PDF | On Jan 7, , Asterios Agkathidis and others published Generative Design. This dissertation argues for one main point: integrating Generative Design as a new stage in the [25/04/]. Using generative design, Airbus created a new cabin partition for its. A plane. Designed by mimicking natural growth processes, the partition is stronger than.

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This paper describes a flexible workflow for generative de- sign applied to architectural space planning. We describe this workflow through an application for the. A practical generative design method Key words: generative design, parametric design, evolutionary design, computer aided conceptual design Abstract A. pdf. Generative Design: Form Finding Techniques in Architecture. 10 Pages Generative Published in by Laurence King Publishing Ltd – city road.

This is currently accomplished by the process of sketching. It helps trigger creative thought processes in exploring emergent concepts[11]. This is why designers keep sketching as it both stimulates the creative process with emergent concepts and helps them to refine the concepts. Production of design ideas depend heavily on this interaction with conceptual sketches.

This interaction is central to emergence[12]. It is precisely the emergent quality of conceptual design and its reliance on visual reasoning facilitated by sketching that is not supported by CAD. Such systems have inadvertently enforced fixation so that it is not surprising that they are not used in early stages of architectural design. Often, this exploration is directed by the outcomes of previous explorations[12]. This emergent process appears to be a chaotic work process. Sketching seems to be the preferred process by which the designer navigates the solution space[11].

CAD in its current form is unable to support this process. Hence, any proposal that is made for supporting early stage creative design should be able to operate in such chaotic conditions.

Out of them, the centrality of the human designer in driving the design process, the non-procedural and emergent nature of the process and the inherent vagueness, incompleteness and ambiguity of early stage design need to be considered in developing CAD based conceptual design processes. Many previous proposals have been procedural and mechanistic in their structure and have shown little understanding of the emergent nature of the process.

Many CAD systems now, e. Sketcup, Alias recognize the need for vagueness, which they somewhat allude to by imitating hand drawn sketch lines. Though it is a superficial gesture, it is a recognition and attempt at introducing the associated visual qualities of incompleteness which is an essential part of conceptual design.

Be flexible in allowing the designers to navigate the design space in the way they see fit. Be structured as an assistive tool, giving the designer the choice to either use it or not use it.

Support and enable emergence in ways that stimulate the creativity of the designer. Enable an efficient transition of design content in to a detailed design phase. The workflow should be driven by the designers in the order they see fit, depending on the type of the design problem and should support their own highly developed design method.

In short, it should not have a pre-structured workflow. It should not impose on the designer an externally conceived framework for design. Instead, it should support emergence which provides a fertile source of inspiration towards further exploration, similar in quality to the creative stimulation that designers derive out of hand sketching.

New understanding will inevitably require the continuous modification of concepts, evaluation criteria and constraints throughout the design development process. The proposition is also practical rather than theoretical in that, it needs to be designed with practical considerations in mind. It is to be implemented with minimum overheads on existing processes. In addition it needs to work seamlessly throughout the entire design development cycle, from conceptual to the final stages of design.

The chosen concepts should be easily transferable to the detailed design phase. It should be able to function without complete information about the design problem, which is often the case in early stage design; where the design problem itself is under formulation.

In supporting the creative work process of designers it should enable designers to work on limited aspects of the design. It should allow the designer to explore selective regions of the design space at various levels of detail. The remainder of the paper is organized as follows: In the following section we present the theoretical frame works for the proposed generative design method along with methods of implementation.

Details of the steps are provided. We then compare the proposed method with the genetic algorithm based method in the generation of a coffee table design. We explore other applications and conclude with a discussion on further research.

Related works The theoretical frame work of the proposed method draws heavily on previous research on constraint driven parametric search and genetic, evolutionary algorithms. A compilation of Genetic algorithm based methods in CAD is given in [15].

Genetic Algorithms require the maintenance and breeding of a population of designs, which are evaluated by fitness function. The proposed method does neither and therefore is distinctively different from Genetic Algorithms. In the proposed method, the evolution of the design is driven entirely by the designer, who by constantly adding design details, constraints and evaluation criteria, modifies both the design representation and the search space until valid designs are found.

In GDM, the genotypes are CAD models and the phenotypes are instances of it, thus the mapping between the two is direct. The other key differentiation is the intent. The proposed method is designed to search for a multiplicity of viable solutions that are different to each other; for the designer to select, instead of a selection of a single optimal solution.

