Computers in Industry 60 (2009) 539–550

The future of computer-aided innovation

Noel Leon

Tecnologico de Monterrey, CIDYT-CIII, Ave. Eugenio Garza Sada # 2501, Col. Tecnologico, Monterrey, NL, CP 64839, Mexico

A R T I C L E I N F O

Article history:

Available online 27 June 2009

Keywords:

Computer Aided Innovation

TRIZ

QFD

A B S T R A C T

A new category of tools known as CAI (computer-aided innovation) is an emerging domain in the array of

computer-aided technologies. CAI has been growing as a response to greater industry demands for

reliability in new products. Some initial CAI ideas and concepts focused on assisting product designers in

the early stage of the design process, but now a more comprehensive vision conceives CAI systems as

beginning at the fuzzy front end of perceiving business opportunities and customer demands, then

continuing during the creative stage in developing inventions and, further on, providing help up to the

point of turning inventions into successful innovations in the marketplace. CAI methods and tools are

partially inspired by Innovation Theories, such as TRIZ, QFD (Quality Function Development), Axiomatic

Design, Synectics, General Theory of Innovation, Mind Mapping, Brain Storming, Lateral Thinking, and

Kansei Engineering, among others. The goal of these new CAI tools under development is to assist

innovators, inventors, designers, process developers and managers in their creative performance, with

the expectation of changes in paradigms through the use of this new category of software tools. CAI,

therefore, stands out as a departure from the usual trends. The latest approaches are presented and

analyzed to derive conclusions regarding the present status and the future of these emerging tools.

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1. Introduction

The evolution of technology is also the history of human beings intheir eternal struggle to dominate their surroundings as part of theirown survival and well being. With the technological evolution,human beings unleashed the capacity to produce useful objects forfulfilling required functions. The transition from resource-basedproducts to knowledge-based products is compelling the NewProduct Development process to be more innovative and efficient,and making innovation processes ever more challenging.

The development of a new category of tools known as CAI(computer-aided innovation) is an emerging domain in the array ofcomputer-aided technologies. CAI is growing as a response to agreater industry demand. These new tools are challenging theprevious standards, with the goal of supporting enterprisesthroughout the entire innovation process. Although some initialideas and concepts of CAI focused on assisting product designers inthe creative stage of the design process, a more comprehensivevision conceives CAI systems as beginning at the fuzzy front end ofperceiving business opportunities and customer demands, thencontinuing during the creative stage of developing inventions and,further on, providing help up to the point of turning inventions intosuccessful innovations in the marketplace.

URL: http://cidyt.mty.itesm.mx.

E-mail address: noel.leon@itesm.mx.

0166-3615/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.compind.2009.05.010

As Product Life Cycle Management tools are being integratedwith knowledge management methods and tools, new alternativesarise regarding the engineering. It is expected that changes ininnovation paradigms will occur through the use of computer-aided innovation methods and tools, the structure of which ispartially inspired by Innovation Theories, such as TRIZ, OTMS-TRIZ,ASIT, Axiomatic Design, Synectics, General Theory of Innovation,Mind Mapping, Brain Storming, and Lateral Thinking. Althoughthese theories were conceived with very different aims and scopes,they are coexisting and competing nowadays in innovation praxisand, in some cases, authors are hybridizing them in the search fornew approaches.

Moreover, the use of new information technologies andmethods, such as semantic web, data mining, text mining, chaostheory and evolutionary algorithms are being increasingly used tomore accurately foretell the next steps in the technologicalevolution and in the development of new products [1–5]. Theimpact of these information technologies will be analyzed inparagraphs 6 and 7. In particular, the impact of evolutionaryalgorithms will be analyzed in depth in paragraph 3.

As a result of these developments and insights, new methodsand tools are taking shape. The goal of these new CAI tools underdevelopment is to assist innovators, inventors, designers, processdevelopers and managers in their creative performance, with theexpectation of changes in paradigms through the use of this newcategory of software tools. CAI, therefore, stands out as a departurefrom the usual trends.

N. Leon / Computers in Industry 60 (2009) 539–550540

In search of efficient innovation, scientists, engineers, aca-demics and managers all over the world are joining in an effort toclarify the essential factors characterizing these new emergingtools for bridging the gap between traditional methods and currenttrends. The following avenues are being researched:

� Clarification of the role of computer-aided innovation tools.� Support for innovation efforts with computer tools and methods.� Support for the Engineer’s Desktop, focusing on the end-to-end

product creation process with methods and tools to ensure thefeasibility and success of innovations in all stages of the new-product development process.� Organizational, technological and cognitive aspects of the

application of CAI methods and tools.� Evaluation of the effectiveness and efficiency of CAI methods and

tools.� CAI’s theoretical foundations.

