We are faced with many challenges in life, and we are able to find solutions to most of these challenges with our minds. We drive home from work and we try to find the best route considering the traffic, the cost and time required to make it to our destination. We consider cost, fuel consumption, maintenance, and after-sale service and then select the best alternative when buying a car. An airline company assigns pilots to the flights considering work hour constraints and aircraft maintenance schedule. A company has limited budget and many project alternatives. Each project has a cost and revenue and the company tries to find the best projects that will maximize the revenue. In each case, the optimum solution is desired. The problems can be modeled as a mathematical problem in which an objective function, decision variable and constraints exist. The objective is the maximum revenue, the decision variables are the projects and the constraint is the limited budget for the company. However, most of these theoretical problems are more complex in practice and need special solution search methods.

Once a problem is formulated, the next step is to use a solution method to find an optimum solution. Finding an optimum solution is similar to finding a treasure on a mountain or in the ground with limited time. If there is no sign of the best location or no guidance to the right direction, this search is pointless and a waste of time. In other words what this means is that we merely hope to find the best solution and continue on digging. This is obviously not an efficient method. Sometimes helpful information is provided for the location of the treasure and we directly climb the mountain to reach it. Most cases are between these two strategies. One can look at some places randomly and look for some clues of treasure, then move to another location and continue until he gives up or finds the treasure. Another useful approach might be looking for the treasure with a group of people. Each group member chooses a random path and shares information about their experiences until they reach the treasure [1]. These methods are trial-and-error based search methods.

People use their intelligence and instincts when they try to find a solution for a problem. We tend to choose the directions that will take us minimum time when driving home. Kids learn using trial and error methods and we tend to solve problems in daily life using trial-and-error approaches. The solutions that are based on trial-and-error are called heuristics. Heuristics methods can find a good solution to a complex problem in a reasonable amount of time but there is no guarantee that the solution is best. These methods are preferred when the best solution is not required and a good solution is desired in a short time. Computer programs and simulations are developed to solve large-scale complex problems using heuristics.

The best designs, solutions, inspirations, and engineering mechanisms that are beyond the reach of current technology are abundantly found in nature. Scientists visualize and simulate the behavior of animals in nature and apply their strategies to problem solving. These strategies provide successful heuristics methods.

Ants teach us how to find the right path

The behaviors of ants provide a good solution method to solve complex problems on graphs. An ant leaves its nest and randomly chooses a path to look for food. If it finds the location of food, it returns to the colony and leaves traces of a chemical on the return path called “pheromone.” When another ant finds this chemical, the ant follows the same path to reach the food. More ants that follow this path leave the same chemical markings, making the path more attractive to follow. If the path is long and unattractive, the chemical evaporates and loses its attractiveness as it is not used frequently. If a path is used more frequently, it will be more attractive for an ant as each ant leaves traces of chemical one on top of the other [2]. The most attractive path is the path that is shortest or optimal since it is preferred by many ants that make the chemicals denser and keep it fresh all the time.

It is required to find the shortest path to an optimal solution of a complex problem. A graph is used to analyze all the possible paths and hence find out the shortest path. The traveling salesman problem is a famous example of such problem. There are many possible combinations of the optimal solution. The required computational time becomes exponential as the size of the problem gets bigger. On the other hand, ant colony optimization can provide good solutions in a very short time.

The search algorithm works in such a way that each ant randomly selects a path in the search space and the length of the paths (value of the objective function) are recorded. The solution developer arbitrarily assigns ants to the search space and allows them to select a path. This search process is replicated many times and as one path is selected more frequently, then it means this path is shorter and better than other paths and hence can be preferred as a good solution. This solution is recorded as the best solution to a large scale problem.

Social behaviors of bird and fish swarms

Modeling the social behaviors of animals in their natural habitats provides us a way to reach a good solution. Bees, termites, ants, birds, fish and wasps are good examples of animals that have a social and collaborative life style. These animals work together in a social environment and interact with each other to continue their life and supply their basic need of food. Food is distributed often times far away from where these animals are located and they have to find a trail to reach their food without knowing the exact location. The physical paths and mental strategies used by each animal to find a solution to their problems—which is in a way like developing a natural algorithm to find the right way to the food—is noteworthy enough to do extra research.

Algorithm designers have modeled the strategies of these species and found out that it can be used to find an optimal solution to a problem [3]. If a group of birds is considered, the information that each bird in the flock is equal to the solution has been obtained so far. The best known food location signals the best solution. This information is distributed in the flock and other birds move to this location to focus on the area. The move from one place to another by each bird represents the development of a solution in the optimization problem. The most important issue is the collaboration of each individual in the flock, whether it is a bird or a fish.

