Therefore, the products existing in nature should be far more sophisticated than ours. Investigating any species of plant or animal whether it be a microscopic or a giant creature, we reach the following conclusions: They have been specially designed to adopt their environment. They own the precise and perfect skills, organs and defence mechanisms, needed for their survival. They are so perfect that none of our engineering skills are enough to produce anything even approaching the quality of these organisms. We need also to mention that products in nature are alive, a concept which has not yet been precisely understood or described despite all our advances in technology and science. Examples supporting the above argument are innumerable, covering all branches of science. I will present only a few for illustration purposes. Fish flow in a medium of liquid. They are exposed to two components of pressure while swimming, the static pressure and the dynamic pressure. The static pressure is directly related to the weight of the water above them and does not vary while swimming at constant depths. However, this is not the case with dynamic pressure. It increases or decreases depending on the velocity of the liquid flow around the body. Researchers have found that the eyes of fish are precisely located on the body so that the dynamic pressure is always zero. This means that vision is not distorted while the fish are swimming at varying speeds. The heart of the fish is located at a point where the dynamic pressure is most negative. This enables the functioning of the heart to be much easier at high swimming speeds. The mouth is placed at the very front of the body where the total pressure is highest. This high pressure makes it easier to take water for oxygen during fast swimming. Consider another example, the octopus, which is one of the more primary creatures in the so-called evolution process.

For thousands of centuries, the octopus has been using the conservation of momentum principle. The octopus takes in water and propels it through a narrow pipe in a direction opposite to its line of movement. This jet propulsion principle has been effectively used in man-made motors only in this century. It should be evident then that these sophisticated designs and techniques cannot be generated by those animals themselves, still less randomly produced by the trial and error of blind (unguided) natural forces. The physical laws and the perfectly adapted designs must originate from the same source, the Supreme ‘Engineer’. This explanation is the most rational and logical. Other explanations, which attempt to attribute design and engineering skills to plants and animals or to blind and deaf nature, make no sense at all. Another example is the development of the embryo. From the manufacturing point of view, this development can only be explained by the term miracle. In engineering practice, the size of each part in a product is predetermined and manufactured separately. Those parts are then assembled together to form the final product. Let us call this type of manufacturing static manufacturing, since the sizes of the parts remain the same during assembling. In the case of an embryo, the sizes of organs are changing with time while a continuous assembling takes place under those conditions. New organs are created inside, without any interference from outside, developing in size over time, yet holding the assembly in a perfect condition at each interval of time. This process is an example of dynamic manufacturing which is, to put it bluntly, quite impossible for us to achieve. In usual manufacturing, the size of a part is smaller than the raw bulk of material from which it is produced, and some of the material is wasted. In some cases, moulds are used to achieve the desired shapes. In the creation of an embryo, however, there are no moulds at all, no spare parts thrown away, no wastage.

These manufacturing techniques are by far beyond the limits of humanity. Note that we have not yet mentioned the events that take place at the micro level inside the cells. Even a general glance at the global events shows us how extraordinary the development of an embryo is. A final example will be given from the mechanics of materials. For birds to be able to fly, they must balance minimum weight with maximum strength. Their bones can be considered as hollow pipes. Calculations reveal that the ratio of the inner radius of the bones to the outer radius is selected in the optimum way precisely so that, with minimum weight, maximum strength is achieved. We have not mentioned the macro creation process (cosmos, galaxies, solar systems etc.) since these topics are more related to pure sciences such as physics, astronomy, chemistry, biology etc. An understanding of creation, even then not comprehensive, requires knowledge of these pure sciences together with knowledge of engineering and design. The Creator of the earth and cosmos describes Himself as ‘the Best of Creators’ (Mu’minun, 23.14). The reader may consider what we have said here as a tiny effort towards understanding this verse. 