2. CAI and market analysis

Computer-generated social simulations have been developed toaddress the link between creativity and innovation with the aim ofclarifying the interaction between the change agent (individuallevel of agency) and its social and cultural context in the generationand evaluation processes of innovation. [6]. According to currenttheory, innovations perceived by social groups as having greaterrelative advantage, compatibility, and less complexity will beadopted more rapidly than other innovations. In particular,compatibility with previous solutions and infrastructure canactually benefit creativity and innovation. The adoption of newideas tends to increase as these ideas mature. High quality andcommercial success are usually found not in radical innovativeideas, but in more compatible modifications. A high level ofcompatibility may cause technological innovations to be moresuccessful, yet the opposite may be true for artistic innovations,where the expectation is to break away from current standards.

What level of compatibility would better predict the success ofa new design idea? Computer-generated social simulations areaimed at clarifying this behaviour based on the complementaritiesof generative and evaluative processes by individuals and groups indesign [7]. A framework is being developed cantered on the idea ofsocial groups, which are implemented as multi-agent systems orcellular automata, whose members interact in order to generateand evaluate a range of ideas. This enables the modelling ofsocieties where some agents introduce novel ideas that aresubsequently valued by their social groups. This means thatrandomness is replaced by a more grounded approach to guide theprocesses of generation and evaluation of ideas, where ideas arerepresented as two-dimensional geometrical shapes that someagents (designers) generate and the rest (societies) perceive,evaluate and ultimately adopt or reject. This process is imple-mented by individual mechanisms of shape perception. The socialspaces where agents interact include mechanisms of social

Fig. 1. Graphic description of the system architecture used as a

influence in various dimensions: societies converge and divergeover time re-shaping groups of agents that share preferences,perceptions and/or decisions regarding existing ideas. Fig. 1 showsthe system architecture used.

It should be noted that this figure could be improved bydetailing the rationale behind the layout of the ‘‘adopters’’rectangle and explaining the meaning of the size and positionsof the elements included.

Compatibility is conceptually defined here as the degree towhich a novel idea shares attributes or properties with dominantor competing ideas. This can be expressed in several ways in anygiven computational implementation by gradually traversing thespaces of compatibility and complexity of new ideas in thegenerative processes.

Despite their apparent simplicity, these models generate non-linear effects that emerge from the interaction of their componentsover time. In this way, researchers are equipped with CAI toolswhere they can ‘grow’ different states from a set of initialconditions and gain insights into the role of designers as changeagents in complex systems.

3. CAI and PLM integration

The product design process is becoming more demanding andcomplex. Many empirical studies point out that a structured, goal-oriented design process is mandatory for innovation success.Therefore, innovation-supporting software becomes a key factorfor the NPD process that may guide the project teams through thecomplexity of the market nowadays. As Product Life CycleManagement tools are being integrated with knowledge manage-ment methods and tools, new alternatives arise regarding thecreation of new Engineering Desktop paradigms.

Methods for structural and topological optimization, based ongenerative algorithms, are now used by practitioners to obtainoptimal geometrical solutions that earlier were only possible aftercostly and time-consuming trial and error approaches [8].Currently, new knowledge-based engineering systems supportinnovators’ activity through rules and knowledge re-use, thusreducing product development time while increasing productfunctionality, quality and reducing environmental damage.

The capability to support all the stages of the productdevelopment process in a more integrated fashion will be one ofthe most important competitive factors for these systems in thecoming years. In a concurrent engineering view, in fact, the phasesof conceptual design, optimization and detailed design should beintegrated. To reduce development time and increase effective-ness, design and product development processes have to beconsidered as a continuous iteration of these phases (Fig. 2).

Real potentialities of CAI, TO and KBE tools are reduced due tothe lack of integration between different systems; as theseapplications have been developed in the past as stand-alonesolutions, in most cases they are still unable to communicate witheach other and inter-operate. Hence, it is not only a question of IT

framework to study creativity and innovation in design [6].

Fig. 2. Methods and systems to support the product development process [8].

N. Leon / Computers in Industry 60 (2009) 539–550 541

solutions, but also one of the in-depth focalization on thedevelopment of methodologies and concepts for supportinginnovation teams more effectively and efficiently, thus improvingthe advantages of adopting new integrated CAI systems.

3.1. CAI and problem-solving methods

There are many universal heuristic problem-solving methodsapplicable to conceptual design, such as Brain Storming, MindMapping, Lateral Thinking, Morphologic Matrix, Synectics, Axio-matic Design and TRIZ, among others. Brain Storming, MindMapping and Lateral Thinking are more psychology-based andhelp in braking (o breaking?) psychological inertia. MorphologicMatrix and Synectics provide procedures for the generation ofdesign concepts from a given body of knowledge.

TRIZ (Russian-based acronym for the Theory of InventiveProblem Solving) constitutes the basis of several software packagescontaining methods, procedures and directives that help solvedesign and other technological problems, as it aids in conceptgeneration. TRIZ was developed mainly using knowledge acquiredfrom engineering patents awarded in all engineering domains andis based on the concept that every superior invention is the resultof resolving a contradiction and that technical systems followgeneric tendencies throughout their existence, which have beennamed as the laws of technical system evolution [9].

