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Scientific and structural base of manufacturing
RoboUcs & Computer-Integrated Manufacturmg, Vol I, No. 3/4, pp. 389-396, 1984
0736-5845/84 $3 00 + 0 00 Copyright @ 1985 Pergamon Press Ltd.
Pnnted in the U S.A. All rights reserved
• Paper
SCIENTIFIC
AND STRUCTURAL GUNNAR
BASE OF MANUFACTURING SOHLENIUS
IVF/KTH, The Swedish Institute of Production Engineering Research and The Royal Institute of Technology, Stockholm, Sweden Science comes from Latin scientia meaning knowledge. Science is difficult to define, but we can obtain an understanding of what we mean. Knowledge, however, is wider in scope than what we generally define as science. Engineering, of which manufacturing is a part, requires creativity, fantasy, scope, and imagination as well as scientific knowledge. These elements are also needed in the scientific work itself. Scientific quality and language to tell the result must be considered separately from each other. Scientific quality means true and accurate knowledge, and the language must be adapted to the problem and to the receiver of the scientific result. Axiomatic decision-rules are being proposed as a scientific method to help in sorting out good solutions in engineering. The manufacturing system can be described and analysed as three main production systems: the manufacturing production system (MPS), the data production system (DPS), and the innovation production system (IPS).
Let us start by considering science. Is it possible to define what we mean by science? Perhaps not, for science is one of the m a j o r activities of our mind, in this sense resembling knowledge about nature, engineering, art, religion, and philosophy. None of these can be understood unless we consider them in relation to their past history. Science may perhaps be regarded as a mode in which we consider our world. We may understand what we mean by science but it is hard to define. As soon as we attempt to discuss science as a whole, a host of difficulties appear. The Latin word scientia meant nothing more definite than knowledge, but the modern usage covers only certain kinds of knowledge. The area of these, however, is now so vast that no man can have a grasp of more than a minute fraction of them. Moreover, even the kind of knowledge regarded as scientific is extremely diverse. It extends from sub-atomic reactions to mental processes, from mathematical laws of thermodynamics to the economics of race relations, from the birth and death of stars to the migration of birds, etc. Can these innumerable and endlessly diverse topics be brought under any one formula? Certainly not. However, scientific work has one important c o m m o n d e n o m i n a t o r and that is that it deals with d e v e l o p m e n t and description of knowledge about an existing reality. Scientific works in these very different activities and disciplines all involve systematic and unbiased
observations. Due examination of the records of these by trained minds leads to classifications. From such classifications general rules or laws arc deduced. These laws may be applied to further observations. Failures in correspondence between new observations and accepted laws may result in alterations of the laws, and these alterations lead to yet further observations and so on. This chain of activities is usually held to constitute the method of science. Let us now consider manufacturing. This is an engineering discipline. Engineering has been defined by the Engineer's Council for Professional D e v e l o p m e n t (U.S.) as the creative application of "scientific principles to design or develop structures, machines, apparatus or manufacturing processcs, or works utilising them singly or in combination; or to construct or operate the same with full cognisance of their design; or to forecast their behaviour under specific operating conditions; all as respects an intended function, economics of operation and safety to life and p r o p e r t y " . The term is sometimes m o r e loosely defined in G r e a t Britain as "the manufacture or assembly of engines, machine tools and machine parts, including instruments and associated measuring and control devices". It is also commonly used where the words "engineering science" would be m o r e appropriate; for instance, in describing a course of study undertaken by university students. Manufacturing has been defined by C I R P (1983) 389
390
Robotics & Computer-lntegrated Manufacturing • Volume l, Number 3/4, 1984
Performonce ~ I ~
~
~
~ ~ F,mshedproducts (mcludmg~(removol~,.~(fullyassembled,
Cost and capob~hbes -" Needs (product requ~rernents)
'--,=Creativtty (productconcepts) F~g i Computer integrated manufacturing system. Dr. Merchant was one of the first in the world to visuahze the potentml impact of the use of computers in developing advanced manufacturing systems. His philosophy of computer integrated manufacturing systems is being followed by people around the world.
