Niccolò Becattini
email: niccolo.becattini@kaemart.itgaetano.cascini@polimi.ityuri.borgianni

Yuri Borgianni

Gaetano Cascini

Federico Rotini
email: federico.rotini@unifi.it

Computer-Aided Problem Solving -Part 1: Objectives, Approaches, Opportunities

Keywords: Computer-Aided Innovation, inventive problem solving, conceptual design, dialogue-based system

Among the different aims and scopes of Computer-Aided Innovation (CAI) systems a relevant topic is the support of inventive problem solving tasks. The paper presents the research activity developed by the authors in this domain, encompassing the review of the distinctive features of problems encountered by designers and the common approaches employed to overcome them. A further thread of the investigation carried out in this paper concerns the limitations of computer-based approaches exploiting acknowledged models for problem solving. Downstream of the performed analysis the authors highlight the requirements that a novel CAI application should fulfil, supporting the opportunities for building a dialogue-based system.

Introduction

CAD/CAE systems have reached considerable performances in terms of rapidly carrying out routine tasks, allowing to successfully perform detailed design activities.

On the contrary, their development has not so far concerned the ability to support the early stages of the design process, such as conceptual design, resulting in limited functionalities to enhance product development cycles. Such design phases, in which designers" creativity and inventiveness are primarily exploited, play a crucial role for the success of the products in the marketplace. Thus, considering companies" double goal of delivering innovative products and reducing the time to market, CAD/CAE systems have consistently focused just on the second objective, by speeding up the processes concerning detailed design. Indeed, starting from the CAD-CAM systems which spread since the "80s, computer means supporting the product development cycle have progressively extended their domain of application, also acquiring virtual prototyping capabilities. Product models correspondingly extended the detail level of representation, in order to allow simplified simulation and testing. In the last decade, CAD/CAE systems have been integrated into PLM (Product Lifecycle Management), further extending their domain of application, but still missing to support the earlier stages of design. Figure 1 represents the domains of application of these systems with respect to the phases of product development and the abstraction level of the adopted product models.

Thus, the aforementioned scope of shortening the lead times has resulted in less accurate initial design phases, with poor support to problem investigation, function analysis, solutions generation and exploration [START_REF] Tay | Product modeling for conceptual design support[END_REF], thus shrinking innovation opportunities.

Computer-Aided Innovation (CAI) systems emerged for suitably assisting the conceptual design phase. As depicted in figure 1, the borders of CAI systems domain is still fuzzy, but their capability to support problem solving seems to be a core element on which the scientific community agrees.

Within this context, the authors" research aims at developing an original computer application viable to overcome the deficiencies of CAD/CAE tools with respect to conceptual design and the limitations of CAI systems in exploiting designers" skills, creativity and intangible knowledge.

Due to space limitations, the recalled subjects are treated in two separate and complementary manuscripts: the current one deals with the identification of the requirements to be fulfilled and the tasks to be performed by a CAI problem solving tool. With reference to these findings, the second paper illustrates a dialogue-based algorithm, embedding the TRIZ logic, tailored for people without any background in problem solving methods. By aiding the investigation of a technical problem, such algorithm represents a first module for a complete CAI system to support the conceptual design phase.

According to these premises, this paper firstly performs a review of problems typically encountered by designers (Section 2); then, problem solving approaches and computer-based approaches for problem solving are discussed, focusing on the role played by knowledge in the design context (Section 3). The investigation within innovation tools in design subsequently focuses on the opportunities represented by dialogue-based systems and the employment of natural language (Section 4). The outcomes of the whole research are finally summarized in Section 5.

Types of Problems

In the literature there is a plenty of definitions of the term "invention": among the others, for the scopes of the present paper, it is useful to mention the followings:

 Patent Law recognizes as inventive a technical solution that is useful, novel (no single prior art reference shows the identical development), and unobvious to a person "skilled in the art";  Cavallucci et al. associate the concept of invention to the transfer of knowledge between different fields of application; in [START_REF] Cavallucci | Assisting R&D activities through definition of problem mapping[END_REF] it is claimed that "Inventive R&D requires the use of knowledge or know how from a substantially different technical domain".

The first definition is here assumed as the reference to identify an invention, since it is more universally accepted, at least in the industrial world. It relates to the usefulness of an invention, meaning that the technical solution is addressed to satisfy a need, which implies to solve an existing and acknowledged problem. Nevertheless, the second definition is relevant for a class of "inventive problems" and requires a specific solving approach.

Technical problems can be indeed distinguished between inventive and noninventive, whereas demands and cognitive processes make the differences in this classification. Non-inventive problems don"t require any inventive step, thus relate to situations where the desired outcome can be achieved just by means of an optimal adjustment of system parameters. On the contrary, inventive problems are characterized by at least two conflicting requirements that cannot be satisfied by choosing the optimized values for system parameters. Thus, inventive problems require an inventive solution, which means that it"s necessary to solve a contradiction in order to produce a useful, novel and unobvious answer to an emerging demand.

From another point of view "difficult problems", according to Funke and Fresch [START_REF] Funke | Complex problem solving: The European Perspective[END_REF], have at least one of four specific characteristics that make them harder to solve than "easy problems":  Intransparency, due to the ill-definition of the problem itself, some elements required to achieve the solution are not known;  Complexity, due to the great number of parameters of the technical system(s) and their mutual connections;  Dynamics, due to either time-dependent characteristics of relevant features, or to the need of achieving the solution under time pressure;  Polytely, which means that the problem is characterized by multiple, noncompatible goals.

From a different perspective, it is possible to distinguish between typical and nontypical problems. Both problems can be approached by means of rules and procedures, but the second ones cannot be solved just by means of such methods; their solution requires the support of knowledge provided by the problem solver and the available informational support. An example of typical problem is the calculation of the roots of an equation of the n-th degree: it"s required just to know the appropriate formulas (available for equation from first to 4th degree) or a method (e.g. Ruffini-Horner) that provides the desired approximation of their values. On the contrary, when the problem is non-typical, even if some solution path is provided, the solution cannot be entrusted just to the method itself. This problem solving process requires elements of knowledge that reside outside the boundaries of the method and its language. To get to the desired outcome it"s necessary to leverage personal knowledge as well as personal wisdom.