The word ‘robot’ was first used in the 1922 play R.U.R. by the Czech playwright Karel Capek: the title is an acronym for Rossum’s Universal Robots which become so sophisticated that they take over the world. ‘Robot’ is compounded from the Czech words ‘robota’ or work, and ‘robotnik’ or serf (Capek. 1923).

The use of industrial robots, first clearly identified in the 1960s, along with computer aided design (CAD) and computed aided manufacturing (CAM) systems, characterizes the latest trends in the automation of the manufacturing process (Roth, 1983). These technologies arc leading industrial automation through another transition, the scope of which is still unknown.

Growth of the robotics market has slowed compared to the early 1980s. The use of industrial robots is at present concentrated in rather simple, repetitive tasks which do not to require high precision. However, manufacturing market analysis predicts that early next century industrial robots will become increasingly viable in applications which require more precision and sensory sophistication such as assembly tasks. The automotive industry, where robots have been economically justified since the 1970s, will continue to be the leading user. However, the major growth of the US robot population will occur in non-automotive industries.

2. Robot classes and characteristics

Robots can be classified in many ways. To establish a generic classification system, we shall refer to dimensions or degrees of freedom or DOF.

The DOF of a mechanical system refers to the number of physical axes through which motion can occur. In robotics, DOF can often be equated with the number of joints in the robot.

Typical present-day industrial robots have from one to six-DOF, although more are certainly possible. For example, a wrist can be made more flexible by adding rotation to the twisting already in that joint. Similarly, a fourth DOF can be added to the shoulder, where the arm joins the base to allow additional rotation of the arm. Industrial robots are also classified by the mechanical configuration of the individual elements of the arm and actuators. Theses classifications are: rectangular class (X,Y,Z): cylindrical class (R,?,Z): spherical class (R,?,?); and jointed class (?1,?1,?). This classification begins with simple movements in a rectangular co-ordinate system such as the x-y co-ordinate system.

3. World’s robot population

More than 610.000 industrial robots are now at work according to a new annual publication by the secretariat of the United Nations Economic Commission for Europe (UN/ECE) and the International Federation of Robotics (IFR).

The world’s robot population grew by about 6% in 1993 compared with 8% the year before. These growth rates fall significantly short of those of 16-23% recorded in the booming late 1980s and early 1990s. However, in view of the deep recession which commenced at the end of 1990 in robot-using countries and resulted in large reductions in investment and industrial employment, growth in the robot stock of 6%-8% is still quite impressive. 

Japan accounts for more than half of the world robot stock. However, the net increase in Japanese robot stock fell sharply in both 1992 and 1993. In 1993, the net increase in the robot stock was only about a third of the record year 1990, underscoring the depth of the Japanese recession.

With 325 robots for every 10.000 persons employed in manufacturing, Japan has by far the world’s highest robot density followed by Singapore with 109, Sweden with 73, Italy with 70 and Germany with 62. As a result of falling employment in the manufacturing industry in 1992-1993, robot density increased rapidly in many countries even though the robot stock increased only modestly.

In most countries, welding is the predominant application area for robots, particularly for major motor vehicle producing countries, accounting for more than 20% of the total robot stock. In a few countries machining was the largest application area. Assembly was the largest application area in Japan, accounting for 40% of the total stock of robots. It is worth noting that in Japan assembly accounted for 50% of the net increase in stock while welding only had a share of 9%.After a solid recovery in 1994, the robot market is forecast to boom in the period up to 1998. Based on macroeconomics forecast of the development of world economics the UN/ECE and IFR forecast that the world stock of industrial robots will increase from some 610,000 units at the end of 1993 to over 830.000 units at the end of 1997. As the number of personnel employed in industry is falling, the density of robots measured as the number of robots per 10.000 workers will continue to surge. In terms of units, shipments are estimated to increase from about 54.000 units in 1993 to over 103,000 units in 1997.

While the robot market was expected to be somewhat hesitant in Japan in 1994 and 1995, it was expected to boom in the United States, Western Europe and the dynamic Asian economies. If growth and world trade gain momentum as predicted from 1995, the prospects for the robotics business seem extremely bright.