Despite these differences, GDM shares with Genetic Algorithms the concept of a genetic model, performance space, mutation and selection criteria. Genetic Algorithms that rely on numerical optimization are fundamentally unsuitable for design problems that require complex evaluation criteria.

Its development has gone through various phases, led by mainly academic researchers focused largely on design theory. Though the lack of methods of implementation was recognized [18], formal methods for generative design were not forthcoming. With the growing interest of practitioners and schools of architecture, this gap was filled by CAD companies[], [22] offering various generative design solutions.

The need [23] , [18] for a guiding theoretical frame work for generative design was now felt by leading researchers. In this context, a particular form of generative design - the Generative Design Method GDM is defined here as - a designer driven, parametrically constrained design exploration process, operating on top of history based parametric CAD systems that are unstructured in terms of design development and supportive of emergence.

We do not make this distinction in this paper as contemporary CAD systems use internal procedures to construct geometry, though the procedural aspects are hidden from the user. Generative design requires the following components: A design schema 2. A means of creating variations 3. The overall scheme of a generative system operating on procedures is given by Bohnack et al. A modification has been made to his diagram Fig.

It is important to note here, the central role of the designer in continuously modifying the generative scheme based on the resultant outcomes; by which the solutions space is navigated in search for viable design solutions. Generative Design Process 2. The closest amongst them are discussed here. It treats the design process as an interlaced combination of constraint optimization, modeling and optimizing the scheme for searching and evaluating designs.

In short, it is an interactive design development process driven entirely by the designer. The designer interacts specifically with 1 variables, 2 constraints, 3 objective functions and 4 search strategy.

It strategically separates the design tasks into quantitative and qualitative tasks. It relies on Genetic Algorithms for generating design solutions. Caldas[29] demonstrates how building performances can be greatly increased by combining parametric generative schemes and building simulation software to evaluate thermal performance.

He uses fitness functions and Parento Genetic Algorithms to optimize the chosen multi-criteria design problem. The generative scheme seems to interface directly with CAD systems to create the variations required for thermal and lighting analysis. His intention is to thoroughly explore the design space and make it apparent to the designer. It is also a designer driven process and does not rely totally on quantitative criteria.

It is suitable for form finding problems in structural design where the design space is searched through the use of two layers. An outer layer representing the topology connection patterns and the inner layer representing the geometry of the structure.

Both layers are algorithmically searched. IGDT is designed to dynamically adapt to evolving design criteria and to assist designers in the early conceptual design phase. It evolves shapes from random blobs. A phenotype is first specified based on the design space and the genotype is specified based on the solution space. A suitable evolutionary algorithm is then chosen and the fitness function is defined.

Multi-objective genetic algorithms are then used to evolve the solutions. This is a classic application of evolutionary algorithms for form design. The generated results are compared in section 4. However, the sophisticated geometry and constraint modeling capability of modern CAD systems seem to have subsumed [31] t, he original intent of Shape Grammar.

Despite being developed more than 30 years ago, its adaptation by industry is limited due to various reasons. This is a highly structured but interactive process where design generation is carried out by grammatical design transformations. An L-system based generative grammatical encoding has been developed by Hornby [33] who is able to demonstrate that the generative encoding of the genetic model is able to create significantly fitter solutions than non-generative encoding.

He is able to demonstrate this in the design of a table. A neural network based approach has been proposed [34], to enable the system to learn the preference of the designer in selecting designs.

If this method can be implemented across a range of design problems, it will enable to reduce the cognitive load in the selection process. The search aspect of the proposed method is closer in many ways to what is known as a morphogenetic approach [35], which focuses on the dynamics of growth. The GDM is composed of six key components: Genotype — is composed of a generic parametric CAD model, list of design parameters and their initial value and initial exploration envelope.

Phenotype — generated CAD files that may include build history, built-in relationships and built- in equations. Exploration envelope — a list of minimum and maximum values of the driving parameters specifying the limits of the design space to be explored. Design Table - a data table that stores the driving design parameters, their initial values and the limits and other data that may be required and the generated design values preferable in an accessible spreadsheet format.

Design Generation Macro — a macro or a spreadsheet function that operates on the design table. It generates random variations of the driving parameters within limits set by the initial design envelope. CAD system - is a parametric CAD engine with a transparent and editable build history, preferably with a 3D geometric kernel with capabilities to manage geometric relationships, engineering equations and connect to external design tables.