Therefore, TRIZ supports the generation of creative designconcepts involving resolution by elimination of contradictions.Special emphasis has been placed on the development of CAI toolsbased on TRIZ, which are today commercially available, althoughthis does not mean that from the author’s viewpoint actual TRIZ-based software is effective in supporting the process of solvingcontradictions or substantiating the laws of technologicalevolution.

Interesting research is being conducted in the further devel-opment of the General Theory of Advanced Thinking (Russianacronym: OTSM), as OTSM-TRIZ, for a deeper understanding of therole of contradictions and dialectical thinking in inventive problemsolving and innovation [10,11].

3.2. CAI and optimization

It is known that performance improvement is commonly firstobtained through quantitative changes in parametric design insearch of optimization, but once the resulting improvementreaches its limit and further performance improvements are notpossible, new searches must be carried out through qualitativechanges or paradigm shifts that lead to innovation [12].Optimization, then, is a search process that looks for the mostadvantageous state of equilibrium before a contradictory situationwhere the improvement of one or several performance character-istics brings deterioration in others.

Traditionally two kinds of problems have been distinguished:problems that can be solved by optimization methods and thosethat cannot be solved by such methods [13]. This view is based onthe fact that until now the ‘‘search space’’ susceptible to researchusing optimization methods has been a space in which onlyparametric variations of fixed design structures have beenconsidered possible for study. From this perspective, onlyparametric points of a given search space are evaluated accordingto the problem description. If it is possible to find a parametercombination that solves the problem, then that point is considereda solution and the method ends; otherwise, the problem isconsidered unsolvable with optimization techniques, and otherheuristic methods have to be applied to expand the search space toother variations, such as shapes, topologies and/or differentphysical functional principles.

One of the key issues of optimization techniques is that theyhave evolved because they are efficient in finding a solution, if itexists within the search space. In this article, we hypothesize that ifthere is no solution in the conventional search space, it is possibleto use optimization methods and techniques to expand the searchspace not only to ‘‘parameters’’, but also to shapes, topologies,materials and physical principles that allow the use of the mature,efficient capabilities of optimization methods for effectivelyfinding solutions to problems unsolvable by optimization methodsup to the present. That means that this new kind of ‘‘optimization’’method should be able to ‘‘imagine’’ a completely new system to fitthe problem’s requirements by fulfilling what should be known asComputer-aided Inventive Design.

To achieve that goal, new tools and methods are required, thatare not only capable of searching in the parametric values space,but also of varying other system characteristics that until now havebeen considered the exclusive domain of the human brain. To dothis it is necessary to change the currently available CAD and CAEsystems, originally conceived to facilitate only parametric varia-tions on modelled parts, and give them the capacity to fulfil othertypes of variations automatically. In fact, there are already someinitial attempts that allow other types of variations. A briefdiscussion of some existing methods and tools allowing differenttypes of variations for finding solutions to a given problem follows.

Topology optimization methods have been introduced inmeshing environments to improve product performance [14,15].These topological optimization functions are currently used to findoptimum topologies and shapes for given parts under prearrangedconditions. This is achieved by describing a defined space for the partthrough a finite element (FE) mesh model, while an optimizationalgorithm finds an optimal material distribution within a series ofestablished restrictions. Properties of the FE model, such as densityor Young modulus, are modified during the optimization processuntil an optimum shape is obtained. This type of mesh-levelvariation is practical for finding suggestions regarding part shapesand topologies; however, shapes and topologies obtained this wayare not models with a CAD structure, and they require manual post-processing or even a complete redesign, if converting them into a fullstructured CAD model is desired.

New advances are shown for implementing methods that maybe integrated into conventional CAD/CAE systems for executingshape and topology changes that transcend parametric valueswhile searching for performance enhancements with the aid ofgenetic algorithms and shape and topology variations in both CADand mesh models that may be converted in both directions [16,17].

As long as new optimization methods are not yet fullydeveloped, it has proven useful to allow the interaction ofdesigners with the search process as intermediate steps in thesearch process may stimulate their imagination and ability togenerate new ideas for design variants, which means reducingdesigners’ psychological inertia.

Fig. 3. Pareto front.

N. Leon / Computers in Industry 60 (2009) 539–550542

3.2.1. Optimization tools in CAD/CAE/PLM environments

The further evolution of CAD/CAE/PLM systems should allow adrastic modification in the way companies develop new products,as it is not only possible to virtually test technical solutions atlowers costs and reduced times, but these systems are also able toallow the introduction of innovation-oriented variations in theCAD environment without limitations on human creativity.

Feature-based modelling and tree structures of parts andassembly geometry of 3D CAD systems are suited for linking theprocess of feature ‘‘conception’’, and achieve functional require-ments. However, the psychological inertia of designers is increasedbecause of the long series of operations required for introducingnon-parametric modifications of the parts, sub-assemblies andassemblies of an existing technological system.

