FOCUS ON DIGITALISATION
Steiling , of Optimate ’ s set-up two years ago .
This initial vision has since become reality with a digital solution for the analysis and optimisation of sheet-metal parts . Those using the solution no longer need to run the risk of bringing a part into production that may not be possible to manufacture at all , or only at high cost .
“ Not only do we give design and work preparation more process reliability , but timeconsuming queries with the customer and expensive , manual redesigns are now a thing of the past ,” commented Jonas Steiling .
Optimate wishes to digitise sheet metal expertise and capture knowledge for its web app solution . Image : Shutterstock . com .
Part analysis via cloud-based app
Optimate ’ s cloud solution consists of two products : the feasibility check and the part optimisation . In a first step , the user uploads his sheet-metal part as STEP or SolidWorks format in the cloud-based web app . Multiple 3D CAD files can be uploaded and analysed simultaneously . Structural steel , stainless steel and aluminium can be selected as material options .
Within a few seconds , the potential of each part is listed . According to Optimate , a highly optimisable part can mean cost savings of more than 30 %. A part that can be optimised well can lead to cost savings of up to 20 %.
“ The user now sees optimisation opportunities where he previously had no transparency about his parts and where it is worth taking another closer look ,” explained the CEO of Optimate .
The Optimate service recognizes the optimisation potential within seconds and indicates any design errors . Parallel to the potential detection , a feasibility check runs in the background to ensure that each part can also be manufactured in a process-safe manner . If this is not the case , the corresponding areas on the part are marked in colour as warnings .
While deformations often affect the part ’ s appearance , a large proportion of the warnings issued prevent the production worker from being able to manufacture this part at all . If , for example , the minimum flange length is not reached and the marked error leg is too short ( depending on the bending angle , sheet thickness , material and tool pairing ) then the leg cannot be bent . The designer can therefore be alerted as early as possible to the warning or other interfering contours , which are marked in red in the 3D graphic ( see Figure 1 above ).
Part optimisation at the push of a button
But what is the use of warnings if they have to be corrected manually ?
The highlight of Optimate ’ s solution : at the push of a button , a design proposal for the
Figure 1 : Mesh plate with undercut flange length . The leg is too short and cannot be bent . The feasibility check detects the error and marks it . At the push of a button , the leg is adjusted to the minimum flange length and the CAD file is ready to download . ( Photo source : Optimate )
process-safe production of the part is made immediately .
“ With one click , an optimisation graphic displays an extended leg to the minimum flange length ,” Jonas Steiling explained .
The benefit is that the designer and work preparation staff can always see the actual state displayed next to the optimised target state . Users can then download the adapted part directly as a STEP or SolidWorks file and transfer it to their CAD program .
For the whole thing to work , the existing bending tools must be initially entered into the individual customer profile . This prevents optimisation proposals from being made for which no required toolset is available .
AI with over 90 % accuracy
The start-up team has developed its own artificial intelligence to identify the optimisation potential . The AI algorithm contains around 60 different features that can describe most sheet-metal parts .
“ In the meantime , we can predict with 92 per cent accuracy whether there is potential for optimisation , even for parts that the AI has never seen before ,” explained Jonas Steiling .
The more users upload their parts to the cloud solution , the “ smarter ” the AI eventually becomes . “ We want to keep training the AI so that the accuracy of the hits keeps growing ,” added Steiling .
Optimate has also thought of the current last 8 % of the route . If users reach the limits of what is possible , they can click on “ Consulting Services ” in the cloud platform and bring Optimate ’ s consultants on board .
Optimisation potential
Often the feasibility check does not detect any warnings i . e . the analysed part can in principle be manufactured in this way . AI , on the other hand , recognizes high potential for optimisation . So why not optimise when this can often eliminate entire production steps and make enormous cost savings possible ?
The issue now is how can this sheet-metal part be redesigned with system support ? The start-up has also thought up and developed a solution for this ; automated optimisation . Matching CAD parts can be uploaded easily . In the background , the 3D body is unfolded into a 2D representation . This unfold is then transferred into a graphical description in which each surface has a connection type to its neighbouring surfaces . On these graphs , the Optimate solution generates alternative design methods for the part and displays the most relevant ones . A stability check and a cost calculation are automatically run for each of these solution variants .
An example in point is a sheet-metal