engineering have worked magic since the 1960s. Modern buzzwords include high-performance computing, big data, parallelism, distributed systems, and quantum computing. Storage and processing capabilities, as well as algorithmic investigations, have made rapid progress. This has created a new interest in heuristics and learning methods for future generations.

Machines can only mimic the human skills of reasoning and knowledge acquisition. Humans learn through different methods because there is no “one-size-fits-all” solution. The learning takes place through example (simulation), trial and error (heuristics) and repetition or memory. Machine learning is usually described by experts as a subset of artificial intelligence. It is also highly rated by teaching researchers and computer scientists.

The aforementioned confluence of different technologies indicates the value of ML. This simplifies manufacturing and makes urban transportation more efficient. The medical, legal and governance fraternities are also expected to reap immense benefits. Justice, ML can improve the quality of medicine

Machine learning frees up the higher realm of business intelligence and administrative decision making. It achieves this through logical consistency, reliable assumptions, and resource visualization.

So, what is Machine Learning (ML)?

For a while, AI experts focused their research on modeling and computational teaching. From these experiences, the ML principles were summarized and carried forward. Currently, machine learning is concerned with algorithmic refinement and data modeling. Efficient algorithms are designed, built and analyzed using computers. Big data is also collected from various sources and is designed to produce more accurate estimates.

The strength of ML lies in inputs, data-driven analysis, and data acquisition. Unlike simple computer instruction, machine learning specializes in the assessment. Technically, the subject is closely related to computational statistics and probability. Therefore, it plays an important role in analytics attendance analytics for business solutions. Researchers refer to this topic as mathematical optimization and heuristic learning techniques.

Applications: Manufacturing sector

Machine learning methods are integral to image, face, and speech recognition. Smart agencies also serve as digital assistants, intelligent bots and speech processors. Other important uses are audio-visual analysis, automatic translation or transcription and driverless cars. Industry 4.0 is expected to benefit from MI methods in the following areas:

• Smart factories have a closely monitored, automated production process.

Collection Advanced digitized networks for data collection and transfer are installed.

• Mechanical data includes power, speed, force, weight, pressure, and so on.

Smart manufacturing is characterized by preventive measures and adaptive manufacturing.

• Machines, humans, software systems and products interact on the Internet.

Citations Checks, monitoring, changes, and communication can be automated.

Closing Point:

Computerized and automated industrial processes go hand in hand. Their confluence is achieved through the right combination of hardware and software. Artificial intelligence and machine learning techniques are also applied to heuristics. The future of the industry appears to be a combination of refined machines and custom software. These two important components can be plugged into a grid or framework.

Big Data Analytics and Cloud Computing Architecture guarantee versatility and scalability. Companies can improve production processes using the “Learning by Example” method.

Predictive analytics can help sharpen business intelligence. Profits can be spent on the newest embedded infrastructure. Operations can be scaled without compromising on the quality of the communications or the feedback of the machine.

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