New integrated CAD-PLM and multi-physics CAE systems aresuited for facilitating the implementation and simulation of non-parametric modifications as required at different stages of theproduct innovation process. Feature-based modelling integratedinto functional, oriented multi-physic CAE environments is seekingto change the approach of CAD model definition from geometry tofunction-centric geometric entities.

Such structures prepare the way for automatically generatingshape and topological variations of the virtual technological modelthat may be simulated in multi-physics environment. As the numberof possible variations increases exponentially this way, it becomesnecessary to develop new optimization tools that allow a reductionin the search time. However, as this integration is in the beginningstage, performance is still poor, and new research and developmentefforts are needed to enhance efficiency and performance.

3.2.2. Evolutionary algorithms

John H. Holland, a 76-year-old computer science professor atthe University of Michigan, came up with the notion in the early1950s. One of his students, Edward Codd, later won the A.M. TuringAward for designing the first relational databases [18]. Goldberg,also one of Holland’s former Ph.D. students, documented GA in atextbook [19].

A definition of evolutionary algorithms follows: ‘‘Evolutionaryalgorithms’’, also known as genetic algorithms or GAs, take theircue from biological evolution. In sexual reproduction, each parent’sgenes – combined with random genetic mutation – createorganisms with new characteristics, and the less fit organismstend not to pass on their genes to succeeding generations.Evolutionary algorithms work much the same way, but inside acomputer [20]. Genetic algorithms (GAs) act on a population P(t) ofcandidate solutions for exploration in the search space byintroducing variations into the population by means of idealizedgenetic recombination operators. The most important recombina-tion operator is called crossover. By means of the crossoveroperator, two structures in the new population exchange portionsof their internal representation. Another recombination operator isthe mutation. A mutation is a secondary search operator thatincreases the variability of the population by randomly changingeach bit position of the structure in the new population with aprobability equal to the mutation rate M. During each iterationstep, called a generation, the structures in the current populationare evaluated, and, based on those evaluations, a new population ofcandidate solutions is formed by means of the recombinationoperators using the individuals of the former generation thatshowed the best performance. Experimental studies show that GAsexhibit extremely high efficiency. GAs consistently outperformsboth gradient techniques and various forms of random search [21].

By the mid-1990s, engineers at the General Electric ResearchCentre in Niskayuna, New York, had built evolutionary methods intoan in-house design tool called EnGENEous, which was used to findthe most efficient shape for the fan blades in the GE90 jet engines

used on Boeing’s 777 aircraft. After this initial success, GAs wereused in many different applications across all of GE’s businesses.Engineers at Rolls Royce, Honda, and Pratt and Whitney havefollowed suit, incorporating genetic algorithms into their owndesign processes. This approach has also been used recently todevelop antennas, resulting in innovative, unexpected designs [20].

As the basic statement of Altshuller’s patterns of evolutions startsfrom the analogy that technological systems evolve in a manneranalogous to living organisms, the idea of hybridization and randommutations at gens level are inherent to the technological patterns ofevolution [9]. Therefore, theoretically the evolutionary algorithmapproach may be easily related to the concept of pattern ofevolution. Research is being done by the author and his team at theMonterrey Institute of Technology in Mexico in the development ofgenetic algorithm applications in product innovation and in thediscovery of links to the pattern of evolution [22–24].

Most of the real-world GA applications reveal that the objectivefunctions are multi-attribute. Historically, multiple objectives arecombined ad hoc to form a scalar objective function, usually througha linear combination (weighted sum) of the multiple attributes. Afew studies have tried a different approach to multi-criteriaoptimization with GAs: using the GAs to find all possible trade-offs among the multiple, conflicting objectives. These solutions(trade-offs) are non-dominated in that there are no other solutionssuperior in all attributes. In attribute space, the set of non-dominated solutions lies on a surface known as the Pareto optimalfrontier. The goal of a Pareto is to find and maintain a representativesampling of solutions on the Pareto front. Hence, the term‘‘optimize’’ is the reference for finding a solution which would givethe values of all the objective functions an ‘‘acceptable trade off’’ tothe designer. Moreover, computer geneticists have faced the conceptof the ideal [25], and named it the ideal point. The Pareto diagram inFig. 3 (used mainly in multi-objective optimization processes)shows a boundary that divides the region of feasible solutions fromthe point where the ideal solution lies. When there is a set of optimalsolutions lying on a curve that prevents the functions from reachingthe ‘‘ideal’’, at the same time, because of constraints in the solutionspace, reaching the ideal point becomes an unrealistic goal. Someauthors propose performing a set of mono-objective optimizationtasks to reveal conflicts [8].

3.2.3. Structure of 3D CAD systems

In 3D CAD systems the information is structured hierarchically.Shape and topological changes of the parts are performed bypromoting changes in the existing relations between the featuresthat constitute them. To modify the shape of a part, changes in the

Fig. 4. Hierarchical structure of CAD systems.