We now briefly describe how these components are connected Fig. Creating the genetic model 2. Setting the initial envelope 3. Generating designs 4.

Filtering phenotypes 5. The driving dimensions are then set with an initial value using the native dimensioning system of the particular CAD package and stored in the design table Table. The maximum and minimum range of these values are then set individually or as a percentage value to limit the search within an exploration envelope. The genotype here represents the design space and the limits of search in a format that is operable in CAD.

The Design Table Macro then generates random values within the exploration envelope. The CAD system then generates new instances of the designs based on these values.

The generated designs are referred to as phenotypes. Performance filters are then used to judge the viability of these phenotypes. The phenotypes that pass through these filters are then considered viable designs.

The filters draw values that are directly related to the design parameters such as distances, which may be drawn from the table and values such as volume and weight may be drawn from the CAD package. If the CAD system posseses a geometric kernel that is able to detect build- failure then it may be used as a geometric filter. Proximity filter may be used to filter out designs that are similar to each other to ensure that the generated designs are somewhat dissimilar. These steps are described in greater detail Section 3.

All designs generated may be saved and retrieved for comparison or design refinement depending on the work process preferred by the designer. GDM also allows the designer to explore design possibilities interactively around a generated design. This is accomplished by setting the generated parametric values of the phenotype as a new genotype and by re-setting the initial envelope to cover a smaller region of interest Fig 3.

Representation of solution space. The performance envelopes represent the parametric limits of the design that satisfy specific requirement. The viable design space is then the intersection of various performance envelopes. In generative design, designers are faced with the problem of selecting amongst thousands of designs.

This places a significant cognitive burden on the designer.

The designer is able to assess only a limited number of design solutions without cognitive fatigue [16].

Hence, the designs presented to the designer for assessment have to be limited in number and widely dispersed with the viable design space. Each instance may be taken to represent a region of design possibilities as shown Fig. Such an approach makes it possible for human designers to explore large regions of design space based on a limited number of design instances. GDM allows for the continuous evolution of the solution space.

Since the parametric representation of design makes the design space easily navigatable, accuracy of the envelopes representing the performance limits becomes less of an issue in the early stages of exploration, where the focus is mainly on the identification of viable regions.

Generative Design / Topology Optimization

Once the final design is chosen, the region around it can be examined in finer detail and the limiting performance envelopes can be defined with greater certainty 3. The proposed arrangement affords great flexibility. It allows the designer to change the CAD model, the genotype values and the filters at any stage of the design process. It also allows the designer to apply GDM methods to selected features of the design.

It is this feature that enables it to support conceptual designs where the genotype and performance criteria are still under evolution. Performance r ……. Filters s Phenotypes Fig. Generative Design Method — the overall scheme.

The genetic model needs to engender a rich set of design possibilities as it represents the design space. The genetic model should not only capture the common generic geometry of the desired designs but also the common underlying patterns behind the geometry. Nature provides many great examples as to how geometric variations can be created while maintaining an underlying structure. While there are about , beetles that appear to be very different from each other, they all share a pattern of relationships that is common and constant.

A well structured genetic model will be able to represent a much wider range of design variations than a poorly structured model. The genetic model needs to be robust and hold its geometric logic while being subjected to significant and unpredictable random variations during the generative stages of the design.

An example of a family of designs generated [39] out of a single genetic model is shown Fig. The underlyingbase geometry needs to be structured early on or higher up in the design tree and all the less important features at the latter stages or lower down in the design tree. Since most CAD systems construct the geometry sequentially, such an arrangement would prevent non- critical failures of the less important aspects of the design, invalidating the entire design. Developing the genetic model is a design exercise in itself and it would require quite a few iterations.

Janssen identifies some of its key attributes. The genetic model is also an embodiment of knowledge of the design problem and solutions to it. Structuring genotypes with performance objectives is a way of embedding design knowledge into the genotype itself.

The genetic model may contain some embedded requirements. By embedding such requirements into the genetic model, we can be assured that these requirements are met in all generated solutions. But if there are too many of such built-in requirements, we face a real danger of creating an over constrained generative model and a reduced search space. In setting up the initial parameter values, the commonest form of the design needs to be considered.

To achieve this, the start state of a genetic model genotype should be somewhat in the center of the design space representing the most common design. This can be achieved in cases where the commonest form is known; e. In other words the designer should avoid extreme representations when creating the initial instance of the genotype.