In a flock, each bird acts as an agent to achieve a global objective while co-operating with other members without any conflict. Each member in the flock is governed by local rules and interactions among which agents lead the flock to its global objective. As mentioned, this objective might be to find food, foraging or constructing shelter. The behaviors of these agents illustrates that a self-organized social community exists without any education and experience as there is no central control in this system. A member of the flock does move according to directives from an authority or according to a plan. Each member coordinates its movement according to movements of its neighbors. Each bird stays close enough to the flock so it does not lose it. It also avoids collisions if the members of the flock move very close to each other. This is called “separation.” Each agent follows the average heading of its group which is called “alignment.” Since there is no leader or bird which makes the commands, the movement of the entire flock is the result of collaboration. Each member is free to fly in any location point of the flock. As there are more eyes that are collaboratively looking for food, one bird uses the eyes of all other birds. It is interesting to note that a school of fish show a similar organized behavior. An observer might conclude that the motion of these groups is pre-planned, but in reality they are not.

The motion of a school of fishes and flocks of birds are animated in computers, and their movements are modeled using mathematical formulations. The flock starts its motion from a current location X0. Let’s say that f(x) is the problem that needs to be maximized, i.e., we would like to find a solution X that will maximize f(x). The function corresponds to the best location that the food is abundant for the bird flock. The flock moves from X0 to X1 then X2 and ends with Xn which is the last point during the search. Each point represents a possible location for the food, the best is represented as:

This is the point where the food is abundant and the swarm concentrates to this area. When the swarm moves from Xn-1 to Xn based on the velocity vector given,

note that the new location is determined based on the inertia, personal experience and social influence. Then when they find food, every member benefits from the result.

Artificial bee colony is another method that is used in optimization problems. Each honey bee goes to look for food and returns to the hive with some nectar [4]. The bee then dances in the hive signaling the amount of nectar it found at the source. Other bees choose one source depend on the dances of returning bees and they also return to the hive with some nectar. The amount of nectars is evaluated and the abandoned food sources are replaced with sources that have less food. The amount of food corresponds to the objective of the optimization problem that will be maximized. The number of returning bees with nectar corresponds to the number of solutions to the problem. Since bees share information through dancing, the location that has the highest amount of food is determined as the optimum solution to the problem.

Bat algorithm, firefly algorithm, krill herd algorithm, and intelligent water drops are some other important methods that are inspired from nature. It is also worth mentioning that new methods are being tested and we can expect that they will be in use in the near future. These methods basically mimic the behaviors and strategies used in nature and apply these strategies in solving real life mathematical problems.

Nature as a source for humankind

Nature is the service of mankind as a source of food, a shelter, and a source of life with its oxygen and water. Humankind now realizes that nature contains solutions to their problems more than ever. Biomimetics, a science that has been developed recently, is the science that analyzes the systems, processes, models and materials of the creatures in nature and develops methods that help to solve real world problems. Bath University in the UK developed a database that contains the engineering systems of natural creatures and when a new machine, material or solution is developed, scientists benefit from this database. They estimate that only 10% of the potential in the nature is currently being used. The heuristic methods that have been analyzed in this article are only some of the available algorithms.

How can animals display such a level of knowledge to find methods and solutions? Do they develop them all by themselves? Are they able to share knowledge among themselves in a conscious way so as to reach optimum results, or are they born with these capabilities? Nature offers a banquet of amazing phenomena for us to ponder over with all sorts of models and systems to provide solutions for many human problems.

In other words, intelligent human beings benefit from the strategies of the species that are not intelligent. It means that there is another global intelligence behind what is beyond the reach of human capability.

References

Yang, Xin-She. 2010. Nature-inspired Metaheuristics Algorithms, Luniver Press, 2nd Edition, UK.

Dorigo, Marco, Thomas Stutzle. 2004. Ant Colony Optimization, MIT Press.

Reynolds, Craig. 1987. “Flocks, Herds, and Schools: A Distributed Behavioral Model”, Computer Graphics, Vol. 21(4), July.

Karaboga, D., B. Basturk. 2007. “A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm,” Journal of Global Optimization, Vol. 39 (3), pp. 459-171, November.

Even if the defenders of the theory admit it to be just a theory, it is still being presented to the masses as if it were a scientific fact. In fact, there is a great deal of evidence against the theory, and this accumulates, day by day. Recently, the claims of great genetic similarity between apes and human beings have been refuted. The article entitled, “Chimpanzee Chromosome Surprised,” published in Nature reveals that the genes of humans and chimpanzees are far more different than they were thought to be.