N. Leon / Computers in Industry 60 (2009) 539–550 543

sketches’ shapes must be performed in the hierarchical topologystructures of the CAD system [12]. The geometric forms of the facesor surfaces, edges and vertexes are dependent on the parentfeatures (see Fig. 4).

An example of possible ‘‘automatic’’ topological variationsmacro is shown in Fig. 5 [16]. These variations are performed by a

Fig. 5. Automatic shape and topological v

Fig. 6. Automotive suspension

macro that is able to connect the abstract functional level with thesubsequent levels of more detailed geometric specification. Thistype of macro is able to generate, on request, topological and shaperedefinitions of the parts and assemblies in CAD systems, whichallows expansion of the optimization techniques search space inCAE environments.

3.2.4. Multibody systems

Multibody systems are used to model the dynamic behaviour ofinterconnected rigid or flexible bodies, each of which may undergolarge translational, rotational or complex spatial displacements.Currently, the term multibody system is related to differentengineering fields, such as mechanisms and robotics, and isextended to the kinematic and dynamic simulation of wholemachines and vehicles. Multibody systems usually offer analgorithmic, computer-aided way to model, analyze, simulateand optimize the motion of interconnected bodies. Introducingoptimization techniques based on evolutionary algorithms maycontribute to developing new mechanisms that perform betterthan existing ones by addressing not only the parametricdimensions of the constituting bodies, but also their topology.

Introducing CAI tools into multibody systems allows, therefore,introducing changes not only into isolated parts of a new product,but also introducing modifications into the whole technical systemevaluating the behaviour of the complete technical system.

For illustration purposes a hypothetical case is shown next inFig. 6, which displays an automotive suspension modelled in acommercial multibody system. Its graphical topology is alsorepresented. This case was selected for explaining topological

ariations in CAD environments [16].

and its graphical topology.

Table 1Topological relationships in a multibody system.

Topology of model: Suspension with bushings

Ground part: ground

JOINT_3 connects Curved-Bar with ground (Revolute Joint)

JOINT_2 connects ground with Bar_Res (Revolute Joint)

BUSHING_Bar_Left_2 connects Bar_Left with ground (Bushing)

BUSHING_Bar_Left connects Ring with Bar_Left (Bushing)

BUSHING_Curved_Bar connects Ring with Curved-Bar (Bushing)

BUSHING_Bar_Res connects Bar_Res with Ring (Bushing)

Vibration connects Ring with ground (Point Motion)

Spring.sforce connects Bar_Res with ground (Single_Comp_Force)

Damper.sforce connects Ring with ground (Single_Comp_Force)

Part ground

Is connected to:

Bar_Res via Spring.sforce (Single_Comp_Force)

Ring via Damper.sforce (Single_Comp_Force)

Curved-Bar via JOINT_3 (Revolute Joint)

Ring via Vibration (Point Motion)

Bar_Left via BUSHING_Bar_Left_2 (Bushing)

Bar_Res via JOINT_2 (Revolute Joint)

Part Ring

Is connected to:

Ground via Vibration (Point Motion)

Ground via Damper.sforce (Single_Comp_Force)

Curved-Bar via BUSHING_Curved-Bar (Bushing)

Bar_Left via BUSHING_Bar_Left (Bushing)

Bar_Res via BUSHING_Bar_Res (Bushing)

Part Bar_Left

Is connected to:

Ground via BUSHING_Bar_Left_2 (Bushing)

Ring via BUSHING_Bar_Left (Bushing)

Part Bar_Res

Is connected to:

Ring via BUSHING_Bar_Res (Bushing)

Ground via Spring.sforce (Single_Comp_Force)

Ground via JOINT_2 (Revolute Joint)

Part Curved-Bar

Is connected to:

Ground via JOINT_3 (Revolute Joint)

Ring via BUSHING_Curved-Bar (Bushing)

N. Leon / Computers in Industry 60 (2009) 539–550544

relationships, as the CAE multibody software selected is integratedinto different commercial CAD systems, in which not onlyparametric but topological modifications as well may beperformed.

The topological relationships are shown in Table 1. As may benoted, the topological relationships are described as textualrelationships between both geometric components and betweenfunctional (forces, vibration) and geometric components.

3.2.5. Open challenges and perspectives in evolutionary algorithms

An open challenge for the future of computer-aided inventivedesign is to be able to automatically perform hybridizationoperations of such multibody systems and also non-parametricmutations of the topologic relationships. These kinds of hybridiza-tions and mutations are commonly performed by engineeringdesigners during innovation processes. An important number ofinnovations in the market are derived from some kind ofhybridization between existing products and new, emergingproducts.

As stated in Section 3.1 Genrich Altshuller [9] discovered thattechnical systems do not evolve at random, but follow certainpatterns, which he called ‘‘Patterns of Evolution’’. It is affirmed thatthey characterize the evolution of a technical system and, thus,could be helpful in foretelling how technological systems willevolve [26]. This approach has been evolving as other authors havebeen adding new proposed ‘‘trends’’ and ‘‘lines’’ of evolution in theattempt to make this approach more current.