Finally, Simon, in [START_REF] Simon | The structure of ill-structured problems[END_REF], explores the distinction between a well-structured problem (WSP) and an ill-structured problem (ISP): "… a problem may be regarded as well structured to the extent that it has some or all the following characteristics:

1. There is a definite criterion for testing any proposed solution, and a mechanizable process for applying the criterion; 2. There is at least one problem space in which can be represented the initial problem state, the goal state, and all other states that may be reached, or considered, in the course of attempting a solution of the problem; 3. Attainable state changes (legal moves) can be represented in a problem space, as transitions from given states to the states directly attainable from them (…); 4. Any knowledge that the problem solver can acquire about the problem can be represented in one or more problem spaces; 5. If the actual problem involves acting upon the external world, then the definition of state changes and of the effects upon the state of applying any operator reflect with complete accuracy in one or more problem spaces the laws (laws of nature) that govern the external world;

6. All of these conditions hold in the strong sense that the basic processes postulated require only practicable amounts of computation, and the information postulated is effectively available to the processes -i.e., available with the help of only practicable amount of search.…"

His description is founded, by his own admission, on a set of features that problems must have in order to be considered as WSP, since it"s impossible to provide a formal definition. The class of ISP, indeed, must be considered as a residual of the one related to WSP.

Given these commonly faced situations, Computer-Aided Innovation (CAI) systems constitute an emerging technology for assisting the conceptual design phase by supporting the solution of inventive problems, but also improving the efficiency of any problem solving activity arising in an innovation process. A framework for Computer-Aided systems should be thus capable to face and solve:

 inventive problems by the search of conflicting requirements and the identification of features that the technical solution should have;  difficult problems by both clarifying their definition and prioritizing the objectives;  non-typical problems by helping the user in retrieving useful data and information from various domains;  ill-structured problems by organizing the designer"s knowledge according to a framework useful for a successful solution.

These objectives are achieved if the framework helps to approach the problem with a structured process that, during the analysis, stimulates the user"s knowledge and his/her creative skills. Thus, a problem solving tool should have the capability of assisting the user in the exploitation of his/her competences during the solving process. Moreover, when the solution space depicted by the designer doesn"t provide any successful result, the CAI system should drive the user in the widening of its design space by enriching it with external knowledge. Relevant information should be first searched in a close field of application, since also experts not necessarily fully master their own domain; then, the search should be extended to completely different domains of technical knowledge where similar problems have been already solved with success. With the goal of maximizing the efficiency of the innovation process, such an information search must be performed as automatically as possible by the computer system and should require minimal efforts to the designer.

Problem Solving

This section introduces some key concepts related to inventive problem solving and conceptual design, with references to approaches, techniques and research insights relevant for the present work.

Problem Solving Approaches

The emerging demands for better performances, the reduction of resources consumptions and the removal of harmful side effects generate technical problems due to the conflicting nature of these features. Whenever the optimization of the values of such parameters allows to satisfy the design demands within the established constraints, the solution doesn"t require any inventive activity. On the contrary, whenever two or more requirements (demands or constraints) appear as non-mutually compatible just by changing the values of the related design parameters, the issue requires a dialectical approach in order to redesign the system. This inventive problem task implies that the new solution (synthesis) must integrate the needed features (thesis and antithesis) after the removal of the causes that prevent their direct implementation. Optimization and trade off could be considered as routine design, while inventive problem solving relates to non-routine design [START_REF] Gero | Modeling Creativity and Knowledge-Based Creative Design[END_REF].

Non-routine design activity requires a creative contribution for problem solving. The creativity leaps underneath the solving process have been deeply studied since the "70s [START_REF] Newell | Human problem solving[END_REF][START_REF] Dorst | Design Problems and Design Paradoxes[END_REF][START_REF] Kruger | Solution driven versus problem driven design: strategies and outcomes[END_REF] both to understand human thinking and to provide an efficient way to improve the problem solving activity. With a particular emphasis in [START_REF] Simon | The structure of ill-structured problems[END_REF], Simon distinguishes between ill-structured and well-structured problems and observes that the problem solving approach should be the same, regardless of the problem structure. In a recent paper [START_REF] Dorst | Design Problems and Design Paradoxes[END_REF], Dorst calls into question the differences claimed by Simon between well-structured and ill-structured problems, also by highlighting that those differences mainly reside in the skills of the problem solver of turning the latter into the former. Therefore, even according to Simon, the designer"s subjectivity becomes relevant for the design process, since the greatest part of its creative contribute is addressed in changing the structure of the problem. To this end, particular regard should be given to the problem solver"s interpretation of the problem, both taking into account his knowledge and the method he follows. Kruger and Cross [START_REF] Kruger | Solution driven versus problem driven design: strategies and outcomes[END_REF] recognize four different types of cognitive strategies in design:

1. Problem driven design: the designer focuses closely on the problem at hand and only uses information and knowledge that is strictly needed to solve the problem.

The emphasis lies on defining the problem. 2. Solution driven design: the designer focuses on generating solutions, and only gathers information that is needed to further develop a solution. The emphasis lies on generating solutions. 3. Information driven design: the designer focuses on gathering information from external sources, and develops a solution on the basis of this information. 4. Knowledge driven design: the designer focuses on using prior, structured, personal knowledge, and develops a solution on the basis of this knowledge. Only minimal necessary information from external sources is gathered.

The solution, whatever the adopted strategy, must be found by means of the creative leap of the problem solver or even by increasing his/her knowledge by information gathering. Beyond the cognitive aspects, the above strategies are characterized by different approaches to systematize the design process, then it is worth to make a brief distinction between cognitive and systematic features of the problem solving methods.

Cognitive approaches are focused on creative thinking features like analogy, abstraction and references to previous experiences by associations of ideas. Furthermore, they can be used regardless of the technical/industrial domain and the increase of their effectiveness must rely on multidisciplinary working teams composed by creative people. These methods leverage tacit knowledge, stimulate "cross-fertilization" thinking processes and individual creative attitude upon appropriate conditioning techniques [START_REF] Ross | A model of inventive ideation[END_REF].