as "a series of interrelated activities and operations involving the design, materials selection, planning, manufacturing production, quality assurance, management and marketing of the products of the manufacturing industries". In the 1960s Dr. Merchant defined the manufacturing system according to Fig. 1, and in 1983 CIRP defined the manufacturing system as "an organization in the manufacturing industry for the creation of manufacturing production. In the mechanical and electrical engineering industries, a manufacturing system in general has an integrated group of functions: viz. the sales, design, manufacturing production and shipping functions. A research function may provide a service to one or more of the other functions." Structural base, finally, is also somewhat difficult to define. Obviously it means here functional relations in time and space between objects and events. As science means knowledge, scientific work has to do with mental modelling of reality. Therefore it may be logical to observe the function of the human brain. This can be described in terms of the left and the right hemisphere and also the upper and lower level (Fig. 2). The mental activities that we traditionally connect to natural sciences are dominated by the upper left hemisphere. Those activities, however, that represent knowledge about scope, wholeness, and general goals take place in the upper right hemisphere. In engineering, which combines science and art, the activities of both brain hemispheres are, of course, fundamentally essential. We often talk about engineering based upon science. This obviously means that knowledge of the detailed, logical, sequential, mathematical type is to be combined with imagination, fantasy, and creativity. In engineering we create new solutions to problems and
new products. By doing this we also principally extend reality by creating new real things. In manufacturing research we therefore obviously are working with engineering based upon science and also scientific studies of manufacturing engineering (Fig. 3). So, to be good at manufacturing research we have to develop knowledge of both principal types. As science fundamentally means knowledge, one way of expressing our scientific problem in engineering research today is by the question: How can we also use the type of thinking of our right hemisphere as a method of scientific work? This has probably always been important but seems to grow in importance along with the introduction of computer technology and computer-integrated manufacturing systems. To be able to develop such systems that meet our goals, we have to focus more on scope and wholeness than before. This is true both in terms of goals for the development and in terms of technical
Log~cat Analytical Mathemotrcot Techn,eot ~ Probtem- / ~
Reserved ~ ~
Conse~,ot,ve X. \
Admm~strohve
UPPER /r~
\1/
Creative Combining Artrsbc RIGHT /
LOWER
Human Senslhve Mustcot Splrttuat Tatkmg
Fig, 2. Brain actwlties in the left and right hemispheres Individual persons have different profiles describing abilities m the different parts.
Scientific and structural base of manufacturing • G. SOHLEN1US
~ t
Object
Scientificstudy~ clence / Engineering, , . ~ ' ~
Object
Object
Object
Newdevetopment Fig. 3. Englneeering based on ~clence and ~oentific study of engineering.
solutions. Along with the technical development, we always have to go through the accurate process of observations, classifications, and formulating rules that make the knowledge accurate, basic, and accessible in the activities belonging to the left hemisphere. This is also an important requirement for the d e v e l o p m e n t of algorithms upon which the systems can be based. Suh has expressed this in a p a p e r published in 1982": " M a n y fields of endeavour which are not yet based on a solid scientific foundation share a few c o m m o n characteristics and problems. The apparent randomness and inscrutability of these fields brings about an admiration for those who excel, because excellence in these fields requires unique blends of creativity, intuition and experience. So are the fields of art, music, design and even manufacturing. We may be able to recognize excellence in these subjects, but we are unable to state why or how the particular combination of elements in a work causes it to be excellent. In fact, these fields lack an absolute frame of reference, which often leads to disagreements and differing opinions in evaluating the merits in these fields. The spatial and temporal aspects that contribute to successful works in these fields cannot be described based on scientific principles. Therefore intuition and experience play a key role when we compose music, design a product or a process or paint a landscape. Unfortunately, unless we can state explicitly what we know, it cannot be taught to and used by others. These are typical characteristics of a field that has not developed into science in which governing natural laws describe the underlying thought processes and reduce a seemingly complex array of facts and observations into a consistent set of statements and descriptions." We have many tools that we can use to define consistent sets of statements and descriptions, We have mathematical algorithms; various diagram techniques from simple block diagrams showing relations between objects and functions to logical
391
diagrams; flow charts describing functions of programmes; S A D T diagrams which can be described, in any detail, the relations between objects and events (Figs. 4a and 4b); and we have all possibilities that coordinate diagrams or histograms can give us, etc. Now I will mention a work by Professor Suh and his associates. They are working with a new approach to this problem, in which they propose an axiomatic approach. The axioms may be stated in terms of several equivalent statements. The most explicit set of statements for the axioms proposed is the following: • •
Axiom 1: Maintain the independence of functional requirements. Axiom 2: Minimize the information content.
Function requirements are hcrc defined as a minimum set of independent requirements on a product or a process. Functional requirements must be satisfied by the design within specific tolerances. This is a very important starting point using this approach. Information is defined as the minimum complete description of a product or process. This definition comes really from information and communication theory and is somewhat difficult to understand from a manufacturing point of view. In our terms, axiom 2 really means that the solution which gives the best probability of meeting the functional requirements within specified tolerances is the best one. As cost can often be regarded as a functional requirement, it also can meet the requirement to choose the solution with the least complexity (cost) together with all the other functional requirements. A simple, commonly known example may make this more concretely understandable. A shower arrangement in a b a t h r o o m can be designed simply with one tap to regulate the volume of hot water and one tap for cold water. If you want to get the right t e m p e r a t u r e within a few degrees you have to accept problems in obtaining a precise water-flow. This is caused by the functional coupling between temperature and volume. A better solution in this sense would be a thermostatic tap where you control the t e m p e r a t u r e with one knob and the volume with a tap. This, however, is more expensive, which means that if your requirement on volume is not so tight in tolerance the simple device would be the better one because it is cheaper and less complex. However, if your requirements on both t e m p e r a t u r e and volume have tight tolerances the thermostatic device is the best one in spite of the higher cost. The axiomatic approach aims at defining the best solution a m o n g a set of proposed solutions which meet the agreed set
392
Robotics & Computer-Integrated Manufacturing • Volume 1, Number 3/4, 1984
Def.