From that point of view, these types of hybridizations arecharacterized by one of Altshuller’s patterns of evolution:‘‘increasing complexity, followed by simplicity through integra-tion’’. One example mentioned frequently is that stereo musicsystems have evolved from adding separate components, such asspeakers, AM/FM radio, cassette player, CD player, etc., to anintegrated ‘‘boom box’’.

Although such types of ‘‘hybridization’’ may appear to be toocomplex to perform automatically, it is a question that has to besystematically approached by future computer-aided innovationtools based on evolutionary algorithms, in order to provide themeans to start finding ways that allow the development of newproducts based on the integration of existing or emergingtechnologies. It is to be expected that this will be occurring assimple merging initially, but that it should naturally evolve to beable to perform hybridizations with increased complexity later.This kind of hybridization should be able to deliver ‘‘inventions’’ ofnew products based on the introduction of emerging technologiesinto existing products the way it is performed today by inventorswithout any computer support. As the addition of these newpossibilities increases the number of variants enormously, findingpriorities in hybridizing and mutations requires a systematicapproach. One possibility is to follow the refinement of patterns ofevolutions, also considering the dialectic laws.

As dialectics is the process of looking for contradictions within aphenomenon, it is the main guide to understanding what isoccurring and what is likely to happen. The central approach will

Fig. 8. Dynamic relationships between pattern of evolution and system components

[23].

N. Leon / Computers in Industry 60 (2009) 539–550 545

be finding ways to identify contradictions in a system, whichmeans identifying both the actual state of studied systems and thedesired objectives. This understanding could be reached throughthe simulation of the actual state and a subsequent comparison ofit to the stated objectives and restrictions.

The core difficulty in making the situation evolve from theactual state to the desired one may be clearly elicited in thismanner. This understanding of model behaviour makes it possibleto understand how to make models evolve in order to introducechanges, moving from quantitative to qualitative. Once a contra-diction is elicited, it is necessary to apply the modifications thatenable resolution, i.e., the changes that provide enhancements interms of satisfaction of the stated objectives. As, by definition, thesatisfaction of the objectives cannot be achieved throughquantitative changes, a new inventive solution should emergefrom introducing new variables that will lead to new functions. Thenew set of design variables introduced this way require newevaluation criteria, which have to be evoked not only from‘‘objective’’ technical criteria, but also from the viewpoint of thenew criteria that emerge from the new qualitative change. Thisstep is hard to automate through software means and requires thesearch for new ways to obtain response from the market before thenew product is introduced. Finding computer-aided ways to helpenterprises get market response using surveys or market simula-tion is one of the main challenges that have to be solved bycomputer-aided innovation tools and methods.

One possible approach to looking for the development ofcomputer-aided hybridization tools is to follow the concept ofElementary Architecture of Technological Systems [27] (see Fig. 7).This elementary structure may be used for the likelihood ofchanges in a technological system.

If these subsystems of the elementary general technologicalsystems are put in a table, where the general patterns of evolutionare the columns and the subsystems are the rows, it may beconcluded that different speeds or rates of change are related toeach cell in the table shown in Fig. 8 [23].

Historically, the evolution of ‘‘external’’ components as energysources and material sources have had a stronger influence on thespeed of change in the elementary (internal) components oftechnological systems due to the fact that during the evolution,elementary components influence each other. It is also known thatdriving elements may be applied to many different transmissionsystems and the latter may be applied to many different workingsystems. Very often new products on the market are based on theuse of new driving or transmission elements applied to existingworking systems. On the other hand, the development of newcontrol systems has a cross-influence on all the elements as newsensors and actuators are applied simultaneously in manytechnological systems across all technical disciplines.

This insight may be used for establishing ‘‘optimization’’strategies, as innovations in different components of the elemen-

Fig. 7. Elementary Architecture of Technological Systems [27].

tary technological system may be subsequently applied in existingtechnological systems to achieve new performance levels. Thistype of change may be implemented in search processesdepending on the results given in respective Pareto fronts, if thedesired performance objectives have not been achieved. Forexample, if an optimization process for reducing the weight of acomponent does not achieve the desired performance by changingshapes and dimensions, then, new materials may be used to movetowards the desired objective. Of course, such a decision requiresautomatic access to the information on the new materials requiredthat can be used in the given process.

3.2.6. Example of cross-hybridization in wind generators

To gain insight in the predictions made in the formerparagraphs regarding the development of computer-aided hybri-dization tools, a brief description of a research project being carriedout by the author’s research team to develop more efficient windgenerators using genetic algorithms is presented as a caseexample: the development of new renewable energy sources aredriving the research and use of wind generators. Different windgenerator configurations are available, like the traditional one witha horizontal axis and several other types with vertical axes (seeFig. 9).