These limits need to be set with approximate values as their main purpose is to prevent the waste of computation energy in exploring unviable regions of the design space.

Detailed constraint envelopes will further limit the exploration space but in regions that it does not, the initial envelope can be set to act as the default constraint envelope. The initial envelope can also be set as a n- dimensional envelope where n is the number of parameters. But for now, it is set as an independent, single dimensional, minimum and maximum value. The same can be accomplished by embedded Macros, which we refer to as the Design Generation Macro.

Generated designs that are too close to previously generated designs can be discarded using the proximity filter Fig. A better approach would be to assess the generated designs, to ensure that they are spaced with sufficient distance from each other in the performance space. A measure of geometric differences could, for example be used to ensure visual differentiation of generated designs.

But such strategies will require the generation of a large number of designs before they can be assessed, making it computationally costly.

Initially, it is sufficient to set the constraint envelope with reasonable accuracy, primarily as a bounding region to set the limits of design exploration. Once the candidate solutions are identified, the constraints in these regions can be reset with much greater accuracy. GDM allows for the adding, deleting and modification of constrains in the form of filters throughout the design development. Most parametric CAD packages have built-in engineering functions and connect seamlessly to a slew of analytical packages that may now be used, to assess various performance aspects of the design.

The comprehensive mapping of the constraint envelopes for the initial design space may be computationally costly, but it will speed up the evaluation process.

Though it is possible to pre-compile these envelops, it is not recommended; mainly because the genetic model undergoes significant development during the design process.

The order of assessment may be determined by computational cost and work process issues that are discussed Section 3.

Once the high potential designs are identified, the regions around them can be explored in much finer detail. In exploring design space, the designer faces the same set of problems faced by a geographer in mapping new and uncharted territory, looking for minerals that are found in only regions with particular combination of geological characteristics. Many parts of the unmapped territory would represent continuous stretches of unremarkable designs, lacking in value or novelty.

There may be regions where interesting things begin to happen. The exploration of performance space requires a similar approach. The designer will develop a mental map of the design solutions space and be able to move from one design to another Fig. Exploring the design space Performance may become erratic in certain regions. These could well be high potential regions as performances rise or drop dramatically. Then, beyond a point the designs would hit the edges of the constraint envelopes.

Generative urban design with cellular automata and agent based modelling

These regions are also interesting, as they could be regions where performance could not be further increased due to the limitations imposed by constraints. By negotiating these constraints, the designer may seek to achieve novelty or increase its performance.

If the designs are to be evaluated visually, they may be rendered realistically or be rendered real time in 3D. Once the designs are chosen, they can be fine tuned easily due to their parametric nature.

Variations in texture and color may also be explored at this stage. Current CAD systems are mostly history based. Most modern CAD packages with geometric kernels have abilities to set up geometric relationships within the part file. They also have equation editors that allow designers to create equation driven relationship between dimensions. These two features can be used with great advantage in GDM.

Though many CAD packages have means of storing data internally, an external data storage approach was preferred for reasons of transparency and for easy connectivity to other analytical packages.

Current CAD systems are mostly history based. Most modern CAD packages with geometric kernels have abilities to set up geometric relationships within the part file. They also have equation editors that allow designers to create equation driven relationship between dimensions. These two features can be used with great advantage in GDM.

Though many CAD packages have means of storing data internally, an external data storage approach was preferred for reasons of transparency and for easy connectivity to other analytical packages. The design table is essentially a spreadsheet with basic spread sheet capabilities which include functionalities to generate random numbers and abilities to work with internal or external macros.

Alternatively, a data file can be used with a program that can achieve the same results. Three ways of implementing GDM is discussed here. The designs are then generated using a simple function that creates a random value between two numbers. The table is may be structured as shown on Table. The generated values in this design table are then read by the CAD program to create the generated instances of the design.

Additional filters can be implemented within the XL table using simple XL functions to filter out delete the designs that fail the set criteria. The advantage of this approach is that the same macro can be used for various generative design projects.

They can also write the data in formats required by other analytical packages. Filters too can be structured as macros. One advantage of this approach is the separation of the Generative scheme from the CAD package. This will enable the building of common data structures that can be shared across CAD platforms.

This approach has significant advantages in terms of ease of use. It greatly reduces the steps involved in setting up the generative scheme and in navigating the design space directly from a CAD environment that the designer is familiar with.