Requnrements,
(o)
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Out
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t
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Fig. 4. SADT diagrams. of functional requirements. The important thing is the control of the functional requirements and constraints, the degree of functional dependency (axiom 1), and the probability of meeting the requirements within constraints (axiom 2). The aim of scientific work is, of course, to obtain further development and maintenance of knowledge about our real world. Certainly we have to regard scientific work in many dimensions. Maybe the two most important requirements on scientific work are quality of the knowledge and the means and quality of the communications or language. The most important requirement on quality of the knowledge is, in my opinion, the level to which the knowledge lets us understand and by that understanding really grasp reality. The function of the language is to transmit the knowledge from the researcher to other people and between people, who can be other researchers, teachers, engineers, or lay-men. The language has to be adapted to the ability of the receiver. His ability, of course, depends on his education and experience. The quality of the language defines how well and accurately it transmits the knowledge to the receiver. As researchers and
teachers, I think we have to pay great attention to the language we are using in these two aspects: the ability of the receiver and the accuracy of the message. The field of research in manufacturing is today very broad, and we are communicating between different disciplines, such as natural sciences, engineering, and human sciences. So, even in the communication between researchers we have to strengthen our efforts to express the message in enough understandable languages. Let me now take a simple example further to show what I mean. Let us look at the Taylor connection between cut velocity and tool life. Figure 5 shows four ways to express the Taylor connection. If you give the expression according to 5(a) to a mathematically trained person, he can easily understand 5(b) and 5(c). To a mathematically untrained person you can tell the story according to 5(d): "Tool life is varying with cut velocity along a straight line", in diagram 5(c). Higher velocity results in shorter tool life. You can also add "Diagram for each material can be bought from research institutes and material suppliers". All these four expressions, (a)-(d), have the same scientific quality, transmit the same know-
393
Scientific and structural base of manufacturing • G. SOHLENIUS
to)
ledge. However, the language is very different and the ease of understanding is different for persons with different backgrounds. If you now reveal the true physical curve according to Fig. 6 and say that the Taylor equation is valid only between a and b and that the standard deviation that you can expect for a certain material is d, you increase the scientific quality of the message because you make a more precise description of the real connections. Of course if you also, as Kronenberg did, express the dependency from cutting depth and feed, you still increase the scientific quality. Of course you can now yourself extend this example greatly and I hope you understand what I mean. I think this distinction between scientific quality and language is very important to consider. Let us now consider the overall scope and structural base of the manufacturing system. Figure 7 gives an overall picture of the main functions in the manufacturing system. 7 If we analyse these functions we can sort them into three principally different production activities. Production is here used in its basic original interpretation. The Latin pro ducere means to bring or carry forward. Within the manufacturing system there are three main groups of production activities, each of which can be defined as a production system (Fig. 8). The first one is the manufacturing production system (MPS). The input to this system is material, components from subcontractors. The output is products that are inspected and ready for use. It is controlled by data (information). The manufacturing production system consists of and uses tools,
v Ta=C
(b) tog v =-a Log + tog C
tog
(c)
~=o ton a
tog
C
tog v
(d) Toot life ts vorymg with cut velocity along a strmght line in a log-tog diagram. A higher vel.oclty resutts ~n o shorter toot life (5c).
(A diagramfor obtamed
each material can be from research institutes
and moterlot suppliers.)
Fig. 5. Four ways to express the Taylor c o n n e c u o n between cut velocity and tool hfe.
Log T
',\\ tog
v
Fig. 6. Plot of log T vs. log v.
Strategy
11
Order routing, Planning, control
I
I
I
i I_ Des=cjn I
Product devetopment
II
PLanning I
Purchase
l
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I I
,q/ 1 Manufacturing production . . . .
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Fig. 7. The main functions m the manufacturing system.