An interview was held with Dr Michael J. Behe, a leading American biochemist. Dr Behe, famous for his work criticizing the theory of evolution, has made important contributions to shedding light on the question of the true origin of life:

Dr Behe, could you tell us about your opinions concerning the scientific data given in the article published in Nature that proves the genes of humans and chimpanzees to be very different in reality?

A group of researchers from the University of Tokyo have compared all the letters on the alphabets of the 21st and 22nd chromosomes. The conclusion they arrived at is very remarkable, for it has turned out that there was a far greater difference between the two species, as opposed to what was formerly accepted. Darwin’s theory really gets into trouble here. As a matter of fact, the more we learn about biology, the more trouble Darwinism gets into. If we have a superficial knowledge of living beings, we think them to be simple and we can accept Darwinism, which tries to explain seemingly simple systems through small accidental changes. Within the last thirty years however, we have learned that life is incredibly complex, beyond our imagination. For instance, bacteria, seen by evolutionist taxonomy to be the simplest creatures, have minute but very complicated and perfect biochemical motors that enable them to move. The only way to explain how bacteria can have such a complex mechanism is to accept the existence of a supernatural creation.

Then what does the similarity between the different proteins, genes, and organs of different creatures signify?

Can they be considered as evidence supporting Darwinist claims?

No, similarities between different creatures first of all fail to answer the basic question of biology. That is, how did organs and systems so peculiar and complicated come to exist? Darwinism can give no answer to that.

On the other hand, there are surprising similarities even between the species that are thought to be very different from each other. Between humans and bacteria for instance. . . The question is: “Do these similarities constitute a picture compatible with Darwin’s theory?” In fact, they don’t, because the species which are supposed to be close relatives according to Darwinist claims sometimes turn up to be genetically different. Or some living beings that are supposed to be totally irrelevant to each other have very similar organs or genes. For example, the human eye and the eye of the octopus are almost the same. But of course this does not mean that we are relatives of octopuses. It is more logical to accept that these two eye structures do not come from a “common ancestor,” but from a design that emanates from “the knowledge of a single Creator.”

In my opinion, this concept of “design” is based on the theory of “intelligent design,” which you also support. Do you think this theory explains the similarities between living things better?

Yes, you can explain these similarities through design. We know that many designers or inventors use similar parts in different systems. For instance bolts, nuts, or cables are used in different devices. They are the best pieces to be used in the relevant mechanical systems. Of course, we cannot say that one device with a cable evolved from another. They were designed separately. The intelligent design theory is very consistent in its accounting for such similarities.

The intelligent design theory is sometimes severely criticized by defenders of Darwinism and they have tried to refute it. There is an inclination to present Darwinism as if it were an undeniable part of biology. What do you think is the reason behind this?

The reason is not scientific, but rather there is a philosophical and ideological aspect. Some scientists believe that we must explain the universe and life by natural factors alone. The basis for their belief is presumption that accepts the universe as a product of natural forces alone. But what if this is not so? Even when we see a pair of eyeglasses, we know that it is not a product of natural forces; we can infer that it was made by an intelligent and skilled optician. And life is thousands of times more complex than that. Therefore, we conclude that life must have been created as well. Here, the important point is evaluating scientific evidence without prejudice, as much as possible. Darwinists claim that science cannot accept a supernatural power. But until the mid-19th century, a great majority of scientists accepted the existence of a creative power, namely, God. The claim that science should be materialistic became widespread after Darwin. However, this claim increasingly conflicts with scientific evidence. Science should not try to give a materialistic explanation for life but rather to produce a correct explanation for life. Evidence should be analyzed, even if some people’s philosophical assumptions are disturbed.

Your book “Darwin’s Black Box” has been chosen as one of the most important 100 books of the 20th century by National Review magazine. In your opinion, what was it that made this book so important?

As a matter of fact, the reason for this was not the new and original information found in the book. I only showed the reader that in molecular levels of life there are very sensitive and complex systems and all these constitute evidence for a conscious planning and organization. When most people take a superficial look, when they consider plants, animals, birds, or fish, they can feel that there is some plan and program. But Darwin’s theory of evolution, which is taught in schools, tells us that this order and system in nature can be explained without a Creator. I think the greatest influence of my theory was to show that the Darwinist explanation was too superficial and misleading.

What do you think is the greatest challenge Darwinism faces?

