There have been several important manufacturing automation developments in numerous fields in the twentieth century: electrical systems, improvements to computer-writing technology and implementations, advancements in sensor engineering and the advent of a philosophy of quantitative regulation. Both of these advances have contributed to the focus on automation systems.
Electro-digital computing technologies (the ENIAC in 1946 and the Universal Automatic System I in 1951) provided much quicker than traditionally possible for far more complicated control functions in automation and related calculations. The system was established also in the area of modern electronic computing. The development of integrated circuits in the 1960s propelled the trend towards miniaturization of computer technology which resulted in machines that are far simpler and cheaper than their predecessors to calculate at far higher speeds. This trend is expressed today by the microprocessor, a small multi-circulated device capable of performing the logic and arithmetic functions of a massive digital computer.
Parallel improvements have been made in the system computing technology to include programming instructions, combined with application network developments. Electronic storage systems are created by magnetic tapes and discs, magnetic memory balls, laser scanning, video disking, and beam-addressable electron memory devices. In reality, computer programming standards (and other programmable machines) have been overhauled. Modern languages are simpler to use and render data processing and thinking more effective.
Advances in the production of sensors have resulted in a large range of measuring instruments which can be used as components of automatic feedback systems. These devices comprise highly sensitive electromechanical versions, laser beams, machine vision, and scanning field imagery. Others require machine technologies to integrate these sensor networks. Computer vision, for example, requires storing large amounts of data which can only be performed on new high-speed machines. For different manufacturing operations, this device is a modular sensor like part identification, regular checks, and robot control.
Finally, after the Second World War, an increasingly complex mathematical theory has grown on control schemes. The theory frequently involves traditional constructive feedback control, effective management, integrated technologies, and artificial intelligence. Traditional feedback control theory utilizes linear ordinary differential equations to test problems, such as the flying-ball ruler of Watt. Although most mechanisms are more complex than flying ball controllers, there is still observance of the laws of physics described by differing equations.
Optimized control theory and adaptive control theory address the problem of defining and then retaining an appropriate performance index throughout the interest cycle throughout order to optimize its production. The difference between optimal and adaptive controls is that they must be implemented under constantly changing and unpredictable conditions; thus, if the control technique is to be introduced, environmental sensor measurements are required.
Artificial intelligence is an innovative field in computer science where the software is programmed to display features indicative in human intelligence. That involves listening ability, language comprehension, interpretation, problem solving, professional evaluation and related intellectual skills. Artificial intelligence technologies are meant to allow robots and other “smart” devices to communicate with humans and obey commands of a very high level, rather than detailed gradual programming statements that are typically required by programmable machines nowadays. For example, an artificially smart future robot will be able to understand and execute the “assemble the package” command. “Currently, industrial robotics require a detailed set of instructions showing the position and order of the product components, and so on.