The potential for expansion of robotics is enormous. If other industrialized countries were to approach the robot densities of Japan and if industry in general were to reach only half the robot density of the motor vehicle sector, the robot stock would increase manifold, and this is not counting the potential for robots in the service industries. The following example gives an illustration of the potential: if industry in France and the United Kingdom were to achieve a robot density half that of the motor vehicle industry in those countries, the robot stock would more than double; if it reached half the density of the Japanese motor vehicle industry, the robot stock in those countries would increase more than 20 times.

4. Summary

The emphasis in this article has been on industrial robots and techniques currently used in that environment. The future of robotics depends on improvements in many technologies to reduce cost and increase the range of performance so that robots become effective in more environments. These technologies include motors, actuators, contact sensors, non contact sensors, mechanisms, lubrication, electronics, computers and artificial intelligence.

References 

• CAPEK. K. (1923) R.U.R.. Samuel French. London.
• ROTH. B. (1983) Principles of Automation, in Future Directions in Manufacturing Technology, based on the Unilever Research and Engineering Division Symposium held at Port Sunlight, April 1983. Unilever Research. UK

CAD/ CAM IN AUTOMATION

The developments of manufacturing can be categorized in two stages, namely the mechanization stage and the automation stage. In the early stage of manufacturing, tools and processes were mechanized. All of the various manufacturing processes were divided into categories such as casting, forging, turning, milling, drilling and cutting, with workers specialized in one of these areas. Specialization resulted in the separation of design from manufacturing. One person would design a product while other specialists would manufacture it. Design and manufacturing communicated through drawings. The mechanization stage was able to accomplish mass turnover and speed in production. However, it lacked flexibility and integration.

The next stage in the development of manufacturing is automation. In 1975, mass production was automated through the use of transfer lines. In 1976, batch production was automated through Flexible Manufacturing Systems (FMS). In 1979, design and draughting through CAD (Computer Aided Design) started to be used widely. The integration of these started in 1985. The goal of this stage is to have completely automated manufacturing plant operating with only a minimum of human involvement. Progress is being made in this regard, but total integration has not yet been achieved. The totally automated factory will be capable of mass turnover and speed in production, will be flexible and completely integrated.

The most important development regarding automation in manufacturing has been the computer. It provided developments in manufacturing control, material handling, planning and in other activities. The use of computers in manufacturing control improved NC (Numerical Control) technology such as computer aided NC code generation. It is now possible with some CAD/CAM (Computer Aided Design/Computer Aided Manufacture) systems to generate NC tape directly from the designed part stored within the CAD data base. Computers have greatly enhanced automated manufacturing. NC machine tools have been replaced by CNC (Computer Numerical Control) machine tools: almost every machining process can now be efficiently automated with a significant degree of accuracy, reliability and repeatability.

Computer Aided Design (CAD) can be defined as the use of computers to assist in the design process including calculation, analysis, modelling, draughting and testing. Initially, CAD systems were primarily used for draughting. Nowadays it also includes Finite Element Modelling (FEM), geometric modelling and kinematic analysis. (FEM is widely used for the analysis of many engineering problems, namely static, dynamic and thermal stress analysis of various structures including vibration analysis. Geometric modelling is concerned with the mathematical representation of objects in a computer.)

The development of NC machine tools was the beginning of CAM systems. CAM can be defined as the use of computers to enhance or assist in any manufacturing process. CAM comprises a large number of functions such as Computer Numerical Control (CNC), Direct Numerical Control (DNC), Flexible Manufacturing System (FMS), Automated Guided Vehicle (AGV), automated material handling, inspection and computer controlled assembly systems. CNC is an NC system that uses a dedicated computer to perform NC functions. DNC can be defined as a manufacturing system where a number of machine tools are controlled by a central computer simultaneously. The part programme is transmitted to the machine tool directly from the computer. An FMS is a programmable manufacturing system capable of producing a variety of products automatically and it is composed of CNC machine tools, automated material handling systems, robots and a computer system to control them. An Automated Guided Vehicle (AGV) is a robot-like vehicle that is used to carry objects from one place to another and can be programmed to trace a path.

