whole design intent, but this is what Inventor is good at. The way it packages bits of 'scripted' components into 'little models' that can be stored and re-assembled is central to MCAD working.
The Inventor model shown is almost 5 years old. We don't model like that any more, however it does offer a good idea of general MCAD modeling approaches.
iParts is useful in certain situations, it could've been useful in the above model, its usefulness is often in function of the quantity of variants/configurations.
So much is scripted in GH, maybe it should also be possible to script/define/constrain/assist the placement/gluing of the results?
...
Starting point: I think we are talking across purposes. AFAIK, the solving sequence of GH's scripted components is fixed. It won't do circular dependencies... without a fight. The inter-component dependencies not 'managed' like constraints solvers do for MCAD apps.
Components and assemblies are individual files in MCAD.
Placement of these within assemblies in MCAD is a product of matrix transforms and persistent constraints. There is no bi-directional link, the link is unidirectional (downflow only), because of the use of proxies.
Consequently, scripting the placement of components is irrelevant in GH, unless you decide that each component needs to be contained in its own separate file.
This also brings up the point that generating components and assemblies in MCAD is not as straightforward. In iParts and iAssemblies, each configuration needs to be generated as a "child" (the individual file needs to be created for each child) before those children can be used elsewhere.
You notice the dilemma, if you generate 100 parts, and then you realize you only need 20, you've created 80 extra parts which you have no need for, thus generating wasteful data that may cause file management issues later on.
GH remains in a transient world, and when you decide to bake geometry (if you need to at all), you can do that in one Rhino file, and save it as the state of the design at that given moment. Very convenient for design, though unacceptable for most non-digital manufacturing methods, which greatly limits Rhino's use for manufacturing unless you combine it with an MCAD app.
One of the reasons why the distributed file approach makes perfect sense in MCAD, is that in industry you deal with a finite set of objects. Generative tools are usually not a requirement. Most mechanical engineers, product engineers and machinists would never have any use for that.
The other thing that MCAD apps like Inventor have, is the 'structured' interface that offers up all that setting out information like the coordinate systems, work planes, parameters etc in a concise fashion in the 'history tree'. This will translate into user speed. GH's canvas is a bit more freeform. I suppose the info is all there and linked, so a bit of re-jigging is easy. Also, see how T-Flex can even embed sliders and other parameter input boxes into the model itself. Pretty handy/fast to understand, which also means more speed.
True. As long as you keep the browser pane/specification tree organized and easy to query.
:)
Would love to understand what you did by sketching.
I'll start by showing what was done years ago in the Inventor model, and then share with you what I did in GH, but in another post.
Let's use one of the beams as an example:
We can isolate this component for clarity.
Notice that I've highlighted the sectional sketch with dimensions, and the point of reference, which is in relation to the CL of the column which the beam bears on. The orientation and location of the beam is already set by underlying geometry.
Here's a perspective view of the same:
The extent of the beam was also driven by reference geometry, 2 planes offset from the beam's XY plane, driven by parameters from another underlying file which serves as a parameter container:
Reference axes and points are present for all other components, here are some of them:
It starts getting cluttered if you see the reference planes as well:
Is I mentioned earlier, over time we've found better ways to define and associate geometry, parameters, manage design change, improving the efficiency of parametric models. But this model is a fair representation of a basic modeling approach, and since an Inventor-GH comparison is like comparing apples and oranges anyways, this model can be used to understand the differences and similarities, for those interested.
I haven't even gotten to your latest post yet, I will eventually.…
Added by Santiago Diaz at 10:36am on February 26, 2011
he picture (4).
Previously, I had a problem with generating intersections between the two directions of the beams, but a colleague helped me by extending beams, so there was no problem with lines of intersection. But this solution has generated curl (5) at the highest vertex geometry, which I ignored in order to repair it before printing, perhaps this mean my problem with my beam spread properly. Only when the beams is 19, does not jump no problem, but I still can not distribute them properly.
(1)
(2)
(3)
(4)
(5)
I tried to show as simply as possible by removing or signing my code in GHX file.
Thank you in advance for your help
…
edit 29/04/14 - Here is a new collection of more than 80 example files, organized by category:
KangarooExamples.zip
This zip is the most up to date collection of examples at the moment, and collects t
ers and researchers, programmers and artists, professionals and academics who come together for 4 days of intense collaboration, development, and design.
The sg2012 Workshop will be organised around Clusters. Clusters are hubs of expertise. They comprise of people, knowledge, tools, materials and machines. The Clusters provide a focus for workshop participants working together within a common framework.
Clusters provide a forum for the exchange of ideas, processes and techniques and act as a catalyst for design resolution. The Workshop is made up of ten Clusters that respond in diverse ways to the sg2012 Challenge Material Intensities.