On the other hand, systematic approaches of problem solving are characterized by linear and ordered "step-by-step" procedures that drive the design process. They rely on explicit knowledge such as information and data available in handbooks, patents, and scientific literature. Furthermore, both teams of experts and individuals can use them since the creative attitudes are welcome, but not strictly necessary. One of the greatest restrictions of these methods stands in their limited versatility: they are suitable just for specific kinds of problems and are hard to be generalized for different expertise domains.

Despite many creative process models and techniques might be considered, e.g. those reviewed in [START_REF] Howard | Describing the creative design process by the integration of engineering design and cognitive psychology literature[END_REF], the discussion is here limited to four well known problem solving methods because they allow to highlight a complete set of relevant issues for the present research. Let"s examine in detail the main features, differences and weak points of two methods mostly based on the cognitive aspects of idea generation (Brainstorming and CBR, Case-Based Reasoning) and two methods relying on systematic procedures for generating a solution (CPS, Constraint Satisfaction Problem and TRIZ, Theory for Inventive Problem Solving).

Brainstorming is a well known method for generating a flood of new ideas: a group of open-minded and creative experts in various domains elicits ideas and thoughts to solve the given problem during sessions, thus triggering off new ideas in the mind of other participants. This method is strongly based on memory stimulation and on the association of ideas from different knowledge domains. Pahl and Beitz [START_REF] Pahl | Engineering Design -A Systematic Approach, 2nd Edition[END_REF] suggest the use of such method whenever the design phase comes to a deadlock because of the lack of practical solution concepts and physical principles to be exploited to get the desired outcomes. During a brainstorming session the working team may produce a huge number of ideas even if the greatest part of them is far from being technically or economically feasible. The greatest drawback of this method stands in its implicit trial and error approach: the generated ideas require appropriate validation and the efficiency of the whole innovation process results to be quite poor. Moreover, a brainstorming session intrinsically leverages only the knowledge of the individuals involved in the idea generation.

The aim of Case-Based Reasoning for problem solving is to build a new solution for a given problem by adapting solutions of past cases [12; 13]. This method makes use of cognitive resources with a different approach, since it leverages an archive of successfully solved cases. Despite its cognitive nature, CBR relies on a well structured procedure described by some authors as the "4 R": "Retrieve, Reuse, Refine and Retain". During the retrieval phase the knowledge base of successfully solved cases is searched by exploiting an indexing system that summarizes relevant features of the cases themselves. The best solution gets chosen by means of analogy and similarity criteria. The Reuse phase associates the solution of the retrieved case to the new problem, while the refinement phase checks the overall validity of the proposed solution by verifying whether any modifications are required. The retain phase starts whenever the solution has been adapted to the problem: the new case, comprising the initial problem and its solution, becomes part of the knowledge base of the CBR system.

Constraints Satisfaction Problems (CSP) is a method for finding suitable values for variables subjected to constraints on acceptable combinations of values [START_REF] Kumar | Algorithms for Constraint-Satisfaction Problems: A Survey[END_REF]. A CSP model consists of a set of variables, a set of possible values for each variable and a set of constraints the variables must satisfy. Whenever these constraints cannot be completely satisfied, the user has to deal with an over-constrained problem, an optimization problem that requires non-routine design since its solution cannot be achieved by means of values adjustment. Several CSP methods for over-constrained problem are available to solve non-routine design problems such as Constraint hierarchies, Partial CSP and Extending CSP (Probabilistic CSP; Fuzzy CSP; Weighted CSP). All these methods can be classified according to two general strategies:

 those (as Extending CSP, Constraint hierarchies) that allow to state preferences about constraints, choosing the ones to be overridden whenever the problem model is over-constrained. The CSP algorithm tries to solve the problem by satisfying all the constraints; otherwise the solution is searched by satisfying as many constraints as possible, fulfilling all the required or "hard" ones and neglecting the ones marked as preferential or "soft".  those (as Partial CSP) that allow to relax the boundary values of variables and constraints whenever an over-constrained problem arises. Nevertheless these methods don"t allow the introduction of new variables in the problem model, reducing the space of potential solutions.

As well known by TRIZ practitioners, the theory developed by Altshuller [START_REF] Altshuller | Creativity as an Exact Science[END_REF] has several models to describe problems, technical systems, solutions and to address the problem solving process, which is systematically driven according to its main classical tool, the Algorithm of Inventive Problem Solving (ARIZ). ARIZ is a stepby-step procedure that integrates all the others TRIZ instruments and brings from the analysis of two contradictory requirements to the synthesis of a new technical system capable of fulfilling such goal. From a formal point of view, this method cannot be considered as completely systematic since "ARIZ is a tool to aid thinking, but it cannot replace thought itself, (...) if the human brain does not use the power of a lifetime"s knowledge, a lot of potential associations and images would be neglected. But it is exactly the sparks of imagination that lead humans to produce creative non typical ideas" [START_REF] Khomenko | A Framework for OTSM-TRIZ Based Computer Support to be used in Complex Problem Management[END_REF].

The above-described features of these four methods are depicted in Figure 2, 3 and 4 by means of boxes. Each box qualitatively places the method in a two dimensional diagram, where each axis is referred to a specific characteristic. The boxes may have continuous or dashed border meaning, respectively, that the described characteristic is explicitly presented in the reference or has been inferred by the authors of this paper. Whenever a specific feature of a method cannot be certainly classified because of its intrinsic variability the related box gets wider.

Both cognitive and systematic methods of problem solving have strong and weak points. The importance of individual knowledge is crucial in problem solving activities, and then it"s important to combine the power of systematic approaches, in order to overcome with efficient processes the boundaries of personal creativity, with the capability of cognitive methods to leverage individual tacit knowledge.

Computer-Aided Systems for Problem Solving and Conceptual Design

The domain of Computer Aided Innovation (CAI) includes systems aimed at assisting Inventive Problem Solving by stimulating creativity and guiding towards suitable problem solving paths. In the last decade, Information Technology systems have substantially fostered a shared vision of creative patterns among different disciplines, resulting in a consistently growing interest in creativity concept [START_REF] Candy | The digital muse: HCI in support of creativity, creativity and cognition comes of age[END_REF]. This led towards the birth of a novel and fertile field of research, namely the interplay between creativity stimulation and computer systems. Given the development of software systems that support the human creativity, Lubart [START_REF] Lubart | How can computers be partners in the creative process: Classification and commentary on the Special Issue[END_REF] eventually proposes a classification among the ways such aid is provided, with a growing degree of machine involvement:

 by facilitating the management of the working process, encouraging the perseverance of designer in the research of innovative solutions;  by easing the communication between design team members, since circulation and integration of ideas play a relevant role in the creative process;  by aiding the designer with a coaching activity, acting as an expert system that guides the user throughout cognitive processes;  by cooperating in the creative process, thanks to the Artificial Intelligence systems that contribute to ideas generation.