INTEGRATION OF CAD/CAM

Due to development in computer technology, numerically controlled equipment, robots and computer controlled automation in CAD and CAM systems, many manual skills have been automated resulting in reduction of lead times, improvements in production, increase in manufacturing accuracy and flexibility. However, the full integration of CAD and CAM systems in industry has not yet been achieved and they have been developed separately (see Davies et al., 1988; Irani et al., 1990; Joseph and Davies 1990).

It is acknowledged that significant benefits can be obtained when CAD is integrated with CAM within a single company. For this reason the integration of CAD and CAM systems has become an important goal in factory automation (see Semakula and Gill, 1989; Sing et al., 1990). Computer Integrated Manufacturing (CIM) is the term used to denote the complete integration of all aspects of CAD and CAM systems.

CAD and CAM systems have not been totally integrated due to the difficulties in automating intermediate functions (see Joseph and Davies, 1990; Joseph et al., 1990). In order to achieve the goal of full integration of CAD and CAM two major obstacles should be addressed, namely complete CAD and CAPP/CAM interface and a fully automated, flexible CAPP system. (CAPP stands for Computer Aided Process Planning.)

CAPP is an important activity which bridges CAD to CAM (Figure 1) and translates the design information into manufacturing instructions to produce mechanical components (see, most recently Desai and Pande, 1991; Cho et al., 1991). The task of process planning in industry is usually performed by an experienced process planner manually employing his or her expertise and knowledge about machining operations. The quality of the plan developed depends on the experience and preferences of the planner whose highly skilled expertise is difficult to replace (Bandyopathyoy et al., 1981; Joseph and Davies, 1991).

Several CAPP systems have been developed. However, the computer can only assist the planner generate process plans. Fully automated CAPP does not exist and its benefits in the real industrial environment are still to be seen (see, e.g. Chang. 1990; Domazet and Manic, 1990). The complexity of decision making in process planning is a barrier to automating process planning. Many of the tasks carried out by the planner require expert knowledge, experience and intelligent reasoning (see Rustom and Mileham, 1989; Stewart et al., 1989). Other major impediments to the implementation of fully automated CAPP are related to the capturing of planning logic and heuristic knowledge. Industrial robots are programmed by a human programmer. But how does a bee know how to built a honeycomb and make honey?

Algorithmic programming techniques are considered unsuitable to automate process planning because process planning problems are usually solved heuristically, that is, on the basis of human ability to use reason and learn from experience (see Tonshotf et al., 1987; Dumazet, 1992). Manufacturing processes change over time on the factory floor. Algorithmic programs are not flexible enough to accommodate modifications since any alteration in the programme affects the whole structure of the software (Changer et al., 1991). Artificial Intelligence (AI) is a promising approach to automate process planning. Expert Systems or Intelligent Knowledge Based Systems are able to automating the reasoning activities to capture logic, experience-based reasoning and knowledge in a computer environment. An Expert System represents and stores the domain-specific knowledge in a special manner so that it is possible to add, delete or modify the knowledge within the database without any alteration in the program.

In short, the main goal for the industry of tomorrow is to integrate all the activities on the factory floor, i.e. to have automation from design to final manufacturing, (Nordland, 1988).

Assuming that we had a chance to visit such a factory of the future totally integrated, automated, unmanned except by robots, we would be aware that the automation is achieved and controlled by a computer programme which processes data, solves the problems that arise and gives the commands necessary to run the factory.

It is obvious that every such programme requires a programmer. Nobody would claim that the machinery, robots. etc. have themselves decided to develop the complicated software to control the system on the factory floor: it is easy to see that machines and mechanical parts do not have the ability, intelligence and knowledge even to wonder at their own structure.

