Artificial Intelligence for Intelligent Manufacturing

Artificial Intelligence is the science concerned with the creation of machine intelligence which is able to perform tasks, only performed by people. Much of this machine intelligence is symbolic and heuristic. Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving.AI research is divided into subfields that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.

Globalization of the world economy and growth of competition on the market impose increasingly greater demands on modern entrepreneurs. Currently, management and control of production enterprises is impossible without an application of appropriate tools supporting decision making at each stage of a company's functioning from designing through to product exploitation. CIM (Computer Integrated Manufacturing) Systems are an example of such available tools that enable composite automatization of technological and organizational preparation for manufacture, current supervision, technological process control, organization and management. The development of CIM Systems has, in recent years, been directed at applying the methods of artificial intelligence to support decision processes and production control as well as monitoring, simulation and technological process diagnosis.

Intelligent Manufacturing is:

“A set of methods, procedures and tools (e.g. CAD, CAP, CAM) equipped with artificial intelligence tools and supporting designing, planning and manufacturing.”

The following, among others, are the basic constituent elements of Intelligent Manufacturing Systems mentioned as follows:

- Intelligent machines and tools, i.e. numerically controlled machines and robots,

- Intelligent manufacturing systems, and

- Intelligent management systems.

The concept of intelligent manufacturing combines the ability of decision-making support systems in generative

systems to obtain knowledge, to learn and to adapt to a changing environment and to the actual arrangement of

system components. The nature of intelligent manufacturing is system’s possibility to learn and its self-development as well as the possibility to generate information necessary to control the integrated production system.

Designing Intelligent Manufacturing System

Intelligent Manufacturing System of a production enterprise denotes a manufacturing systems integrated with information system which provides necessary information, enables its analysis and use of analytical and simulation - based decision making models in order to assist decision making at each stage of decision process, as well as it is capable of learning and adapting to the dynamically changing environment and the current arrangement of system components. In other words, it is a decision supporting system based on the applied methods and tools of artificial intelligence able to solve complex decision problems, semi structured or non-structured, requiring the processing of incomplete, unreliable, contradictory, or difficult to formalize knowledge.

The demands towards Intelligent Management Systems in a production enterprise are as follows:

- A possibility of collecting and processing different types of information from all sources, both internal and external, in order to acquire and model knowledge necessary to make decisions at all levels of decision process in an enterprise. At the same time a possibility of modeling knowledge and processes, based on human thinking, is required.

- In a decision process, at decision selecting, the decision maker’s subjective evaluation based on his experience and intuition should be taken into account in IMS.

- There should be a possibility of preliminary information handling and analysis with analytical methods as well as modern artificial intelligence technologies.

- A possibility of detecting emergency and critical situations and of prompt reaction to them. There must be a possibility of situational data analysis in real time, necessary in an emergency inside the production system or in its surroundings.

- A possibility to allow for complexity and comprehensiveness of decision-making issues in strategic management support.

- Taking into account the lack of stability and change dynamics, both in the surroundings and inside the enterprise, the IMSs under design should have the capability for learning from experience and adapting the experience to intensive alteration of working conditions.

In intelligent manufacturing systems, the following selected contemporary methods and techniques of knowledge and decision process modeling should be integrated:

- Artificial neural networks – the most fascinating tool of artificial intelligence, capable of modeling extremely complex functions and, to some extent, copying the learning activity in the human brain.

- Fuzzy logic – technologies and methods of natural language formalization, linguistic and quality knowledge processing and fuzzification.

- Genetic algorithms and methods of evolutionary modeling – learning algorithms based on theoretical achievements of the theory of evolution, enriching the artificial intelligence techniques.

The combination of these tools, in which knowledge is represented symbolically, with the traditional expert system will make it possible to create complex programmatic tools for solving difficult decision-making problems at each stage of enterprise functioning.

Artificial Intelligence and Control Engineering

Artificial intelligence (AI) relates to control engineering is when embedded software helps with situational awareness. The software algorithm looks at feedback from a situation, then actuates the decision (closed-loop control) without human consultation, or the software recommends a course of action with human consultation (open-loop control).Control engineering or control systems engineering is the engineering discipline that applies control theory to design systems with desired behaviors. The practice uses sensors to measure the output performance of the device being controlled and those measurements can be used to give feedback to the input actuators that can make corrections toward desired performance.  Control engineering is the engineering discipline that focuses on the modeling of a diverse range of dynamic systems (e.g. mechanical systems) and the design of controllers that will cause these systems to behave in the desired manner. Although such controllers need not be electrical many are and hence control engineering is often viewed as a subfield of electrical engineering.