It is able to operate directly within the CAD environment Fig. The external data table is used here purely for storage of chosen designs. An internal data storage is used to save generated data. These maximum and minimum imum va values may be modified later if necessary. A software proximity proximit filter may be applied here to asses if the he generated gener parameters are beyond a threshold d distance distanc of values generated previously to avoidid similar designs being generated.

If it is within a certain c Euclidean distance the design is re-gener generated. This is considered a geometric etric viability via filter that is only possible is CAD systems stems with w geometric kernel capable of detecting ng unviable unviab geometries. If it fails to re-generate the desigdesign, it generates a fresh instance of the he design Go to step 3. This his album albu may then be recalled to narrow down theth selection by comparing the generated designs against each other.

This process is executed entirely ntirely fro from within the CAD environment. At anytime time during dur this process, the designer may alter the design, ign, add or delete new design parameters, modify fy the exploration ex envelope or the threshold values that control ontrol the diversity between the generated designs.

Such an arrangement not only provides complete flexibility xibility but b it is almost identical to the normal CAD based design environment used by designers. Some off the unique un implemental aspects of this implementation entation is covered by US Patent 7,, [44]. Selection by comparison 3. In such cases the computational costs becomes an important consideration. The computational time involved for generating designs and filtering through various filters can be estimated as thus.

If m is the average model generation time and fn is the percentage of designs passing through filter n and ftn is the time it takes for the filter to evaluate the solutions. If g is the number of genotypes generated then, the computational time Tn at the nth filter can be evaluated as: The model rebuilding time depends very much in the use of the geometric kernel. In GDM the designer has the choice of setting requirements as built in equations that can be embedded into the generative scheme, or as filters or as part of the evaluation criteria.

From a computation point of view, the equation would be the more efficient as it would not waste computational resources required in creating and testing a range of unviable solutions. In essence, equation filters and evaluation criteria are all used for the same purpose — to prune the solution space; but their computational costs differ. Thus, the efficiency of the generative scheme depends on the strategies used in pruning the search space. Individual filters are used to remove unviable designs based on singular criteria.

They are best used to represent evaluation conditions that are not easily analytically derivable. Evaluation is best used to represent multi criteria selection processes. Filters too can be computationally costly; hence it is best to eliminate the unviable designs as early as possible. This also means that less permissive filters should be placed first to reduce the number of designs passed on for further filtering.

More examples of GDM generated designs are also shown Fig. A further example Fig. It also illustrates the filtering process 4. Industrial designers usually make foam models they initially with low levels of detail. The same process can be followed in GDM. The action of the internal proximity filter may be observed from the geometric geometr diversity of the generated designs.

Generated base forms In most CAD systems, the hide func function and history function can be used to control the geometric build. This allows the designers to switch from high detail to low detail models or to any p point in the earlier build history and also to switch itch off selected details in order to focus their attention ttention on a limited aspects of the design.

Generation of Design Details Once a collection of initial base forms are selected, the designer may progressively add details Fig. The base form may be kept constant if needed. The designer may at any point modify the base form and the switches will modify according to the relationship that have been embedded in the model.

If a certain geometric form is to be maintained its exploration envelope value can be set to a singular value to preserve its current geometry in subsequent rounds of generations. GDM allows designers to explore a wide range of designs as shown Fig. These designs were generated by a trained industrial designer[39] with some experience in generative design.

The designer has the freedom to override any of the generated parameters by directly modifying the CAD model. This was done in the illustrated example at points where the designer has a high level of certainty as to the desired nature of the outcome. This approach is normally taken at the fine tuning stage of the design process.

In the design of the MP3 player, we assume that it is driven here by the PCB dimensions, which can rarely be considered the key driving dimensions. By dimensioning it as such, the geometry of the shell will vary according to the PCB geometry and a certain minimum clearance between the PCB and shell can be ensured. This is a simple example of using the dimensioning capability of CAD systems to maintain the geometric logic of the design during random generation.

This is only possible if the CAD system posseses a geometric kernel that can manage the geometric logic of the design. The designer can exploit the geometric intelligence of the kernel to maintain proper relationships between the components purely by dimensioning.

If the designer wishes to maintain a constant LCD area, the equation facility within the CAD system can be used to drive the other parameters example to ensure the given LCD area is maintained this is not shown in this example. Similar equations can be used for example in the generation of bottle designs to keep the volume of the bottle constant. Though many architectural magazines display parametric design variations using various generative schemes.