The greatest problem for Darwinism is explaining how new biological structures, how new creatures, come into existence. Darwinism can shed light on how already existing biological structures may undergo small changes. For example, it can offer you an explanation about how the small differences in the beaks of finches in the Galapagos Islands appeared. But how did birds come to exist in the beginning? How did complex structures like the feathers or wings of a bird form? How did all the sensitive organs and systems like the brain, the eyes, the clotting of blood, all of which require several parts to work in perfect harmony, come to be? It is impossible for Darwinism to explain these, for each of these is a very complex structure that can function only when complete. The most consistent way to account for their origin is to accept the interference of a Conscious and Omnipotent Power, a supernatural Creator.

Do you have any expectations about the future of Darwinism? Do you think Darwinism will survive?

I believe that Darwinism is leaving the stage. It will be seen that explaining life through this theory is not possible and the theory will be abandoned. The process leading to this end has already begun. The reason for this is not what I and scientists like me are doing. The more we learn about life, the better we understand how complex it is. Scientists are beginning to realize that such complex structures cannot be attributed to purposeless and random mechanisms.

As we know, the supporters of Darwinism usually say that they think within scientific grounds, and those who oppose them base their ideas only on religious belief. The picture you are giving seems to be refuting this claim. Do you agree?

Yes, exactly. In the past people used to reject Darwinism on a religious basis. And the defenders of the theory so far have always claimed science to be on their side. But the surprising findings obtained since the last quarter of the 20th century have reversed the picture. Today, our rejection of Darwinism is not based on what we do not know; rather it is based on what we know. Now the followers of dogmatic thought are Darwinists themselves. We present them scientific evidence demonstrating that living beings are created in a planned and programmed fashion, whereas they reject this only due to their philosophical and ideological worldviews.

There are insistent narrow-minded defenders of old theories that occurred before scientific revolutions. But then science is victorious against false theories. I think this is what will happen to Darwinism soon.

Who is Michael Behe?

Dr Michael Behe, still teaching biochemistry at Lehigh University, shook the scientific world with his book Darwin’s Black Box: The Biochemical Challenge to Evolution in 1997. The National Review magazine defined his work as “one of the most influential books of the 20th century.” In his book, Dr Behe has put forward a new theory called “the intelligent design” in order to explain the origin of life. Today, there are hundreds of scientists, several institutions and scientific foundations that support the intelligent design theory. As a result of these organizations, the Darwinist claims in the school books in Georgia, Ohio, and New Mexico states have been taken out. The debates concerning this in other states are still going on. The organization Discovery Institute, which leads the intelligent design movement, is presided by Bruce Chapman, one of the consultants of Ronald Reagan.

Human and Chimpanzee Genes Have Proven to Be Very Different

The latest scientific research for comparing the genetic structures of humans and chimpanzees has revealed there to be a far greater difference between the two species than was thought to be. In the research carried out by a group of scientists presided by Dr Todd Taylor in Riken Genome Science Institute in Yokohama Japan, human and chimpanzee genes were compared one by one for the first time. The conclusion surprised the scientists, who had expected to find a great similarity. Dr Taylor et al. published the result of their research in their article in the famous science magazine, Nature. In the article entitled “Chimpanzee Chromosome Surprised” the first detailed comparisonhas revealed that human and chimpanzee genes are unexpectedly different.

Formerly, it was claimed that there was a 98% similarity between human and chimpanzee genes as a result of some limited comparison, and this proof was often repeated in support of evolutionist claims. Dr Fujiyama et al. for the first time made a detailed research on the subject. The scientists who meticulously compared the 22nd chromosome of chimps and the 21st chromosome of humans, which are claimed to be similar, found that 68,000 DNA units in total were different. The researchers have stated that in the 231 genes they studied, they determined a great deal of difference, approaching 83%, and that 23% percent of the genes they studied were completely irrelevant.

Sydney Morning Herald newspaper commented on this result, saying “chimps are not as close to us as they were thought to be.” Dr Jean Weissenbach, the leader of Genoscope, a genetic research institute in France, agreed and pointed to the fact that chimpanzees have thousands of genes that are different from us.

Two of the ways that people demonstrate their intelligence are communication and learning. Effective communication requires skills in the analysis of messages received and in the synthesis of messages transmitted. The ‘message’ may be a letter, an article, a poem, musical composition, painting, or indeed any other form of communication. In order to communicate effectively, one must be able to synthesize a message. To do that well needs the ability to make judgements about the level of sophistication of the recipient, careful use of the language of the communication and appropriate speed of presentation. Understanding a message also requires intelligence. A listener needs to know the meaning of most of the words being used and to have some knowledge of the context of the message.