The differences are mainly in the ways the rotor geometriestake the energy from the wind and settle on their efficiency. Eachvariant presents its own performance parameters (speed andtorque, among others) that make each one better suited fordifferent applications, i.e., wind generators with a vertical axis arebetter for installation in urban zones because their noise andvibration levels are lower. However, their efficiency (Cp) is inferiorwith respect to traditional horizontal axis wind generators. Fig. 10is a comparative diagram with the Cp values of different types ofwind generators.

Therefore, it is desirable to develop rotor geometry for verticalaxis wind generator with a Cp equivalent to existing vertical axiswind generators while maintaining the advantages of lower noiseand vibration levels. The search process is being performed withgenetic algorithms (GA) through variations of the geometry andthe height/wide relationship of a Savonius-type wind generator(Fig. 11). The results are being evaluated with commercial

Fig. 9. Main types of wind generators.

N. Leon / Computers in Industry 60 (2009) 539–550546

computational fluids dynamics software (CFD). The rotor geometryis, thus, the key point in the search process. First, a review of theliterature showed the evolution of this wind generator type sinceits invention in 1922. Fig. 12 gives the main profiles, and Table 2,their main performance parameters.

Recently, new types of hybrid vertical axis wind generatorshave been developed, which resulted from the combination ofSavonius and Darreius types (see Fig. 12). These hybrid types claimto have better performance than their original ‘‘parents’’. With thattaken into account, the question arose as to whether the GA couldautomatically perform such kinds of hybridizations in the searchfor better fulfilling the ‘‘optimization’’ objective. In this specialcase, the hybridization turned out to be rather simple, as it

required just one assembly operation, which is quite common inCAD Systems.

Following this trend, the researchers implemented a procedurefor obtaining combinations of different rotor types using a newtype of hybridization in GAs, which could be considered a‘‘transgenic’’ crossover. Fig. 13 shows a crossover of Savonius–Darreius vertical axis rotors, which was obtained this way. Thiscombination was prepared for performance in the ‘‘optimization’’search process. In this case, the ‘‘hybridization’’ has been preparedfollowing a known existing tendency in the evolution of verticalaxis wind generators; however, this example demonstrates thepossibility that such combinations may be ‘‘automatically’’performed without human intervention, by expanding the search

Fig. 10. Cp with respect to the TSR (tip speed ratio) of most common wind

generators.

Fig. 12. Combination of vertical axis wind generators: (a) Savonius–Darreius

traditional combination and (b) Rotor Becker.

Fig. 13. Combinations of Savonius–Darreius vertical axis wind generators.

N. Leon / Computers in Industry 60 (2009) 539–550 547

space in ‘‘optimization’’ processes. In Fig. 14 new combinationpossibilities are shown.

Besides this possibility, such relatively new concepts as‘‘cataclysmic’’ mutations have also started to play an interestingrole in optimization with evolutionary algorithms. These newconcepts are leading to an expanded scope in optimizationprocesses, as they allow development of solutions that untilnow have been considered the exclusive domain of humancreativity.

3.3. CAI and industrial design

Novel computer-supported design systems use computationalapproaches for producing 3D images to stimulate creativity indesigners. These approaches are also based on evolutionary

Fig. 11. Main variants of the Savonius rotor. (a) Savonius without separation, (b) Savonius with separation, (c) Benesh 1988, (d) Benesh 1996 and (e) Rahai.

Table 2Main performance parameters of different Savonious-type wind generators.

Rotor TSR Cp Source

Savonius 0.85 �24.5 Blackwell 1977, separacion/diam = 0.1, altura/diam = 1

0.95 �26.5 Blackwell 1977, eparacion/diam = 0.1, altura/diam = 1.5

0.85–1.15 �27 Moutsoglou 1995

Benesh 1988 NA 33 US Pat. 4,784,568

0.85–1.15 �30 Moutsoglou 1995

Benesh 1996 NA 37 US Pat. 5,494,407

Rahai 1.45 >40 US Pat. App. 2007/0104582

Fig. 14. Profiles of vertical axis wind generators (a) Lenz, (b) MacWind Alpha and (c)

Darreious.

N. Leon / Computers in Industry 60 (2009) 539–550548

algorithms acting, in this case, on a binary tree for generating 3Dimages [28]. One approach is illustrated by an artwork designexample which uses general complex function expressions to form3D images of artistic flowers (Fig. 15). It shows that this approach isable to generate some innovative solutions.

4. Semantic Web, text and data mining

The concept of Semantic Web stated by Tim Berners-Lee alreadyin 1999 affirms: ‘‘If HTML and the Web made all the onlinedocuments look like one huge book, RDF, schema, and inferencelanguages will make all the data in the world look like one hugedatabase’’ [1]. A major part of INTERNET and INTRANET informa-tion, including patent databases, is unstructured in the form offiles, databases, archives, etc. Information is contained in differentformats and different systems, and also differs with regard to thelevel of access. Such a diverse and ever increasing data flow needsfiltering and convenient structuring, and the concept of pattern ofevolution may become the basic structuring direction.