Across all kinds of computer supports for the exploitation of creativity, some emerging issues are argued about the development and the requirements of these systems, thus including those mostly addressed to conceptual design and problem solving. Within computer based environments, Hewett [START_REF] Hewett | Informing the design of computer-based environments to support creativity[END_REF] remarks that creative key conditions and processes are not domain specific, as well as the need is stressed for analysing different facets of the problem under different perspectives. Shneiderman [START_REF] Shneiderman | Creativity support tools: accelerating discovery and innovation[END_REF], performing a review of well-acknowledged design principles to support creativity, highlights the importance of outlining the steps that led to partial solutions in order to maintain comparative criteria for evaluating different options.

It is beyond the objective of this manuscript to provide a complete state of the art of principles and hints that are suggested for successful creative CAI tools. However, the issues that have been briefly introduced provide a common background and a sufficient support in order to reveal inadequate features of the software systems that have implemented the four problem solving methodologies described in the Section 3.1, regardless of the predominance of cognitive or systematic aspects. The CAI applications with a brainstorming framework reflect the recalled lacks of the methodology in selecting the most promising solutions due to the lack of assessment criteria; nevertheless in [START_REF] Hüsig | Computer aided innovation-State of the art from a new product development perspective[END_REF] it is claimed that several cognitive methods, including Brainstorming, have been implemented into software tools. Beyond their preferential development as Artificial Intelligent systems for help desks and customer services, CBR tools are characterized by being strongly domain specific. By their definition and way of working, CPS systems relentlessly lead to the embodiment of a trade off among various requirements, since they propose only partial resolution of the problem by constraints relaxing [START_REF] Dubois | Comparison of non solvable problem solving principles issued from CSP and TRIZ[END_REF]. Regarding TRIZ based CAI tools, classified among the most diffused software systems for idea generation [START_REF] Hüsig | Computer aided innovation-State of the art from a new product development perspective[END_REF][START_REF] Shai | Creative conceptual design: Extending the scope by infused design[END_REF], their main limitation stands in the implementation of a "generic scheme (almost algorithmic) for designing new product or concept from existing ones", providing "inventions of only incremental nature" [START_REF] Mitra | Computational Creativity: Three Generations of Research and Beyond[END_REF]. Thus the richness characterizing the systematic problem solving paths of TRIZ gets lost together with the abstraction capabilities and the range of aspects otherwise depicted. Also other authors [START_REF] Rovira | The future of computer-aided innovation[END_REF] judge unsatisfactory the performances of currently available TRIZ software, appointing the attention to a closer link with other design tools and to role played by "dialectical thinking in inventive problem solving and innovation". Figure 5 shows how much the abovementioned methods have been implemented into software tools together with their adaptability to case studies in different fields of technique. to their level of integration in computer-aided tools.

The Role Played by the Information Gathering Task within Conceptual Design

The literature clarifies how information gathering currently requires a meaningful amount of time spent by designers, especially during the conceptual design stage. At the same time engineering designers, especially those with limited experience, are not always aware of the information they require [START_REF] Ahmed | An Approach to Assist Designers with their queries and designs[END_REF]. Interpersonal communication emerges as a key resource for progressing design problems in a practical way: designers generally prefer to source knowledge and information through informal interactions with their colleagues [START_REF] Aurisicchio | Understanding how the information requests of aerospace engineering designers influence information-seeking behaviour[END_REF]. However, it is expected that designers will increasingly have to rely on retrieving information captured and stored independently of human memory. These reasons provide compelling evidence about the need, for computer-aided problem solving frameworks, to quickly and correctly formulate queries for the investigation of knowledge databases. As a consequence, with the aim of speeding up the search for valuable information, particular attention has to be paid to the analysis of the encountered problem, so that the main criticalities are individuated, as well as the most characterizing technical parameters, elements of the system, features. A CAI tool for supporting conceptual design therefore requires to guide the user in an accurate and systematic examination of the problem to be faced, clarifying the scopes and the priorities in the solution search, especially in the cases characterized by multiple tasks, complex situations, and tangled interrelations among parameters, effects and physical phenomena. The utmost aim should be to provide the designer, downstream the situation analysis, a limited number of solutions, characterized by promising development and great feasibility.

4

The Opportunities Offered by Dialogue-Based Systems

Within the methods supporting conceptual design with an intensive human involvement, which are currently deemed to be more reliable [START_REF] Rohde | Design knowledge indexing in relational database[END_REF], a dialogue-based system represents a chance for embodying an application to support inventive problem solving in a CAI system. The human-computer dialogue field is claimed to be a rather developed technology and questionnaires administered in a digital form are widespread and increasing. The strong points of computer based dynamic questionnaires lie upon their speed in being carried out [START_REF] Quarteroni | Designing an interactive open-domain question answering system[END_REF] and suitability within information retrieval systems [START_REF] Hardré | Testing differential effects of computer-based, web-based and paper-based administration of questionnaire research instruments[END_REF].

The removal of specific jargon to support users without wide vocational experience, regardless the theoretical support to be chosen for a novel CAI application, encourages to develop a dialogue by the employment of natural language. The literature clarifies that natural language processing is widespread and employed in different engineering domains, as reviewed in [START_REF] Chiu | Biomimetic design through natural language analysis to facilitate crossdomain information retrieval[END_REF]. The research opportunities emerging within design field by linking natural language to engineering and technical concepts [START_REF] Zeng | Recursive object model (ROM) -Modelling of linguistic information in engineering design[END_REF], resulted in an increased interest on questioning techniques. Eris [START_REF] Eris | Asking Generative Design Questions: a Fundamental Cognitive Mechanism in Design Thinking[END_REF] has thus introduced new question categories within well-established taxonomies, by observing the nature of commonly asked concepts within design process. The observations he had carried out gave rise to Generative Design Questions, i.e. explorative queries representing the designer"s diverging way of thinking, that investigate possible outcomes and embodiments of a system, as well as the ways to achieve the desired outputs.