Details of execution are rarely provided. Though there are some experimental applications in product design, currently there are no known mature examples in product design that we can use for comparison. This is yet to be done. Modeling skills however take longer to master. Some examples of student projects are available [45]. Examples shown in Fig. Examples however are provided Fig Generated bottles Generated thumb drives Generated spoons Generated watches Fig.

Using this is method metho he demonstrates the form design of 16 different rent des design examples; tables, set of steps, heat sinks, optical opt prisms, train fronts, boat bows, boat hulls, lls, saloon saloo cars, sports cars and floor planning forr a hospital hospi building. Out Ou of this, the coffee table was chosen as a range of generated gen examples is available for comparison. The designs generated by Bentley require: Every chromosome ch is arranged in hierarchy containing taining multiple m blocks of nine genes with each block lock of ge gene being defined by 16 bits Fig.

A new type of cross over method called hierarchical rarchical cross over is used to create mutations using sing points po of similarity to ensure that there is no loss of meaning. Individual Block Block Block Block Gene9 Sample phenotype notype Fig. The internal populatio opulation is randomly initiated or may be seeded pre-defined components.

Designs constraintss such a symmetry are applied at this stage. Bentley introduced a new method to then scale the fitness values according to the ranges of fitness functions.

Bentley views the evaluation software as the equivalent of designs specifications. He generates 20 coffee table designs in 35 minutes Slightly over a minute per design.

All the generated designs appear to be viable. He has been able to build one of the generated designs [30] [14]. This was done according the process described in section 3. A selection of the generated designs Fig. The genotype Quarter of the table Fig. XL Design Table showing generated values In this example the weight of the table and the center of gravity are used to filter out designs that are too heavy above 50kg or too unstable cg above 30 cm.

Design 2 and 3 fail to pass through the filter in this example. Any number of designs can be generated through this process.

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It took on average less than 6 seconds to generate a single design. GDM generated Coffee table designs 5. GADES requires the setting up of phenotypes, translating them into genotypes, developing and quantifiable evaluation criteria before the generative process can start. In comparison, in the GDM only the genotype needs to be created to commence the generative process.

The critical skills for GADES would include the phenotype building, applying appropriate constrains, genotype mapping, and the setting up of evaluation criteria. All this would require different software tools and experiences.

In comparison, the GDM scheme is built on top of CAD, requiring only a simple macro to create random numbers between set limits and instigate the CAD to generate instances of designs based on generated values. Further, the application of constrains in GADES is not directly geometrical and requires a deep conceptual understanding of genetic algorithms. The development of the generative schemes is often an interactive cyclic activity necessitating the building and testing various schemes.

The genotype building time is often a multiple of a single instance model building time, whereas it is likely to be significantly higher in GADES. In comparing the practicality of these processes, it may be concluded that GDM is significantly easier to set up and requires significantly less skills than what is required to setup the equivalent scheme in GADES to create similar results.

They are mainly: It imposes very little restrictions on the designer. GDM operates on an independent operational layer that does not interfere with the normal design process.

This is an un-encumbered method that designers are more likely to adopt as they need not be totally dependent on it.

Single Platform — the generation scheme is executed using a single CAD design environment, without the need to translate between different frame-works as in schemes where the phenotypes and genotypes are different. Transparency — the generative scheme is based on graphical representations that are relatively easily understood by designers. Ease of setting up — the generative scheme is easily setup by building of generic CAD model.

Effective in early stage design — GDM can work with early stage design models and incomplete evaluation criteria, which is often the case in early stage design. Embedded Design intent — the design intent can be embedded directly into the generative model. Ease of transition — it is relatively easy to take the design from early to late stage using the same model.

CAD advantage — advanced analytical packages now work seamlessly with CAD systems enabling the employment of sophisticated engineering criteria. In summary, the key advantage of GDM is its ease of use. Designers spend a life time developing their own particular design process and are very resistant to process changes. It is unlikely, except for a small minority, that they will adopt processes that require a completely different approach to design. The skill sets and the programming knowledge required to implement most of the evolutionary algorithms based generative methods are well outside the range of most designers.

Most design problems are multi- criteria problems where the evaluation criteria are hard to define. Thougha few multi-criteria optimization methods are available, multi-criteria problems are problematic for evolutionary algorithms that attempt to optimize performance on amalgamated criteria. Even though there are some attempts at developing multi criteria evolutionary algorithms and attempts to quantify aesthetic fitness, they are unlikely to lead to methods that designers would accept.