The ability to learn and understand is considered by many as a vital component of intelligence and perhaps a definition of it.

‘Learning denotes the changes in a system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more effectively the next time.’ ‘When a computer system improves its performance at a given task over time, without re-programming, it can be said to have learned something.’

There are many definitions for Artificial Intelligence or AI. One is: ‘the study of mental faculties that encompasses computational techniques for performing tasks which apparently require intelligence when performed by humans’. The term Artificial Intelligence was first used by John McCarthy in 1956. Since then, and especially in the last two decades, there has been a growing amount of research on AI. With many scientists, engineers and programmers either studying AI techniques or building AI systems or both, national and international organizations dedicated to AI have been formed and are growing. In the USA, for example, there is now an American Association for Artificial Intelligence. A number of AI applications in the medical and engineering fields have been successful and so established AI as a promising area of study with specialist sub-sectors, namely robotics, machine vision, natural language processing, machine learning, expert systems and neural networks.

Looked at closely, each of these sub-sectors turns out to be an attempt to imitate some human organs or faculties. Robotics, for example, aims to imitate what a human being can do using limbs, hands and feet, in co-ordination with the eye and brain-which is what ‘vision systems’ aims to copy. ‘Natural language processing’ is the development of systems that perform tasks using the kind of language that humans use in routine interactions. ‘Machine learning’ aims to make computers learn how to use computational techniques. An ‘expert system’ is built to house and sift large amounts of human knowledge in order to give advice in particular circumstances just as a human expert would do. And ‘neural networks’, as the name suggests, try to reproduce in part the human nervous system.

Even though there have been many successful applications devel

oped in these areas, AI, compared to human intelligence, is still in its infancy. Everyday observation shows that the modest brains of lower animals can perform tasks that are far beyond the range of even the largest and fastest modern electronic computers. Just imagine that any mosquito can fly around at great speed in unknown territory without bumping into objects blocking its path. And a frog’s tongue can catch these insects in full flight within a split second.

At present the number of processors which can be built for specialized parallel hardware is somewhere between 50,000 and 1 million. Compared to the number of neurones in the human brain this number is extremely small. The total number of neurones in the human central nervous system can only be estimated; some estimates put the number about 1011 combined with the average number of synapses per neurones this yields a total of about 1015 synaptic connections in the human brain, the majority of which are developed within a few months after birth.

Comparative facts like those show that there is a lot still to be learnt from human beings, animals and other creatures. When somebody looks at an intelligent system-say a vision system or an intelligent robot, they will remark that it is amazing. Indeed it is amazing to see a system correctly inspect and classify many products in the space of a second. Such systems are already installed and in operation on production lines and their performance is quite good. There are several expert systems in use in real world tasks. The importance of these systems derives from their artificial intelligence which in turn derives from human intelligence. A man-made system can be very smart and artificially very intelligent but no such system so far has been awarded a prize for its innovative abilities. It is the human being who made it who wins the prize. What is prized, what is of higher worth, is not the system but its maker or builder. What about the fantastic system that is a human being? Just as an AI system is strong evidence of the existence of its maker, namely, a human being, so too human beings and other natural systems are more and stronger evidence for the existence of their Maker. To make an artificial intelligence one must first have natural or real intelligence. That means the Maker of human beings and all other creatures has a supernatural power over all of them. Just as no one could say that AI systems build themselves, it should be impossible to say the same for human beings and other creatures. This supernatural power is the All-Mighty Creator. As we have seen, it is not the system but its maker that is prized and respected. Self-evidently, the One who created human intelligence is to be prized and respected more than anything or anyone else. God says in the Qur’an: ‘We have indeed created man in the best of moulds’ (95.3). As we believe that there is definitely a maker of an AI system, why should it be hard to believe that there is a Maker of human being?

References

• CHARNIAK, E. and McDERMOTT, D., (1985) Introduction to Artificial Intelligence, Addison-Wesley Publishing Company.
• HANCOX, P.J., MILLS, W.J. and REID, B. (1990)
• Artificial Intelligence/Expert Systems, Ergosyst Associates, London.
• MULLER, B. and REINHARDT, J. (1990) Neural Networks. An Introduction, Springer Verlag.
• FORSYTH, R. and RADA, R. (1986) Machine Learning:
• Applications in Expert Systems and Information Retrieval, Ellis Horwoood, Chichester.

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

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