As a consequence of the developing research field, the attempts of applying Question-Answer techniques within design process are quite diffused. Wang and Zeng [START_REF] Wang | Asking the right questions to elicit product requirements[END_REF] have elaborated an iterative question asking method with the aim of correctly identifying the requirements of new products on the basis of customer surveys. Andersson [START_REF] Andersson | An iterative Question-Answer driven process to support product design[END_REF] employs a Question-Answer tool to be employed in the final design stages, such as the verification of the product requirements. Less efforts have been paid towards the employment of such tools within conceptual design.

A particular branch of question-answering sequences in the field of conceptual design has been developed to support Conversational Case-Based Reasoning (CCBR), an extension of the Case-Based Reasoning paradigm [START_REF] Aha | Introduction: Interactive Case-Based Reasoning[END_REF]. The most relevant advantages of CCBR are related to its capability to incrementally elicit the problem formulation through the interactive dialogue with the user [START_REF] López De Mántaras | Retrieval, reuse, revision and retention in case-based reasoning[END_REF], as well as their flexibility of use and the ease of implementation [START_REF] Aha | Conversational Case-Based Reasoning[END_REF]. Unfortunately their employment domains rarely overlap with engineering design. The dialogue based system, that is individuated as a chance for a proper CAI system, should thus strive to fulfil the benefits highlighted in the brief overview of Question/Answer techniques, such as effectiveness, simplicity, ease of implementation, capability of following the divergent thinking process inherent to design.

The authors believe that, through a dialogue-based system undertaking the abstraction process, a systematic succession of questions is viable to support the investigation of the problem, allowing even to highlight the least codified aspects, that otherwise would be hardly taken into consideration. The questioning procedure could advantageously foresee differentiated set of queries focusing on various facets of the problem. The logical succession can thus guide the user towards the identification of criticalities and conflicting issues and subsequently to the search of useful technical and scientific information.

Conclusions

The paper has investigated and highlighted the main requirements that a novel CAI system should fulfil in order to support inventive problem solving activities within the conceptual design stage. The findings of the present research can be thus summarized in the following major capabilities that should characterize innovative software applications:

 to face inventive, difficult, non-typical and ill-structured problems;  to overcome the dichotomy between cognitive and systematic problem solving models, in order to both exploit user"s knowledge and guide the design activity towards the solution with a step by step process;  to allow a wide description of the problem, that enlarges the potentialities of solutions from different expertise domains;  to provide comparative means among the feasible solution concepts;  to link the design process with valuable external sources of information;  to take advantage of the opportunities given by Question-Answer techniques in terms of such effectiveness, simplicity, ease of implementation;  to use natural language in order to assist users with poor scientific background about well-established problem solving methods and with the aim of assisting the designer"s thinking process.

As recalled in the Introduction, such features are taken into account in the complementary paper [START_REF] Becattini | Computer-Aided Problem Solving -Part 2: a Dialogue-Based System to Support the Analysis of Inventive Problems[END_REF], that illustrates an original algorithm proposed by the authors to aid the investigation of an engineering problem.

Fig. 1 .

 1 Fig. 1. Current domains of application of PLM and CAI systems within the product cycle and level of abstraction of the corresponding product models. The arrows represent the expected trends of evolution.

Fig. 2 .

 2 Fig. 2. Brief synthesis of the efficiency of problem solving methods: in this diagram resources of time needed to produce solution concepts are compared to the engineering value of the same concepts.

Fig. 3 .

 3 Fig. 3. Qualitative comparison between the capability of solving problems by solution concepts of great inventiveness (whose definition is available in[START_REF] Altshuller | Creativity as an Exact Science[END_REF]) together with the time required to learn and proficiently use the method itself.

Fig. 4 .

 4 Fig. 4. Problem solving process, differences among the four methods. Cognitive processes leverage tacit knowledge and, on the other hand Systematic processes exploit explicit knowledge. Nevertheless, meta-cognitive methods with prescriptive procedures leverage both type of knowledge.

Fig. 5 .

 5 Fig. 5. Flexibility of the four problem solving method briefly presented in the paper, comparedto their level of integration in computer-aided tools.


Huiling Yu

Guangsheng Chen

Congchun Xu

Delin Fan

The Defect Detection of Fibre Boards Gluing System Based on TRIZ

Keywords: Theory of inventive problem solving (TRIZ), 40 Inventive Principles, defect diagnosis, support vector data description

Defect signal is always a problem that interferes with the operation of fibre board gluing control system. Complex problem analysis and solving are an important evolutionary direction of TRIZ. This research creates a defect diagnosis methodology by synthesizing the Theory of Inventive Problem Solving (TRIZ) and a Possibilistic C-Means (PCM) improved Support Vector Data Description (SVDD) method. Applied TRIZ to identify core root causes and confirm contradictions to classify the system defect. A complex problem solving model integrating SVDD and PCM in TRIZ frame is put forward, a case shows the application of this model.

Introduction

Theory of Inventive Problem Solving (TRIZ in its Russian abbreviation) is an analytical approach to creative engineering. It has been developed in the former Soviet Union by Genrich Altshuller. TRIZ is an algorithmic methodology which helps break psychological inertia in problem solving. It also provides engineers with powerful algorithmic approaches to formulate, analyze, and solve complex engineering problems, as well as to use objective Laws of Evolution of Technological Systems for directed development of next generation products and processes. The main postulate of TRIZ is as follows: Evolution of engineering systems is not a random process, but is governed by certain objective laws. Altshuller formulated eight laws of evolution of engineering systems. These laws can be utilized for conscious engineering system development briefly described below. Altshuller analyzed about 400,000 invention descriptions from different fields of engineering. The most effective solutions were selected and examined to reveal the objective laws (trends) of evolution of engineering systems. The evaluation of the solution's effectiveness was based on the concept of engineering contradiction: A problem becomes a creative one when an attempt to improve system's parameters by conventional means leads to deterioration of other parameters generates an engineering contradiction [START_REF] Domb | QFD and TIPS/TRIZ[END_REF] [START_REF] Yamashina | Innovative product development process by integrating QFD and TRIZ[END_REF].