Evolutionarily algorithm based methods have a rigid structure that makes it difficult to incorporate into existing work practices. Limitation of search space — the design space defined by parametric genetic model is limited in size. The use of mutation and cross-overs is likely to increase the size of the search space which could lead to more creative solutions. In-exhaustive search — the generation scheme explores only limited regions of the solution space as directed by the designer.

Evaluation fatigue — GDM places, despite best efforts to limit the number of designs presented for evaluation, places significant evaluation burden on the designer. Genetic Modeling — creating a high quality genetic model is not a straight forward process.

It requires an iterative design and test process. It requires significant experience to develop expressive genetic models. Best practice for creating genetic models is not known. Enumeration of search envelope — the search envelope has to be explicitly defined. This will require additional effort. Designs could be under explored and over guided. Optimization — Genetic algorithm based methods are likely to outperform GDM in evaluatable single criteria design problems dueto its ability to automatically carry out fine grained exploration of the solution space.

Accumulation of positive attributes — Cross-overs tend to accumulate positive attributes. The lack of this effect is a serious disadvantage.

In summary, the GDM has many disadvantages. Genetic modeling also forces designers to parameterize early stage design which designers resist as they are used to designing fluidly without any encumbrances in the early stages of design development. The explicit definition of the constraint envelopes require significant time commitment that designers may not be willing to commit, especially in the early stages of the design process.

GDM forces the designer to enumerate the design search space with the promise of more exciting design possibilities; it is too early to judge if this promise will be realized across a range of design problems. However, it may be safe to state that in its current state of development, evolutionally algorithm based methods are unsuitable for design problems involving multiple complex criteria and difficult to integrate with current design practices.

They are also unviable for early stage design development where the evaluation criteria and the design are still under development. In the context of numerous failed attempts to impose structured and process for conceptual design, we have identified Section 1.

It has been demonstrated through examples that GDM meets most of these requirements. The GDM method is shown to be entirely designer driven; free of restrictive and invasive frame works allowing designers to navigate the search space in the way they see fit.

It has been shown that the selected designs can be further improved or modified manually by the designer using the same phenotype model Fig. Ways of subjecting chosen geometric entities to variations at any stage of the design development has been demonstrated.

It has been shown that GDM can be implemented with history based parametric CAD systems with and without geometric kernels; with the use of simple design tables and macros. In CAD systems where geometric kernels are present, it has been shown that complex geometric relationships can be developed purely by using its native dimensioning system.

In comparison with the shape grammar approach, it does not require the setting up of external rules which require special expertise and additional grammar interpreters to interact with CAD systems. It has been demonstrated that these geometric kernels are able to maintain the geometric logic or the grammar of the genotype through the generative process where the geometry is subject to significant change; demonstrating that the original intent of shape grammar can now be accomplished by most CAD systems with geometric kernels that facilitate dimensioning and the setting up of relationships.

It has been shown that it is possible to preselect designs that are significantly different to each other, by ensuring that the phenotype values are significantly different to each other. It has been also shown that filters can be used to eliminate designs with unacceptable performance, thus helping to reduce the selection load on the designer. The need for simplicity and transparency is considered to be of paramount importance if this method is to be adopted by industry.

In conclusion, it has been shown that it is now possible to transform CAD tools into design exploration tools using methods that are transparent and relatively simple, in a way that is useful to designers. Such genetic models combined with the constraint envelopes could have wide ranging applications outside generative design, particularly in applicators where design freedom is to be maintained within desirable limits.

Mass customization, computer games and co-creation present such opportunities. The creative freedom offered to non- designers in these applications need to be curtailed within certain limits. In such applications, random generation can be replaced by user driven values which can be bound within acceptable limits.

The ability to embed design intelligences and constraints within genetic models will enable non-designers or consumers to participate in the design of complex artifacts. Genetic models can also be used for parametric mapping of consumer preferences, as it presents formal ways of mapping geometric quality of product variants.

This could provide companies with valuables information on consumer preferences. Image based object recognition may also benefit from generative models, as they rely on internal representation of objects for the purposes of matching them with images of real objects.

By replacing the parametric models that are stored internally with more expressive genetic models it would be possible to make more accurate matches. Such an approach can perhaps be used for creating 3D buildings from aerial photographs. Genetic models may also be seen as procedural models.