_l
Marketing SaLe
394
Robotics & Computer-Integrated Manufacturing • Volume 1, Number 3/4, 1984 Product frequency
Order
.j
1 DATA PRODUCTION SYSTEM (DPS)
l
Data
I
People
1
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iJ
MANUFACTURING PRODUCTION SYSTEM (MPS)
Components
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Knowledge
n
INNOVATION PRODUCTION SYSTEM (IPS)
System components I
-I
l
l
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creative
systems, computers, axlomatlcs
educated
Products L
D
I
I L
People
Fig 8 The three production systems within the manufacturing system
machine tools, equipment for heat treatment, coating and painting, machines for assembly operations, machines and equipment for quality control, handling equipment such as industrial robots, automatic charts, conveyors and storing systems, computers and controllers, and finally people. The second production system is the data production system (DPS). The input to this system can be a direct order along with specified production functional requirements from a customer. However, it can also be an internal order in the company based upon prognosis for future delivery to customers via dealers, etc. The output of this system is data (information) about the product, about its parts and about the machinery, heat treatment, assembly, coating, painting, and inspection operations, which is necessary in order to be able to produce the products in the manufacturing production system. The data production system consists of and uses computers, work stations, software, and people. The work that is done is design, or rather choice of or specification of the best alternative within the range of products of the company, planning of thc work operations in the manufacturing production system including programming of machine tools, etc., and routing for the production in the manufacturing production system. Figure 4(b) is an example taken from a CAM-I proposal describing some activities that might be part of the data production system. However, there is obviously no universal detailed structure of the data production system. It is very much dependent
on the products, the market situation, and the people involved, etc. The third main production system is the innovation production system (IPS). The input to this system is knowledge and data about the market opportunities, about the technology of the products of the company, and about the available methods, techniques, software, and equipment. This includes what is already in use in the operation of the company, but also what is available from vendors, universities, and research institutes, etc. The output of the innovation production system is the next generation of products along with the next generation of manufacturing production systems and data production systems. The heart of the innovation production system is qualified creative people with expertise in the market and marketing, in product technology, and in production engineering, data production, as well as manufacturing production, with all that is included in terms of methods, equipment, programmes, and humanities (see also Ref. 3). Figure 9 shows a general lay-out of the total hardware system for all three main production systems in a computer-integrated automatic factory. This may be the type of manufacturing system that is now universally considered in manufacturing research and development. Let us now look upon the manufacturing system and distinguish between the development processes and the operative processes (Fig. 10). As you can see this is only a different picture with the same message as that of Fig. 8. The company is finding a
395
Scientific and structural base of manufacturing • G. SOHLEN1US
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Market anotys=s
Operatlwe process
Oes,gn
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]
vehlc~r~
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Fig. 9. General lay-out of the total hardware system for the three producnon systems.
l
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strategy to meet customer demands in a competitive manner by using its resources in an efficient way. In the development process the human resources must create product ideas and ideas for the development of the production system. Among several technically possible alternatives, it is necessary to find optimal or at least competitively efficient products and methods. Here it is especially interesting and important to consider the interaction between the ideas and the processes. The use of the axiomatic approach might be helpful here. This interrelation is shown in Fig. 11, where the innovative process is shown on the left and the operational process on the right. Here we can see that the market analysis gives functional requirements on products and product systems. The products are defining functional requirements on the production system and vice versa, and they are to be developed together under an innovative development process. The production system, here covering the DPS and the MPS, is put into operation in the operational process, where it processes material into products. The functional requirements on material are defined in the innovation production process (IPS). The material is brought into the production process and for each production case in terms of delivery the production process defines requirements on the product and vice versa. This is solved in terms of NC programs, process data selection, etc. Finally the product is delivered and used by the customer. The
Fig. 10. Development processes and operative processes within the manufacturing system
customer defines his functional requirements on the product along with his order. Suh et al. refer back to information theory. ~'~ In these terms the processes are information channels and the objects; market, product, production system, and product are physical embodiments or messages. However, for these who arc not familiar with information theory I think that this type of description does not contribute to a clearer understanding. The relations and axioms are quite understandable directly in manufacturing and production terms. 1 will not go deeper into the theories now but rather refer to the literature. CONCLUSION It is necessary within the research and development in manufacturing and especially manufacturing systems to expand gradually the understanding of the possibility of combining creativity and fantasy with precise accurate and reusable scientific descriptions. This can be formulated as follows: What methods can be used in the development of manufacturing systems on a scientific basis and what methods can be used in scientific studies of manufacturing engineering? This involves the problem of what methods can be used to combine the type of knowledge that belongs to the right brain hemisphere with that of the left brain hemisphere. Such things as
396
Robotics & Computer-Integrated Manufacturing • Volume 1, Number 3/4, 1984
n Inovahve ~
Operootinot
Physicat embodiments messages
Fig. 11. Interrelation between the ideas and the processes. FR c o m b i n a t i o n of m o r p h o l o g y with the axiomatic a p p r o a c h seem to be interesting. F u r t h e r m o r e , it is also worthwhile to develop and test the axiomatic a p p r o a c h practically on real cases where p r o d u c t systems are developed along with p r o d u c t i o n systems. REFERENCES
1. Encyclopaedia Britannica, Vol. 14. Chicago, London, Toronto, Geneva, Sydney. 1968. 2. Kaneshige: Quantitative analysis of the axiomatic engineering design. Master thesis, MIT, MA. 1981. 3. Kjcllberg, T.: Integrerat datorst6d f6r m~insklig probleml~sning och m~insklig kommunikation inom verkstadsteknisk produktion begr/insat till produktutveckling, produktionsberedning, konstruktion och tillverkningsberedning. En systemansats baserad ph produktmodeller. Thesis. KTH, Sweden. 1982. (In Swedish.) 4. Nakazawa, H., Suh, N.P.: Process planning based on information conccpt. MIT, MA. 1982. 5. Rinderle, J.R.: Measures of functional coupling in design. Laboratory for Manufacturing and Productivity, MIT, MA. 1982.
functional requirements.