TRIZ was born as a problem-solving tool based on a systemic view of the technological world. The primary idea behind TRIZ approach to solving problems is that information about a specific problem must be first generalized, a solution concept has to be generated, and then the concept should be specialized in terms of a feasible solution. Traditional problem-solving methods aim to find a specific solution to a specific problem often using brainstorming or trial and error processes. In contrast, TRIZ translates the specific problem into an abstract problem or model of the problem and then to an abstract solution or model of the solution. Figure 1 depicts this strategy.

Fig. 1. TRIZ model for problem solving

TRIZ has been successfully applied in a number of industries to structure the innovation process. Mixing and supplying glue technology of particleboard is one of the key technologies on particleboard production line, although the investment of the entire section of all investment in whole line just accounted for 3% to 6%, but it makes significant influence on whole line production. In the same process condition, excessive glue application will lead to high moisture content of particleboard, which can easily result in bubbling phenomenon and increased production cost at the same time; if glue application is insufficient, the plasticity of particle will be small, the bending strength and plane tensile strength of particleboard will decrease with the reduction of glue application, which lead to the decline in the quality or substandard products.

Gluing is one of the key steps in the manufacture of fibre board, and gluing control system directly affects product quality and costs. Improper proportion of the glue will lead to the degradation of the physical properties of fibre boards. In the gluing procedure, the quantity of glue should be kept with the change of fibre, and previous researches have proposed a series of optimal algorithms to control the non-linear model of gluing system, but the defect signals, which always interferes the fibreboard gluing control system, is ignored. Existing innovation methods in control field are mainly genetic algorithm or fuzzy-PID. In this paper, a systematic approach to build a method integrated TRIZ and SVDD for gluing system control strategies was proposed.

2

Background of TRIZ

Theory of Inventive Problem Solving (TRIZ in its Russian abbreviation) has been developed in the former Soviet Union by Genrich Altshuller, starting in the fifties.

The main postulate of TRIZ is as follows: Evolution of engineering systems is not a random process, but is governed by certain objective laws. Altshuller analyzed about 400,000 invention descriptions from different fields of engineering.

The difference between control strategy and numerous patents on which TRIZ was based makes some inventive principles, universal parameters and general solutions in TRIZ hard to get correspondences in control theories directly. Applying TRIZ into an integrated control system should include some basic parts, for example, laws and routes of control strategies evolution, levels of invention of control strategies, contradictions and their types in control strategy design problems.

2.1

The Ideal Design with No Harmful Functions

Technical System Describe

Gluing is the key process for the particleboard production, the adhesive which distributed evenly in the debris on the surface will have a direct impact on the quality of the finished product. We considered the factors impacting on the particleboard quality in the process of system analysis and got the sub-system and the super-system. We defined the Operational Zone(OZ) and the Operational Time(OT) then using the three-dimension analysis to get the Route Course(RC). To control the flow of the glue and the rotate speed of the pump according to the weight of the fibre.

Definition of Glue Dosing System

A system can be thought of as -a group of interacting, interrelated, and interdependent components that form a complex and united whole. Systems are almost always defined with respect to a specific purpose within a larger system. The system of supplying glue section in particleboard production line is composed of a glue storage tank, measuring devices, glue pumps, pipes and other components, divided into surface and core layer applying glue, with the same process and the symmetry structure. To solve the existed problem, the following supersystem in which the system resides were considered, such as air, the shape of flake, the pipelines and the density of the flake etc.

Functional Analysis of the Glue Dosing System

When analyzing this case, the first step was to describe the components in and around the system; the second detailing the useful relationships between each pair of components; the third then describing the negative (harmful, insufficient, missing or excessive) function present in the system. The primary useful function of the glue dosing system is to measure flow accurately. Through the proportioned glue is stored in glue storage tank in accordance with the timing, supplying glue process is that shavings is sprayed by the corresponding glue mixture which is pumped from the two glue storage tank separately according to the weight of surface particle (thin stuff)and the weight of core layer and particle weight (coarse material), the particles is transported by Electronic belt scale, particle flow is measured precisely by rotary encoder, and glue content is measured by electromagnetic flow transmitter and load sensors ,and then supplying glue steadily is realized though the method that using frequency converter to control the supplying glue pump.

Define the System Boundaries and Environment

TRIZ is a scientific methodology for defining, analyzing and formulating solutions to difficult problems usually of a technical nature. This research includes other systems nearby the supersystem: forming machine, hot press and sanding machine.

Conditions Around the System and Its Supersystem

Mixing and supplying glue system is a complex system with the characteristics that great time-delay, great inertia, multiple disturbance、nonlinear and time variation, and these characteristics make supplying control more difficult.

Define Available Resources

Since glue cost is high and the supplying glue control has a direct impact on hot pressing results of particleboard, the large time delay characteristic of supplying glue system affects product quality seriously. In addition, as the factors that electromagnetic interference, pressure variation inside and outside the pipe、mutations of shavings flow, failure shutdown etc exist in the control system during the supplying glue process, the traditional PID controller is difficult to achieve the required control quality of the system and manual intervention is often required.

Resources refer to anything in or around a system which is not being used to its maximum potential. TRIZ looks for resources of innovation in the system itself not in the individuals or the organization surrounding it, so we can manage and guide the processes of value maximizing by problem solving. Any physical object with mass or that occupies space and that potentially can be used to improve a system calls substances. Includes Substance Resources: glue, flake, air etc, Space Resources: the length of the pipe, Time Resources: the time of flowing through the pipe, Information Resources: the flake's shape and density; Fields is defined as the energy needed for the interaction of substances within a system that can potentially be used to improve the system. Field resources: Chemical Field, electrical Field, mechanical Field.

The Contradiction in Technical System as a Basis of Innovation

The evaluation of the solutions' effectiveness was based on the concept of engineer contradiction: A problem becomes a creative one when an attempt to improve system's parameters by conventional means leads to deterioration of other parameters.