There are two major divisions in control theory, namely, classical and modern, which have direct implications over the control engineering applications. The scope of classical control theory is limited to single-input and single-output (SISO) system design, except when analyzing for disturbance rejection using a second input. The system analysis is carried out in the time domain using differential equations, in the complex-s domain with the Laplace transform while modern control theory is carried out in the state space, and can deal with multiple-input and multiple-output (MIMO) systems. This overcomes the limitations of classical control theory in more sophisticated design problems. Control engineering was all about continuous systems. Development of computer control tools posed a requirement of discrete control system engineering because the communications between the computer-based digital controller and the physical system are governed by a computer clock. The equivalent to Laplace transform in the discrete domain is the Z-transform. Today, many of the control systems are computer controlled and they consist of both digital and analog components. Therefore, at the design stage either digital components are mapped into the continuous domain and the design is carried out in the continuous domain, or analog components are mapped into discrete domain and design is carried out there.

In manufacturing, a machine running a web-based process may have similar situational awareness. There may be a perfectly good reason to leave the machine running when the last material runs through the rollers and an operator is standing in a certain location. If the machine is unattended at that particular moment, embedded code may begin an orderly shutdown as the best response. Control Engineering relates to the next big thing (TNBT) which is the second generation of smartphones, which have the software capacity to provide situational awareness. TNBT devices will be able to recognize what is going on inside your area or site and determine when something is out of normal but not yet in alarm. Information for this awareness may come from traditional fixed sensors or even by listening for sound patterns such as hisses, whistles, clangs, and bangs. TNBT devices will become true operator assistants; always watching and always listening for out-of-normal conditions or for events that require manual intervention.

Seven Artificial Intelligence (AI) tools have proved to be useful with sensor systems: Knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning, and ambient-intelligence. Applications of these tools within sensor systems have become more widespread due to the power and affordability of present-day computers. The appropriate deployment of the new AI tools will contribute to the creation of more competitive sensor systems and applications.

Artificial Intelligence (AI) helps computing in four ways:

1. Natural language understanding to improve communication.

2. Machine reasoning to provide inference, theorem-proving, cooperation, and relevant solutions.

3. Knowledge representation for perception, path planning, modeling, and problem solving.

4. Knowledge acquisition using sensors to learn automatically for navigation and problem solving.

Artificial intelligence's ability to function as a safety measure and provide another set of eyes, so to speak, can be extremely beneficial to worker safety in manufacturing. It can also enhance our ability to understand what's happening around us and offer solutions that might not be readily available.  As artificial intelligence becomes more prevalent across industries, from defence to aerospace, experts in the field will focus on making AI as safe and useful as possible. Using artificial intelligence in a situation where human lives depend on success could prove catastrophic. Manufacturing is going through a revolution from a labor intensive, blue-collar industry to a white-collar, Silicon Valley industry. This use of AI to assist managers to efficiently run their operations is part of the transition from manufacturing being a labor intensive business to being highly automated at the operations management level as well as on the production floor. Artificial intelligence will eventually touch nearly every industry on the planet, but self-driving cars are among the most sought-after developments for this technology.

Will artificial intelligence (AI) take control of human race for Intelligent Manufacturing?

The answer to this question seems to be positive. Several experts of AI have similar comment as “everything that humans can do machines can do”. Stephen Hawking also warned us during an interview with BBC that “The development of full artificial intelligence could spell the end of the human race.” Ex Machina, a recent enthralling science-fiction film presents the possibility of a robot that has cognitive capability to think, feel and even manipulate human beings. Self-driving cars, Siri on your iPhone, weather forecasts, face recognition on your Facebook photos, etc are all examples. A Japanese company with Deep Knowledge found out an Artificial Intelligence (AI) as one of the directors due to its ability to predict market trend that is “not immediately obvious to humans” .Replacing human with robot in manufacturing is a trend that we can’t stop or avoid. Many experts fear that robots will cause a massive job loss which would result in severe social problems. But it’s not that simple, a country’s implementation of robots has no proven relationship to the percentage of manufacturing jobs lot. Rather they will restructure the labor market, accomplishing poorly-paying tasks and at the same time creating job opportunities which require high qualifications. As technology advances, the low cost, high-accuracy and efficiency of robot is going to benefit the human society as a whole on a broader level. The future holds a generated era in which artificial intelligence comes up with its own designs, its own ideas, its own products instead of all our tools being passive, us telling them what to do and them doing it. In real life, however, artificial intelligence is far more benign, and may provide the most critical catalyst for the future of manufacturing.