6. Rinderle, J.R., Suh, N.P.: Measures of functional coupling in design. ASME 82-Prod-27. 7. Sohlenius, G.: Datorst6dd produktion f6r verkstadsindustrin--mhl och m6jligheter. Verkst/iderna. (9, 10, 11), 52-62, 36-45, 28-33. 1983. (In Swedish.) 8. Suh, N.P.: Basic science and the synthesis of ncw manufacturing processes. MIT. MA. 9. Suh, N.P., Bell, A.C., Gossard, D,C.: On an axiomatic approach to manufacturing systems. J. Engng Ind. 100: 127, 1978. 10. Suh, N.P., Bell, A.C., Wilson, D.R.: Exploratory study of constraints on design by functional requirements and manufacturing. Annual Progress Report to the National Science Foundation. Grant No. DAR77-13296. 1979-1980. II. Suh, N.P., Rinderle, J.R.: Qualitative and quantitative use of design and manufacturing axioms. Ann. ( ' I R e 31 (1), 1982. 12. Ticc, W.W.: The application of axiomatic design rules to an engine lathe case. MIT. MA. 13. Yasuhara, M.: Axiomatic cngineering design. Its concept and procedure. Master thesis, MIT, MA. 1980.
0736-5845/84 $3 00 + 0 00 Copyright @ 1985 Pergamon Press Ltd.
Pnnted in the U S.A. All rights reserved
• Paper
SCIENTIFIC
AND STRUCTURAL GUNNAR
BASE OF MANUFACTURING SOHLENIUS
IVF/KTH, The Swedish Institute of Production Engineering Research and The Royal Institute of Technology, Stockholm, Sweden Science comes from Latin scientia meaning knowledge. Science is difficult to define, but we can obtain an understanding of what we mean. Knowledge, however, is wider in scope than what we generally define as science. Engineering, of which manufacturing is a part, requires creativity, fantasy, scope, and imagination as well as scientific knowledge. These elements are also needed in the scientific work itself. Scientific quality and language to tell the result must be considered separately from each other. Scientific quality means true and accurate knowledge, and the language must be adapted to the problem and to the receiver of the scientific result. Axiomatic decision-rules are being proposed as a scientific method to help in sorting out good solutions in engineering. The manufacturing system can be described and analysed as three main production systems: the manufacturing production system (MPS), the data production system (DPS), and the innovation production system (IPS).
Let us start by considering science. Is it possible to define what we mean by science? Perhaps not, for science is one of the m a j o r activities of our mind, in this sense resembling knowledge about nature, engineering, art, religion, and philosophy. None of these can be understood unless we consider them in relation to their past history. Science may perhaps be regarded as a mode in which we consider our world. We may understand what we mean by science but it is hard to define. As soon as we attempt to discuss science as a whole, a host of difficulties appear. The Latin word scientia meant nothing more definite than knowledge, but the modern usage covers only certain kinds of knowledge. The area of these, however, is now so vast that no man can have a grasp of more than a minute fraction of them. Moreover, even the kind of knowledge regarded as scientific is extremely diverse. It extends from sub-atomic reactions to mental processes, from mathematical laws of thermodynamics to the economics of race relations, from the birth and death of stars to the migration of birds, etc. Can these innumerable and endlessly diverse topics be brought under any one formula? Certainly not. However, scientific work has one important c o m m o n d e n o m i n a t o r and that is that it deals with d e v e l o p m e n t and description of knowledge about an existing reality. Scientific works in these very different activities and disciplines all involve systematic and unbiased
observations. Due examination of the records of these by trained minds leads to classifications. From such classifications general rules or laws arc deduced. These laws may be applied to further observations. Failures in correspondence between new observations and accepted laws may result in alterations of the laws, and these alterations lead to yet further observations and so on. This chain of activities is usually held to constitute the method of science. Let us now consider manufacturing. This is an engineering discipline. Engineering has been defined by the Engineer's Council for Professional D e v e l o p m e n t (U.S.) as the creative application of "scientific principles to design or develop structures, machines, apparatus or manufacturing processcs, or works utilising them singly or in combination; or to construct or operate the same with full cognisance of their design; or to forecast their behaviour under specific operating conditions; all as respects an intended function, economics of operation and safety to life and p r o p e r t y " . The term is sometimes m o r e loosely defined in G r e a t Britain as "the manufacture or assembly of engines, machine tools and machine parts, including instruments and associated measuring and control devices". It is also commonly used where the words "engineering science" would be m o r e appropriate; for instance, in describing a course of study undertaken by university students. Manufacturing has been defined by C I R P (1983) 389
390
Robotics & Computer-lntegrated Manufacturing • Volume l, Number 3/4, 1984
Performonce ~ I ~
~
~
~ ~ F,mshedproducts (mcludmg~(removol~,.~(fullyassembled,
Cost and capob~hbes -" Needs (product requ~rernents)
'--,=Creativtty (productconcepts) F~g i Computer integrated manufacturing system. Dr. Merchant was one of the first in the world to visuahze the potentml impact of the use of computers in developing advanced manufacturing systems. His philosophy of computer integrated manufacturing systems is being followed by people around the world.