Procedural representations are known to drastically reduce transmission bandwidths in online applications as geometries are reconstructed by client machines using inelegantly structured, highly compressed data. By using GDM, unlimited variations of great diversity can be created and deployed efficiently for online games, digital films and virtual environments. In addition, it will enable the participants to create equally rich and diverse content.

Explore how NX is advancing Generative Technology

It provides a structure that embeds constructional history and captures the essential attributes of the design in an interpretable and classifiable format. Before the advent of genetics, biologist dealt with plants and animals based mainly on their external construction. The understanding of genetics and evolution helped understand the structure of life and its designs in a way that revolutionized biology.

Genetics made it possible to interpret, classify and modify the design of life forms. Language too went through a similar transformation. The field of design unfortunately lacks interpretable, classifiable and modifiable structure. The science of linguistics made it possible to extract meaning of out sentences and made written language computationally interpretable. In stark comparison, the structure behind design is currently poorly understood and yet to be developed. The powerful search technologies that we enjoy today are the result of the understanding of the structure of languages.

If such understanding of structure is developed for design it would facilitate search. In the context of genetically structured design, search is design.

Currently, design information is mostly captured in CAD ias final geometric form, which allows for geometry based searches. Such searches are of limited value. In architecture, Building Information Management systems BIM now make it possible to capture significant about of information about buildings.

However, design currently lacks formal CAD independent data structures that are capable of capturing its complexity. If design information is captured in an interpretable, classifiable and modifiable form, then it would be possible as biologist do; to understand the evolutional heritage, constructional logic and the external conditions that shaped the form of all designed artifacts.

Its implications will be far reaching; as it would connect design, manufacture and consumption in a way that was not possible before. Louis sullivan, Art deco and Art nouveau movements were greatly notre dame one of the most signiicant architects of the modern du Haut chapel inspired by it.

Later, ItkE research Pavilion in stuttgart Although Le corbusier tried to systematize his own proportional each of these projects employed diferent tools, they design methods in the book Modulor , praising all mimicked the intelligent processes of living organisms, the golden section as the gateway to beauty.

He also translating them into architecture rather than simply using applied his famous Modulor proportional diagram to them as inspiration for form and appearance.

Both are among his most geometrically advanced projects, proving that the Modulor proportion rules could function as a toolbox ofering unpredictable outputs.

Aldo rossi, for example, he built in Moscow, which still stands today igure Finally, Frei otto, investigating tensile membrane structures, developed For oswald Mathias ungers, responding to the site Figure 03 left the Munich olympic stadium in igure 04 — a deinite diagrid on shukhov involved deining common silhouettes and morphological tower in Moscow, highlight of his career otto and rasch Although characteristics, such as materiality, texture, arches, by Vladimir shukhov developed at a time when computers where not used symmetry, roofs and angles, then trying to reproduce Figure 04 right in architecture at all, the design approach adopted Munich olympic these in a new composition.

Mario Botta, on the other hand, uses simple, oten symmetrical, modernist forms in combination with site-inspired materials, colours and traditional building techniques. His single-family house in Ligornetto, switzerland , for example, bears the characteristic stripes oten found in the region cappellato Design processes driven by performance In contrast to the above, a number of architects and engineers have practised a completely diferent form-inding method.

Eisenman applied these techniques in relation to rules of order, developing several projects on this basis, such as the Biocentrum in Frankfurt and the nunotani corporation headquarters in tokyo ; igure 05 Eisenman Models generative design as a cyclical process based on a simple of design capable of consistent, continual and dynamic abstracted idea, which is applied to a rule or algorithm transformation are replacing the static norms of igure It then translates into a source code, which conventional processes.

It is an or conical forms. Grids, between the designer and the design system. Form is no longer being made, but found, based on a set of rules or algorithms, in association with celestino soddu deines generative design Figure 06 mainly digital, but also physical, tools and techniques.Loading Preview. Single Platform — the generation scheme is executed using a single CAD design environment, without the need to translate between different frame-works as in schemes where the phenotypes and genotypes are different.

The lack of this effect is a serious disadvantage. Once the final design is chosen, the region around it can be examined in finer detail and the limiting performance envelopes can be defined with greater certainty 3.

Selection by comparison 3. By John Frazer. Gero, Creativity in design using a design prototype approach, in Modeling creativity and knowledge-based creative design J.

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