Applicants to the sg2012 Workshop will select their preferred cluster from the following:
Beyond Mechanics
Micro Synergetics
Composite Territories
Ceramics 2.0
Material Conflicts
Transgranular Perspiration
Reactive Acoustic Environments
Form Follows Flow
Bioresponsive Building Envelopes
Gridshell Digital Tectonics
More information about the Workshop and Clusters can be found here:
http://smartgeometry.org/index.php?option=com_content&view=article&id=116&Itemid=131
The application process will close on January 15th, 2012.
Full Fee $1500
Reduced Fee $750
Scholarship Fee $350
Fees include attendance to both the workshop and conference from March 19th-24th.
Reduced Fee and Scholarships are available only for Academics, Students and Young Practitioners, and are awarded during a competitive peer review process.
sg2012 takes place from 19-24 March 2012 at EMPAC (http://empac.rpi.edu/) and is hosted by Rensselaer Polytechnic Institute in Troy, upstate New York USA. The Workshop and Conference will be a gathering of the global community of innovators and pioneers in the fields of architecture, design and engineering.
The event will be in two parts: a four day Workshop 19-22 March, and a public conference beginning with Talkshop 23 March, followed by a Symposium 24 March. The event follows the format of the highly successful preceding events sg2010 Barcelona and sg2011 Copenhagen.
sg2012 Challenge Material Intensities
Simulation, Energy, Environment
Imagine the design space of architecture was no longer at the scale of rooms, walls and atria, but that of cells, grains and vapour droplets. Rather than the flow of people, services, or construction schedules, the focus becomes the flow of light, vapour, molecular vibrations and growth schedules: design from the inside out.
The sg2012 challenge, Material Intensities, is intended to dissolve our notion of the built environment as inert constructions enclosing physically sealed spaces. Spaces and boundaries are abundant with vibration, fluctuating intensities, shifting gradients and flows. The materials that define them are in a continual state of becoming: a dance of energy and information. Material potential is defined by multiple properties: acoustical, chemical, electrical, environmental, magnetic, manufacturing, mechanical, optical, radiological, sensorial, and thermal. The challenge for sg2012 Material Intensities is to consider material economy when creating environments, micro-climates and contexts congenial for social interaction, activities and organisation. This challenge calls for design innovation and dialogue between disciplines and responsibilities. sg2010 Working Prototypes strove to emancipate digital design from the hard drive by moving from the virtual to the actual in wrestling with the tangible world of physical fabrication. sg2011 Building the Invisible focused on informing digital design with real world data. sg2012 Material Intensities strives to energise our digital prototypes and infuse them with material behaviour. They have the potential to become rich simulations informed by the material dynamics, chemical composition, energy flows, force fields and environmental conditions that feed back into the design process.
More information can be found at http://www.smartgeometry.org
Follow us on Twitter at http://twitter.com/smartgeometry…
Added by Shane Burger at 12:29pm on December 13, 2011
lly it should not make much of a difference - random number generation is not affected, mutation also is not. crossover is a bit more tricky, I use Simulated Binary Crossover (SBX-20) which was introduced already in 1194:
Deb K., Agrawal R. B.: Simulated Binary Crossover for Continuous Search Space, inIITK/ME/SMD-94027, Convenor, Technical Reports, Indian Institue of Technology, Kanpur, India,November 1994
Abst ract. The success of binary-coded gene t ic algorithms (GA s) inproblems having discrete sear ch sp ace largely depends on the codingused to represent the prob lem variables and on the crossover ope ratorthat propagates buildin g blocks from pare nt strings to childrenst rings . In solving optimization problems having continuous searchspace, binary-co ded GAs discr et ize the search space by using a codingof the problem var iables in binary st rings. However , t he coding of realvaluedvari ables in finit e-length st rings causes a number of difficulties:inability to achieve arbit rary pr ecision in the obtained solution , fixedmapping of problem var iab les, inh eren t Hamming cliff problem associatedwit h binary coding, and processing of Holland 's schemata incont inuous search space. Although a number of real-coded GAs aredevelop ed to solve optimization problems having a cont inuous searchspace, the search powers of these crossover operators are not adequate .In t his paper , t he search power of a crossover operator is defined int erms of the probability of creating an arbitrary child solut ion froma given pair of parent solutions . Motivated by t he success of binarycodedGAs in discret e search space problems , we develop a real-codedcrossover (which we call the simulated binar y crossover , or SBX) operatorwhose search power is similar to that of the single-point crossoverused in binary-coded GAs . Simulation results on a number of realvaluedt est problems of varying difficulty and dimensionality suggestt hat the real-cod ed GAs with t he SBX operator ar e ab le to perform asgood or bet t er than binary-cod ed GAs wit h t he single-po int crossover.SBX is found to be particularly useful in problems having mult ip le optimalsolutions with a narrow global basin an d in prob lems where thelower and upper bo unds of the global optimum are not known a priori.Further , a simulation on a two-var iable blocked function showsthat the real-coded GA with SBX work s as suggested by Goldberg
and in most cases t he performance of real-coded GA with SBX is similarto that of binary GAs with a single-point crossover. Based onth ese encouraging results, this paper suggests a number of extensionsto the present study.
