How to Successfully Evolve Software Code in a World of Simulation

Maturing isn’t only for good wine. It also creates software that delivers the latest trends.

It is our belief at AutoForm that the understanding of bringing forming related fabrication processes into virtualization goes hand in hand with providing the customer with a fast and easy-to-use-approach.

SAristotelesince the early beginning, AutoForm has dedicated much effort to tailoring our software algorithms towards problem solving, optimizing the entire process, and handling any of the challenges that might be faced throughout the real world engineering of sheet metal stampings.

So how is all this done? How is a truly successful software really evolved?

There are many facets to this success. Here are a few considerations towards the greater picture.

Starting with the GUI, its most recent releases have reached an outstanding level of maturity, and it easily guides users through the typical workflow of sheet metal forming industry.

Or consider the solver. It is the heart of our software program. The objective of ever constant improvement has always been to increase accuracy whilst decreasing the computation time for generating valuable and reliable results.

True to the company credo ‘forming reality’ AutoForm has devoted significant development resources towards making our simulation results match reality; ever closer with each new software release.

This is Kaizen at its best in the realm of sheet metal forming simulation.

But in order to really ensure a permanent process of evolving the code´s capabilities towards a defined target corridor the company runs test, and calibrates their developments, in collaboration with selected customers in the form of well-defined pilots and joint venture development projects.effectiveness-1It should also be pointed out that our company participates frequently in benchmark activities – such as NUMISHEET – and proves itself under the scrutiny of third party test driven contests. The results are rewarding in terms of learning and our software’s performance by all accounts has been outstanding.

Last, but not least, we actively screen publications in scientific journals to stay ahead of the game.

Driven by such a diverse range of feedback the company moves ahead by allocating resources for further code modification for including those latest ideas in upcoming releases.

Then you have to consider that novel materials, such as UHSS, and their processing technologies are constantly emerging onto the scene. Facing these two issues means that we are simultaneously challenged to adapt or at times must completely rethink our modelling methods and algorithms.

The outcome of this process of evolution and adaption could be, for instance, the creation of new alternative draw bead descriptions or even better element formulations.

But the excellence of AutoForm is not claimed, but rather it is widely acclaimed.

Here is a nice example review from Stadnicki and Wróbel, who after an analysis of options for the numerical prediction of springback behavior of a real stamped part stated:

“The results obtained from AutoForm program are characterized by a smaller range of deviations of simulation results from the reality and a greater percentage share of the areas of the model with the smallest deviation from the actual stamped part. The comparison of the numerical effectiveness of the solvers shows that AutoForm definitively ensures a shorter time of analysis.” effectiveness-2

In this regard, AutoForm is particularly proud to see others recognize our efforts, proving that although our efforts to simulate real-life forming processes become ever more detailed AutoForm’s computation times are still well below other competitive systems.

Ultimately and to conclude, we at AutoForm attribute all these successes to the joint efforts of the whole organization, a union of many faces working together to beat any challenge.

Stay tuned for more, this blog post is but an introduction to stories we’ll visit in detail soon.

Have your say, leave a comment. We enjoy your participation.

 

Stamping and Strawberry Pies Done Right

Robustness – what is it and how does it relate to strawberry pies?

In several previous posts we have talked about robustness and the repeatability of processes, and how the process might vary in its output relative to the changing input parameters. And we realize that we have not actually discussed in any detail precisely how you can recognize this variation.

Variation of a stamping process is measured in attributes like splits, wrinkles, fitting of a fixture. This variation has been expressed using images which display color plots with classic traffic signals.

AutoForm Sigma results 1

AutoForm-Sigma result indicating unacceptable failure rate

Robustness of a process and the variability of the output quality that might arise when faced with un-intended input variation is an abstract idea for many in our industry.

Metaphors always help explain abstract ideas, so as a first explanation attempt let’s discuss our latest trip to the market to buy fresh strawberries. The strawberries are intended for a strawberry pie. The recipe that we found on the internet requires 500g of fresh strawberries. At the market there is a vendor selling 500g packages of strawberries – great!