as "a series of interrelated activities and operations involving the design, materials selection, planning, manufacturing production, quality assurance, management and marketing of the products of the manufacturing industries". In the 1960s Dr. Merchant defined the manufacturing system according to Fig. 1, and in 1983 CIRP defined the manufacturing system as "an organization in the manufacturing industry for the creation of manufacturing production. In the mechanical and electrical engineering industries, a manufacturing system in general has an integrated group of functions: viz. the sales, design, manufacturing production and shipping functions. A research function may provide a service to one or more of the other functions." Structural base, finally, is also somewhat difficult to define. Obviously it means here functional relations in time and space between objects and events. As science means knowledge, scientific work has to do with mental modelling of reality. Therefore it may be logical to observe the function of the human brain. This can be described in terms of the left and the right hemisphere and also the upper and lower level (Fig. 2). The mental activities that we traditionally connect to natural sciences are dominated by the upper left hemisphere. Those activities, however, that represent knowledge about scope, wholeness, and general goals take place in the upper right hemisphere. In engineering, which combines science and art, the activities of both brain hemispheres are, of course, fundamentally essential. We often talk about engineering based upon science. This obviously means that knowledge of the detailed, logical, sequential, mathematical type is to be combined with imagination, fantasy, and creativity. In engineering we create new solutions to problems and
new products. By doing this we also principally extend reality by creating new real things. In manufacturing research we therefore obviously are working with engineering based upon science and also scientific studies of manufacturing engineering (Fig. 3). So, to be good at manufacturing research we have to develop knowledge of both principal types. As science fundamentally means knowledge, one way of expressing our scientific problem in engineering research today is by the question: How can we also use the type of thinking of our right hemisphere as a method of scientific work? This has probably always been important but seems to grow in importance along with the introduction of computer technology and computer-integrated manufacturing systems. To be able to develop such systems that meet our goals, we have to focus more on scope and wholeness than before. This is true both in terms of goals for the development and in terms of technical
Log~cat Analytical Mathemotrcot Techn,eot ~ Probtem- / ~
Reserved ~ ~
Conse~,ot,ve X. \
Admm~strohve
UPPER /r~
\1/
Creative Combining Artrsbc RIGHT /
LOWER
Human Senslhve Mustcot Splrttuat Tatkmg
Fig, 2. Brain actwlties in the left and right hemispheres Individual persons have different profiles describing abilities m the different parts.
Scientific and structural base of manufacturing • G. SOHLEN1US
~ t
Object
Scientificstudy~ clence / Engineering, , . ~ ' ~
Object
Object
Object
Newdevetopment Fig. 3. Englneeering based on ~clence and ~oentific study of engineering.
solutions. Along with the technical development, we always have to go through the accurate process of observations, classifications, and formulating rules that make the knowledge accurate, basic, and accessible in the activities belonging to the left hemisphere. This is also an important requirement for the d e v e l o p m e n t of algorithms upon which the systems can be based. Suh has expressed this in a p a p e r published in 1982": " M a n y fields of endeavour which are not yet based on a solid scientific foundation share a few c o m m o n characteristics and problems. The apparent randomness and inscrutability of these fields brings about an admiration for those who excel, because excellence in these fields requires unique blends of creativity, intuition and experience. So are the fields of art, music, design and even manufacturing. We may be able to recognize excellence in these subjects, but we are unable to state why or how the particular combination of elements in a work causes it to be excellent. In fact, these fields lack an absolute frame of reference, which often leads to disagreements and differing opinions in evaluating the merits in these fields. The spatial and temporal aspects that contribute to successful works in these fields cannot be described based on scientific principles. Therefore intuition and experience play a key role when we compose music, design a product or a process or paint a landscape. Unfortunately, unless we can state explicitly what we know, it cannot be taught to and used by others. These are typical characteristics of a field that has not developed into science in which governing natural laws describe the underlying thought processes and reduce a seemingly complex array of facts and observations into a consistent set of statements and descriptions." We have many tools that we can use to define consistent sets of statements and descriptions, We have mathematical algorithms; various diagram techniques from simple block diagrams showing relations between objects and functions to logical
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diagrams; flow charts describing functions of programmes; S A D T diagrams which can be described, in any detail, the relations between objects and events (Figs. 4a and 4b); and we have all possibilities that coordinate diagrams or histograms can give us, etc. Now I will mention a work by Professor Suh and his associates. They are working with a new approach to this problem, in which they propose an axiomatic approach. The axioms may be stated in terms of several equivalent statements. The most explicit set of statements for the axioms proposed is the following: • •
Axiom 1: Maintain the independence of functional requirements. Axiom 2: Minimize the information content.