7. ConclusionsIn this paper, a real-coded crossover operator has been develop ed bas ed ont he search characte rist ics of a single-point crossover used in binary -codedGAs. In ord er to define the search power of a crossover operator, a spreadfactor has been introduced as the ratio of the absolute differences of thechildren points to that of the parent points. Thereaft er , the probabilityof creat ing a child point for two given parent points has been derived forthe single-point crossover. Motivat ed by the success of binary-coded GAsin problems wit h discrete sear ch space, a simul ated bin ary crossover (SBX)operator has been develop ed to solve problems having cont inuous searchspace. The SBX operator has search power similar to that of the single-po intcrossover.On a number of t est fun ctions, including De Jong's five te st fun ct ions, ithas been found that real-coded GAs with the SBX operator can overcome anumb er of difficult ies inherent with binary-coded GAs in solving cont inuoussearch space problems-Hamming cliff problem, arbitrary pr ecision problem,and fixed mapped coding problem. In the comparison of real-coded GAs wit ha SBX operator and binary-coded GAs with a single-point crossover ope rat or ,it has been observed that the performance of the former is better than thelatt er on continuous functions and the performance of the former is similarto the lat ter in solving discret e and difficult functions. In comparison withanother real-coded crossover operator (i.e. , BLX-0 .5) suggested elsewhere ,SBX performs better in difficult test functions. It has also been observedthat SBX is particularly useful in problems where the bounds of the optimum
point is not known a priori and wher e there are multi ple optima, of whichone is global.Real-coded GAs wit h t he SBX op erator have also been tried in solvinga two-variab le blocked function (the concept of blocked fun ctions was introducedin [10]). Blocked fun ct ions are difficult for real-coded GAs , becauselocal optimal points block t he progress of search to continue towards t heglobal optimal point . The simulat ion results on t he two-var iable blockedfunction have shown that in most occasions , the sea rch proceeds the way aspr edicted in [10]. Most importantly, it has been observed that the real-codedGAs wit h SBX work similar to that of t he binary-coded GAs wit h single-pointcrossover in overcoming t he barrier of the local peaks and converging to t heglobal bas in. However , it is premature to conclude whether real-coded GAswit h SBX op erator can overcome t he local barriers in higher-dimensionalblocked fun ct ions.These results are encour aging and suggest avenues for further research.Because the SBX ope rat or uses a probability distribut ion for choosing a childpo int , the real-coded GAs wit h SBX are one st ep ahead of the binary-codedGAs in te rms of ach ieving a convergence proof for GAs. With a direct probabilist ic relationship between children and parent points used in t his paper,cues from t he clas sical stochast ic optimization methods can be borrowed toachieve a convergence proof of GAs , or a much closer tie between the classicaloptimization methods and GAs is on t he horizon.
In short, according to the authors my SBX operator using real gene values is as good as older ones specially designed for discrete searches, and better in continuous searches. SBX as far as i know meanwhile is a standard general crossover operator.
But:
- there might be better ones out there i just havent seen yet. please tell me.
- besides tournament selection and mutation, crossover is just one part of the breeding pipeline. also there is the elite management for MOEA which is AT LEAST as important as the breeding itself.
- depending on the problem, there are almost always better specific ways of how to code the mutation and the crossover operators. but octopus is meant to keep it general for the moment - maybe there's a way for an interface to code those things yourself..!?
2) elite size = SPEA-2 archive size, yes. the rate depends on your convergence behaviour i would say. i usually start off with at least half the size of the population, but mostly the same size (as it is hard-coded in the new version, i just realize) is big enough.
4) the non-dominated front is always put into the archive first. if the archive size is exceeded, the least important individual (the significant strategy in SPEA-2) are truncated one by one until the size is reached. if it is smaller, the fittest dominated individuals are put into the elite. the latter happens in the beginning of the run, when the front wasn't discovered well yet.
3) yes it is. this is a custom implementation i figured out myself. however i'm close to have the HypE algorithm working in the new version, which natively has got the possibility to articulate perference relations on sets of solutions.
…
Introduction to Grasshopper Videos by David Rutten.
Wondering how to get started with Grasshopper? Look no further. Spend an some time with the creator of Grasshopper, David Rutten, to learn the
This blog post is a rough approximation of the lecture I gave at the AAG10 conference in Vienna on September 21st 2010. Naturally it will be quite a different experience as the medium is quite…
Added by David Rutten at 3:27pm on September 24, 2010