Several tools, like the tool for patent analysis described in thenext paragraph, are undergoing development initiatives that afford

Fig. 15. Artistic flowers generated by c

advanced text-analysis technology. These tools are providing thepossibility of automatically identifying and extracting entitiesfrom any text data source, in multiple languages. Commercial textmining products claim that they make it possible to extract keyconcepts, sentiments, and relationships from textual or ‘‘unstruc-tured’’ data and convert them to a structured format that can beused to create predictive models [2,3].

4.1. CAI and patent analysis

Although patent examiners are sceptical about the adoption ofsoftware instruments as a substitute for traditional art analyses,the development of a means to reduce the number of documents tobrowse is considered a top-level priority [29]. With the samepurpose of reducing human involvement, the reduction of theamount of text to be read is also an essential goal.

Algorithms and tools have been developed for patent analysisaimed at

� Translating the description of an invention into a conceptualfunctional map [30];� Identifying knowledge flows between different fields of applica-

tion [31];� Investigating the properties of Small World Networks as a

foundation for a computer-based idea generation system [32].

The identification of the paragraphs disclosing the inventionpeculiarities, the most relevant part of a patent, has emerged as themost challenging issue in computerized patent analyses. Novelcomputerized analyses provide the capability of identifyingselected excerpts of the patent description by connecting to each

omplex function expressions [28].

Fig. 16. Functional map of US patent 5,328,488 ‘‘Laser light irradiation apparatus for medical treatment’’ as presented in [33].

N. Leon / Computers in Industry 60 (2009) 539–550 549

other through the functional map of the invention, so that anexpert in the field can focus his/her attention on just a fewsentences, instead of reading the whole text (see Fig. 16). Thealgorithm used in [33] is capable of performing the functionalanalysis of an invention automatically, by processing the descrip-tion and the claims of the related patent.

It can be stated that an expert in the field will be able tounderstand the content of those paragraphs without reading thewhole document, at least to the extent of recognizing the relevanceof the patent and its core novelty.

5. Chaos theory

Chaos theory was formulated during the 1960s. In 1961,Edward Lorentz discovered the butterfly effect while trying toforecast the weather [4]. One of the foremost contributors to thenew science was Benoit Mandelbrot [5], who, using a homecomputer (1982), pioneered the mathematics of fractals, a term hecoined in 1975. His fractals (the geometry of fractional dimen-sions) helped describe or picture the actions of chaos, rather thanexplain it. Chaos and its workings could now be seen in colour on ahome computer.

Perhaps the most startling finding to come out of this newscientific theory is that order exists within chaos. In fact, ordercomes from chaotic conditions. This statement is the main reasonwhy this technique is considered to be useful for findingstructured, ordered pattern of evolution within the apparentchaos in technological system evolution.

6. CAI and prototype testing

Computer simulation as to-be scenario and as-is scenarioduring product prototype testing highlight shows how the use ofreal-time simulations facilitates innovative methods. By support-ing the to-be test procedure with real-time simulations and 3Dvisualization in particular, the way prototypes tests are conductedradically changes to a more concurrent test process, whichfacilitates distributed collaborative work [34]. In addition, theapproach enables decision making to become a more concurrentactivity, since off-site experts can take part simultaneously in thetesting activities. The approach raises opportunities to extract rich

information on prototypes and their systems, which provides agood basis for well-informed decisions. The connection betweenthe prototype test running and the development office makes for agood base for future innovation in prototype validation anddevelopment.

7. Conclusions

Computer-aided innovation is an emerging domain in the arrayof CAx technologies. The goal of CAI is to support enterprisesthroughout the entire innovation process. A comprehensive visionof CAI systems begins at the creative stage of perceiving businessopportunities and customer demands, continues in the phase ofproviding help for developing inventions and, further on, provideshelp up to the point of turning inventions into successfulinnovations for the market.

As Product Life Cycle Management tools are being integratedwith knowledge management methods and tools, new alternativesarise regarding the Manager and Engineers Desktop. It is expectedthat changes in innovation paradigms will occur through the use ofcomputer-aided innovation methods and tools, and that newinformation technologies, such as Semantic Web, Text and DataMining, chaos theory and Evolutionary Algorithms, will play animportant role in the future of computer-aided innovation.

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Noel Leon is a full professor in the Centre for Innovation

in Design & Technology at Technological Institute of

Monterrey, Mexico. His research work is oriented to

Computer-Aided Innovation, product concept develop-

ment, Eco-Innovation, CAD&CAE and TRIZ. He has a

BEng & MS degrees in mechanical engineering and a

Ph.D. degree in machine design from Dresden Technical

University, Germany. He was founder and director of

the Centre for Studies of CAD&CAM at Higher Techno-

logical Institute of Holguin, Cuba. He has several years

of engineering experience working in projects with

industry. He is also a board member of the Working

Group of Computer-Aided Innovation from IFIP.