In control system, requirements to rise time and accuracy usually can't be satisfied simultaneously with a single control strategy. It would compose a technical contradiction and physical contradiction. From the standpoint of TRIZ, solving a problem means overcoming this contradiction, or satisfying all conflicting requirements. 40 inventive principles (classes) of TRIZ are one of the less widely used innovation tools. Its have been the basis of the study for innovations in mechanism system. TRIZ researchers have configured a tool called a Contradiction Matrix. The contradiction table is a 39╳39 matrix of all the characteristics on the rows and columns. To use it, first look up the character you wish to improve and go to its appropriate row. Next, locate the column of the characteristic that degrades as a result. Where the row and column intersect are numbers that correspond to the 40 inventive principles. The next step is to identify the standard and/or proposed contradictions in the case. In the supplying glue system, we found a main contradiction for the Macro-level and Micro-level that we wish to improve from the row 26 amount of substance, thus the undesired result --the characteristic that degrades as a result of improving the above feature, the column 36 device complexity. From the standpoint of TRIZ, solving a problem means overcoming this contradiction, or satisfying all conflicting requirements. Based on the matrix they would obtain the solutions as 3 Local Conditions principle, 13 Inversion principle, 27 Disposable Object principle, 10 Preliminary Action principle. With the sequence given in the descending order of usage.

Among the four recommended principles, number 10 (preliminary action) provided feasible ideas to realize our IFR(Ideal Final Result, the evolutionary end-point for a system). Problems that contain contradictions or conflicts can be solved one of two ways: either by making tradeoffs or by stating the objectives in the form of a contradiction and then solving the contradiction. Usually contradictions are resolved though trade-offs by finding some compromise between the two requirements. This results in a solution that does not fully satisfy either element. TRIZ, on the other hand, does not attempt to make trade-offs between two requirements in features or functions. Ideality is typically defined as benefits divides by cost and harm.

The last step in the analysis is to identify the principles used in solving the problem and how they were used. Principle 10 preliminary action--Arrange objects so they can go into action in a timely matter and from a convenient position, place each part of the object under conditions most favorable for its operation. Perform before it is needed.

Increasing system dynamism and controllability allows functions to be performed with greater flexibility or variety. We apply the cascade control system design based on predictor in advance. The Smith Predictor will be employed to the control system. Most of the principles suggested from the matrix match the concepts used in the actual solution of the problem.

The paper also considered solving the problem using a substance resource and field. Substance-Field analysis is a TRIZ analytical tool for building functional models for problems related to existing or new technological systems. Substances may be materials, tools, parts, people or environments. Fields may be mechanical, thermal, chemical, electrical or magnetic. 5 classes of 76 standard solutions are offered. According to su-field concept, model of the problem can be represented by S 1 (pump). To control S 1 , a pair F-S 2 should be introduced into the system. S 2 is to transform F into mechanical field (force) which would act on S 1 .Due to ineffectiveness of mechanical F, it was natural to suggest transition to microlevel which can be realized by use of control using a predictor.

3

The Detailed Steps of Using Multi-classifier by SVDD

An Overviews on SVDD

SVDD was developed to solve one-class classification problems, and its main idea is to map data points by means of a nonlinear transformation to a high-dimensional feature space. In the feature space it first find the smallest sphere that contains most of the mapped data points in the feature space, and then mapped back to the data space. In the way above the data can be separated into several components, each enclosing a separate cluster of points [10] [11] [12]. SVDD obtains a boundary around the target data set, and this boundary is used to decide whether new objects are target objects or outliers.

The simplest idea of SVDD defines a hyper sphere around the data and the goal is to minimize the volume of the sphere. The minimum enclosing sphere S is characterized by its center a and radius R which keeps all the training objects inside its boundary.

The structural error of SVDD is as follows:

2 ( , ) F R a R  (1)
Subject to

2 2 i x a R 
(2) To allow for the possibility of some samples falling outside of the sphere, we can relax the constraints by introducing slack variables i  , and then the minimization problem can be turned into (3):

2 ( , ) i F R a R c    (3) Subject to 2 2 ii x a R     , 0 i   ( 4
)
Where a is the center of the minimum enclosing sphere S, i  are slack variables allowing for soft boundaries, the parameter c controls the trade-off between errors and the volume of the description. To solve this problem, the Lagrange multipliers are introduced:

2 2 2 ( , , , ) ( ) i i i i i i i i L R a R c R x a                  (5)
By Using the Lagrange multiplier method, the above constrained quadratic optimization problem can be formulated as the Wolfe dual form. To solve the partial derivatives of L about R , a and i  we set their derivative value to zero, and then get (6) (7) (8):

2 (1 ) 0 1 ii L R R          (6) 2 ( ) 0 i i i i L x a a x a          (7) 0 i i i i i L cc               (8)
Taking ( 6)-( 8) into ( 5), we obtain the dual formulation ( ) ( )

i i i i j i j L x x x x      (9) Subject to 0 i c   , 1 i   
The maximization of (9) gives a set {} i  . Objects i x with i  are called the support vectors (SVs) of the description. [START_REF] Mann | Axiomatic design and TRIZ: compatibilities and contradictions[END_REF] shows that the center a of the sphere S is the linear combination of the support vectors.

To decide whether an object x belongs to the target, the distance from the object x to the center of the sphere has to be calculated. The object

x is accepted as a target when this distance is smaller than or equal to the radius; otherwise it is beyond the target. Hence, the following condition has to be verified.

R is the distance from the center of the sphere a to any support vector i x on the boundary. It is worth noting that all  mappings used in the SV method occur in the form of inner products. This allows us to define a kernel function K, and the nonlinear SVDD can be constructed using only the kernel (10), without needing to know the mapping  explicitly.

( ) ( ( ) ( )) i j i j K x x x x     (10)

Constructing Multi-classifier Based on SVDD

We can adopt one-against-all method to construct multi-classifier algorithm

{( , ) } k i i i D x y y k  .
For each subset k D , an SVDD is trained by using k D as the target class and the remaining subsets as negative samples. This leads to c optimal hyper spheres, with each center denoted as i a . Using one-against-all method, the label of an input sample is simply predicted as the class where its nearest-neighbor center belongs to, which is given as 1,2,...