Function requirements are hcrc defined as a minimum set of independent requirements on a product or a process. Functional requirements must be satisfied by the design within specific tolerances. This is a very important starting point using this approach. Information is defined as the minimum complete description of a product or process. This definition comes really from information and communication theory and is somewhat difficult to understand from a manufacturing point of view. In our terms, axiom 2 really means that the solution which gives the best probability of meeting the functional requirements within specified tolerances is the best one. As cost can often be regarded as a functional requirement, it also can meet the requirement to choose the solution with the least complexity (cost) together with all the other functional requirements. A simple, commonly known example may make this more concretely understandable. A shower arrangement in a b a t h r o o m can be designed simply with one tap to regulate the volume of hot water and one tap for cold water. If you want to get the right t e m p e r a t u r e within a few degrees you have to accept problems in obtaining a precise water-flow. This is caused by the functional coupling between temperature and volume. A better solution in this sense would be a thermostatic tap where you control the t e m p e r a t u r e with one knob and the volume with a tap. This, however, is more expensive, which means that if your requirement on volume is not so tight in tolerance the simple device would be the better one because it is cheaper and less complex. However, if your requirements on both t e m p e r a t u r e and volume have tight tolerances the thermostatic device is the best one in spite of the higher cost. The axiomatic approach aims at defining the best solution a m o n g a set of proposed solutions which meet the agreed set
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teachers, I think we have to pay great attention to the language we are using in these two aspects: the ability of the receiver and the accuracy of the message. The field of research in manufacturing is today very broad, and we are communicating between different disciplines, such as natural sciences, engineering, and human sciences. So, even in the communication between researchers we have to strengthen our efforts to express the message in enough understandable languages. Let me now take a simple example further to show what I mean. Let us look at the Taylor connection between cut velocity and tool life. Figure 5 shows four ways to express the Taylor connection. If you give the expression according to 5(a) to a mathematically trained person, he can easily understand 5(b) and 5(c). To a mathematically untrained person you can tell the story according to 5(d): "Tool life is varying with cut velocity along a straight line", in diagram 5(c). Higher velocity results in shorter tool life. You can also add "Diagram for each material can be bought from research institutes and material suppliers". All these four expressions, (a)-(d), have the same scientific quality, transmit the same know-
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ledge. However, the language is very different and the ease of understanding is different for persons with different backgrounds. If you now reveal the true physical curve according to Fig. 6 and say that the Taylor equation is valid only between a and b and that the standard deviation that you can expect for a certain material is d, you increase the scientific quality of the message because you make a more precise description of the real connections. Of course if you also, as Kronenberg did, express the dependency from cutting depth and feed, you still increase the scientific quality. Of course you can now yourself extend this example greatly and I hope you understand what I mean. I think this distinction between scientific quality and language is very important to consider. Let us now consider the overall scope and structural base of the manufacturing system. Figure 7 gives an overall picture of the main functions in the manufacturing system. 7 If we analyse these functions we can sort them into three principally different production activities. Production is here used in its basic original interpretation. The Latin pro ducere means to bring or carry forward. Within the manufacturing system there are three main groups of production activities, each of which can be defined as a production system (Fig. 8). The first one is the manufacturing production system (MPS). The input to this system is material, components from subcontractors. The output is products that are inspected and ready for use. It is controlled by data (information). The manufacturing production system consists of and uses tools,
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Robotics & Computer-Integrated Manufacturing • Volume 1, Number 3/4, 1984 Product frequency
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machine tools, equipment for heat treatment, coating and painting, machines for assembly operations, machines and equipment for quality control, handling equipment such as industrial robots, automatic charts, conveyors and storing systems, computers and controllers, and finally people. The second production system is the data production system (DPS). The input to this system can be a direct order along with specified production functional requirements from a customer. However, it can also be an internal order in the company based upon prognosis for future delivery to customers via dealers, etc. The output of this system is data (information) about the product, about its parts and about the machinery, heat treatment, assembly, coating, painting, and inspection operations, which is necessary in order to be able to produce the products in the manufacturing production system. The data production system consists of and uses computers, work stations, software, and people. The work that is done is design, or rather choice of or specification of the best alternative within the range of products of the company, planning of thc work operations in the manufacturing production system including programming of machine tools, etc., and routing for the production in the manufacturing production system. Figure 4(b) is an example taken from a CAM-I proposal describing some activities that might be part of the data production system. However, there is obviously no universal detailed structure of the data production system. It is very much dependent