Even if we do not see the programmer we can infer that one exists who is expert in the particular field and who programmed the automatic systems to do particular things. Similarly, we can liken the earth to an automated factory where animals and plants are like robots or automatic systems that perform some intelligent actions. If, within this factory a bee, for example, is not attributed to a Creator Who tells it how to make its honeycomb and honey then it must be that bees themselves know the necessary chemistry and geometry to do so. But we know that a bee is so unintelligent that when it is trapped indoors it tries to get out through a closed window. Even where there is an open window nearby it does not think of using the open window, but only finds it randomly. Therefore, we may not suppose that bees are intelligent and skilled enough to make honeycombs and honey. Even we, humans, who are the most intelligent creatures on earth, are not able to make proper hexagon-shaped honeycombs without using tools or a die. So we cannot expect a bee to do so all by itself without using a tool.

Every fruit tree is a fruit factory. A vegetable plant is a vegetable factory. They produce fruits and vegetables, respectively. If they are not attributed to a Creator then it must be that they are creating fruits and vegetables by themselves. We know that trees and vegetables are not intelligent enough and lack the knowledge of biology or chemistry to combine the necessary minerals or molecules to create the fruits and vegetables that fulfil our needs. They are not even aware of what we need. Examples can be extended to other creatures in the earth. Vegetables and animals perform some intelligent actions and yet they are not intelligent. Although we do not see the Creator of this factory, the activities around us show that there is One, Who is All-Wise, creates and controls the actions within this factory-like earth. 

REFERENCES

• ClANG, T.C. (1990) ‘Expert Process Planning for Manufacturing’, Addison-Wesley Publishing Company, USA.
• CHANG, T.C.,Wysk, R.A. and Wang, H.P. (1991) Computer Aided Manufacturing, Prentice Hall, USA.
• CHO, K.K., Lee, S.H. and Ahn, J.H., (1991) ‘Development of Integrated Process Planning and Monitoring System for Turning Operation’, Annals of the CIRP, 40/1, pp.423-7.
• DESAI. VS. and Pande, S.S., (1991) ‘GFM. An Interactive Feature Modeller for CAPP or Rotational Components’, Computer Aided Engineering Journal, pp. 217-21.
• IRANI, R,K., Saxena, M. and Finnigan, P.M., (1990) ‘Boundary Based Feature Modelling Utility’, Proceedings of the ASME International Computers in Engineering Conference, 1, pp. 45-51, Boston.
• JOSEPH, A.T. and Davies, B.J., (1990) ‘Knowledge Based Process Planning System for Turned Components’, The International Journal of Advanced Manufacturing Technology, 5, pp.52-65.
• JOSEPH, A.T. and Davies, B.J., (1991) ‘Elictation of Process Planning Knowledge in a Manufacturing Environment’. The International Journal of Advanced Manufacturing Technology. 6, pp.16-34.
• NORDLAND, G.L., (1988) ‘Integrating CAPP Into Factory Management Systems’, CAPP From Design to Production, ed. Joseph Tulkoff, SME, pp. 134-136.
• RUSTOM, E.A. and Mileham, A.R., (1989) ‘The Development of a Generative Computer Aided Process Planning System for Prismatic Parts’, Advances in Manufacturing Technology 4. Proceedings of the 5th National Conference on Production Research, Huddersfield Polytechnic, pp. 259-63.
• SINGH, R., Sittas, E., Mullineux. G. and Medland, A.J., (1990) ‘Intelligent Communications Between CAD and Manufacturing Activities’, Proceedings of the 28th International MATADOR Conference, pp. 305-1 2.
• STEWART, C.D., Wallace, W. and Boswell. C., (1989) ‘The Development of a Knowledge-Based Process Planning System’, Advances in Manufacturing Technology 4, Proceedings of the 5th National Conference on Production Research, Huddersfield Polytechnic, pp. 265-68.