( ) arg min ( )

zi ic y x x a    (11)

Possibilistic C-Means Algorithm

The idea of PCM algorithm was originally proposed to overcome the relative membership problem for fuzzy c-means algorithm, and the algorithm has shown the excellent ability to deal with noises and outliers samples. Its basic idea is to relax the constraint and propose a possibilistic method for clustering by minimizing the following object function:

2 1 1 1 1 ( , ) (1 )
c n c n mm ik k i i ik i k i k J U V u x v u             ( 12
)
where c is the number of clusters, n is the number of data points, ik u is the membership of k x in class i , m is the quantity controlling clustering fuzziness, v is the set of cluster centers, and i  are suitable positive numbers. First we demand that the distances from data points to the prototypes be as low as possible, and then we force the ik u to be as large as possible, thus avoiding trivial solution. We can use kernel technique to generate kernel PCM.

5

Example for the Application of the Proposed Method

5.1

The Defect Problem of Fibre Board gluing system

The structure of fibre board gluing system is shown in figure3, which is made up of 3 parts, fibre supply device, glue supply device, and blender. In the gluing system, there are usually 6 types of defects. The types are the gluing system is out of control, glue bump does not work well, the glue supply pipeline is blocked, the fibre supply screw is malfunctioned, the blender is blocked, and the flux control of fibre supply does not work normally. In order to map problems to the solution domain, we define the 6 defects as 1 2 3 4 5 6 , , , , , R R R R R R respectively, and each symbol matches one type of defect. The symbols and the types of defects are shown in Table 2. Now, TRIZ is used to deal with the above mentioned problem. Accordingly, rich information and insight for idea generation can be achieved by TRIZ technique. When engineers have sufficient training, experience, and practice, they will exploit the potential of TRIZ and search for feasible solutions effectively based on the knowledge base of TRIZ. From some workers with experiences with gluing system, we conclude some parameters that can be measured by some sensors. When the defect happens, these parameters change, so we employ these parameters to identify the defect signals.

Actually, there are 5 parameters expressing the defect types, they are the glue quantity in the storage which is measured by weight senor, the glue flux in the pipeline measured by E+H, the fibre quantity detected by weight sensor, and the rotation speed of glue bump and the blender respectively detected by knotmeter. We define the measured data identifying the defect types as 1 F , 2 F , 3 F , 4 F , 5 F . Table 3 gives the matching relationship between the symbol and physical data measured by different sensors. ( , ) exp( / )

i j i j K x x x x    
. When we use the Gaussian function as the kernel function, we get

( , ) 1 K x x  and 2 ( ) ( ) 2 2 ( , ) k i k i x v K x v    
. Table 4 shows the defect diagnosis accuracy rate comparison of fibre gluing system between original SVDD multi-classifier and the improving SVDD multi-classifier by using PCM. We can see, by using our proposed new algorithm, the performance of the resulting classifier is improved significantly, and the accuracy is up to 97% which is much better than that of the original SVDD multi-classifier. We can conclude that PCM help SVDD multi-classifier overcome the negative effects of the outliers and noise, and the robustness and accuracy improved. Table 5 shows some defect diagnosis results, which is consistent with the actual status. When we get the physical data from the sensors respectively, and then input them into the PCM improved SVDD method, we can get the status of fibre gluing control system. The input section is the physical data which has 5 parameters, and the output section is the status of the fibre gluing system and has 6 statuses which include 5 defect statuses plus 1 normal. Because the defect diagnosis serving in the fibre gluing system gives signals to the control system, the stability of the control system based TRIZ is enhanced, and the production quality is improved.

Conclusions

This paper describes work to integrate SVDD and TRIZ techniques to produce a method combining the best features of both. The paper also describes the research on the defect diagnosis method of the fibreboard gluing control system. Since TRIZ can expedite design process and help designer solve problems in a creative mode, the paper analyzed the supplying glue system problem, formulated the mini-problem and defined the conflicting elements, got the inventive principle to solve the contradict problem.

Fig. 2 .

 2 Fig. 2. Particleboard supplying glue system

1 {

 1 [13]. Given a set of l labeled training samples according to their output classes, each given as

Fig. 3 .

 3 Fig. 3. The structure of fibre board gluing system

  10.Lee, D., and Lee, J.: Domain described support vector classifier for multi-classification problems, Pattern Recognition, vol. 40, pp. 41-51.(2007) 11.Hu, Q., He, Z., Zhang, Z., and Zi, Y: Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble, Mechanical Systems and Signal Processing, vol.21, pp. 688-705.(2007) 12.Pawel Podsiadlo, and Gwidon W. Stachowiak:Fast classification of engineering surfaces without surface parameters, Tribology International, vol. 39, pp. 1624-1633.( 2006)

Table 1 .

 1 Contradiction matrix table

			Characteristic that is getting worse
	getting improved	Characteristic that is	Engineering Parameters 23 Loss of Substance 24 Loss of Information 25 Waste of Time 26 Amount of Substance	35 Adaptability or Versatility	36 Device Complexity 3,13 27,10	37 Difficulty of Detecting
			27 Reliability
			28 Accuracyof Measurement

Table 2 .

 2 The types of defect

	symbol	The type of defect
	R1	gluing control system
	R2	Glue bump
	R3	Block of glue supply pipeline
	R4	The screw of fibre supply
	R5	Block of the blender
	R6	Flux control of fibre supply

Table 3 .

 3 Physical data recognizing the defect types

	symbol	Physical data	Senor type
	F1	The glue quantity in storage	Weight sensor A
	F2	The flux in glue pipeline	E+H
	F3	The fibre quantity	Weight sensor B
	F4	Rotating speed of glue bump	knotmeter
	F5	Rotating speed of the blender	knotmeter
	5.2

Apply the SVDD Method to Find Solutions From

  actual production, we collect 300 data and divide them into 2 groups, the first group containing 150 data is used as training examples, and the other one is used as testing examples. We adopt the Gaussian function as a kernel function in PCM algorithms for improving SVDD, which is

	2
	2

Table 4 .

 4 The accuracy comparison of the 2 multi-classifier

	Method	Accuracy rate
	Original SVDD	82%
	PCM based SVDD	97%

Table 5 .

 5 Status analysis of the gluing control system

			Inputs			output
	F1	F2	F3	F4	F5
	0.60	0.75	0.67	0.48	0.5	normal
	0.66	0.75	0.22	0.78	0.5	R6
	0.67	0.90	0.85	0.48	0.5	R2
	0.65	0.74	0.64	0.48	0.5	normal

Acknowledgment

This topic of research is supported by the Basic National Project Foundation of China (Program No. 2008IM030400).