on the products, the market situation, and the people involved, etc. The third main production system is the innovation production system (IPS). The input to this system is knowledge and data about the market opportunities, about the technology of the products of the company, and about the available methods, techniques, software, and equipment. This includes what is already in use in the operation of the company, but also what is available from vendors, universities, and research institutes, etc. The output of the innovation production system is the next generation of products along with the next generation of manufacturing production systems and data production systems. The heart of the innovation production system is qualified creative people with expertise in the market and marketing, in product technology, and in production engineering, data production, as well as manufacturing production, with all that is included in terms of methods, equipment, programmes, and humanities (see also Ref. 3). Figure 9 shows a general lay-out of the total hardware system for all three main production systems in a computer-integrated automatic factory. This may be the type of manufacturing system that is now universally considered in manufacturing research and development. Let us now look upon the manufacturing system and distinguish between the development processes and the operative processes (Fig. 10). As you can see this is only a different picture with the same message as that of Fig. 8. The company is finding a
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Scientific and structural base of manufacturing • G. SOHLEN1US
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strategy to meet customer demands in a competitive manner by using its resources in an efficient way. In the development process the human resources must create product ideas and ideas for the development of the production system. Among several technically possible alternatives, it is necessary to find optimal or at least competitively efficient products and methods. Here it is especially interesting and important to consider the interaction between the ideas and the processes. The use of the axiomatic approach might be helpful here. This interrelation is shown in Fig. 11, where the innovative process is shown on the left and the operational process on the right. Here we can see that the market analysis gives functional requirements on products and product systems. The products are defining functional requirements on the production system and vice versa, and they are to be developed together under an innovative development process. The production system, here covering the DPS and the MPS, is put into operation in the operational process, where it processes material into products. The functional requirements on material are defined in the innovation production process (IPS). The material is brought into the production process and for each production case in terms of delivery the production process defines requirements on the product and vice versa. This is solved in terms of NC programs, process data selection, etc. Finally the product is delivered and used by the customer. The
Fig. 10. Development processes and operative processes within the manufacturing system
customer defines his functional requirements on the product along with his order. Suh et al. refer back to information theory. ~'~ In these terms the processes are information channels and the objects; market, product, production system, and product are physical embodiments or messages. However, for these who arc not familiar with information theory I think that this type of description does not contribute to a clearer understanding. The relations and axioms are quite understandable directly in manufacturing and production terms. 1 will not go deeper into the theories now but rather refer to the literature. CONCLUSION It is necessary within the research and development in manufacturing and especially manufacturing systems to expand gradually the understanding of the possibility of combining creativity and fantasy with precise accurate and reusable scientific descriptions. This can be formulated as follows: What methods can be used in the development of manufacturing systems on a scientific basis and what methods can be used in scientific studies of manufacturing engineering? This involves the problem of what methods can be used to combine the type of knowledge that belongs to the right brain hemisphere with that of the left brain hemisphere. Such things as
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n Inovahve ~
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Fig. 11. Interrelation between the ideas and the processes. FR c o m b i n a t i o n of m o r p h o l o g y with the axiomatic a p p r o a c h seem to be interesting. F u r t h e r m o r e , it is also worthwhile to develop and test the axiomatic a p p r o a c h practically on real cases where p r o d u c t systems are developed along with p r o d u c t i o n systems. REFERENCES
1. Encyclopaedia Britannica, Vol. 14. Chicago, London, Toronto, Geneva, Sydney. 1968. 2. Kaneshige: Quantitative analysis of the axiomatic engineering design. Master thesis, MIT, MA. 1981. 3. Kjcllberg, T.: Integrerat datorst6d f6r m~insklig probleml~sning och m~insklig kommunikation inom verkstadsteknisk produktion begr/insat till produktutveckling, produktionsberedning, konstruktion och tillverkningsberedning. En systemansats baserad ph produktmodeller. Thesis. KTH, Sweden. 1982. (In Swedish.) 4. Nakazawa, H., Suh, N.P.: Process planning based on information conccpt. MIT, MA. 1982. 5. Rinderle, J.R.: Measures of functional coupling in design. Laboratory for Manufacturing and Productivity, MIT, MA. 1982.
functional requirements.
6. Rinderle, J.R., Suh, N.P.: Measures of functional coupling in design. ASME 82-Prod-27. 7. Sohlenius, G.: Datorst6dd produktion f6r verkstadsindustrin--mhl och m6jligheter. Verkst/iderna. (9, 10, 11), 52-62, 36-45, 28-33. 1983. (In Swedish.) 8. Suh, N.P.: Basic science and the synthesis of ncw manufacturing processes. MIT. MA. 9. Suh, N.P., Bell, A.C., Gossard, D,C.: On an axiomatic approach to manufacturing systems. J. Engng Ind. 100: 127, 1978. 10. Suh, N.P., Bell, A.C., Wilson, D.R.: Exploratory study of constraints on design by functional requirements and manufacturing. Annual Progress Report to the National Science Foundation. Grant No. DAR77-13296. 1979-1980. II. Suh, N.P., Rinderle, J.R.: Qualitative and quantitative use of design and manufacturing axioms. Ann. ( ' I R e 31 (1), 1982. 12. Ticc, W.W.: The application of axiomatic design rules to an engine lathe case. MIT. MA. 13. Yasuhara, M.: Axiomatic cngineering design. Its concept and procedure. Master thesis, MIT, MA. 1980.