Incoming Strawberries

Incoming Strawberries

In absolute terms if I buy any box of strawberries for which I pay for 500g, I believe I am getting 500g. I usually assume that I pay for what I get, and since I pay for 500g I have faith that 500g is what I will take home for my pie. When arriving at home, the strawberries are added with the remaining ingredients as the recipe outlines. Processed in the oven for the correct time and the results are less than expected.nailed it

The question is where did we go wrong? There are many potential sources for the variation from a planned or intended result in this case, from the strawberries to the execution of the recipe’s instructions. Can we blame the recipe (engineering) or is it the materials (bad strawberries, too many, too few strawberries) or any other ingredients and their combination?

We can’t come to any clear judgment it until we actually understand what those inputs (ingredients qualities and quantities) really were. Let’s focus on the strawberries first, but bear in mind other aspects of the recipe would also require similar scrutiny eventually.

If we assume that the strawberry mass was the most influential input that led to the poor pie performance we might conclude that the recipe could be adjusted to account for using fewer of the 500g of strawberries. And we might go to the market, buy another package of strawberries and this time the pie could be too dry.

assumed reliability

Assumed reliability of mass of package of strawberries

These two batches of strawberries might have been the only two outliers among all the strawberry containers at the market. But realistically, it is more likely that the strawberries display typical variations in the delivered mass.

distribution strawberries

Distribution of actual reliability of mass of package of strawberries

The farmer intended to make good on the promise of the specified mass of strawberries, but the process by which the strawberries are added leaves some room for unintended process variation. Such process variation is natural and normal. Strawberries that occur organically in the world are not homogeneous in size and shape, and they might have more or less moisture content, or just fit in the container differently.

If there was a way to assure that our recipe could account for such variations, then this would be highly valuable to us. If we could accurately evaluate the quality of pies and account for ranges of acceptable mass variation, then we would have a better recipe.

Variation Pie Quality

Mass variation range for incoming strawberries
is likely to produce the least variation in pie quality?

The recipe may have been correct the whole time. The presumption that the strawberries are delivered at the specification mass might be the flaw.

How often do we as engineers assume that the ordered specification is exactly what is delivered? Some might argue that we should have re-weighed the strawberries and precisely controlled the strawberry content. However, in this analogy as it relates to stamping there are many input variables which cannot be directly controlled on the shop floor. Yet they still have to be accounted for. Input parameters with such unintended variations include material thickness, mechanical properties of the sheet metal, lubrication, blank holder force, blank location, just to name a few that we have mentioned in previous blog posts. Their unintended variation can cause unexpected part quality issues, increases in scrap rates and production down times during the manufacturing process.

Wouldn’t it be good to know if our strawberry pie will “nail it” or fail – before we make it?

Find out more how to “nail” it in our earlier blog post here:

Robustness Springback Comensation Blog Post

 

What is the future of engineering hemming processes?

During the past 20 years, stamping simulation has brought tremendous change to the way stamping processes are defined and validated, stamping tools are designed and built, and stampings are produced. Without state-of-the-art simulation tools, today’s complex stampings using high-tech sheet metal materials could never be engineered and produced to meet ever-shorter vehicle development time lines. Imagine what the availability of similarly powerful simulation capabilities would mean for engineering hemmed subassemblies!

Today, BiW (Body in white) planners and process engineers have to master the complex interactions between dimensional and surface quality of the individual stampings and the feasibility and quality targets of the hemmed assembly — without the help of up-front and detailed process simulation results. It is, for example, not at all certain that a hemmed subassembly will meet its dimensional specifications even if all stampings in the assembly meet their individual dimensional specifications. And it is not at all clear which measures are most effective to bring the hemmed assembly into spec, i.e., which of the individual parts should be modified in what way to achieve the desired assembly result. Too numerous are the parameters influencing the final assembly result and too complex the involved stamping and hemming processes.

Corrective to  the hemming process often need to focus on the shape and geometry of the flanged part — leading to modifications of the trim line (i.e., the trim dies) and possibly even of the draw die geometry or blank dimensions. Furthermore, modifications interact with each other, making the result very difficult to predict. Since — without up-front analysis and validation in the virtual state  — these modification requirements become evident very late in the process time line, the current process is a complex and interruptive workflow with obvious disadvantages. In addition, experiences gathered with one particular assembly may not necessarily yield relevant suggestions for correcting another. To master this complexity and to shorten tryout time lines, manufacturers have responded by creating dedicated  teams working hand in hand  with involved in-house departments and suppliers – i.e., stamping operations and process engineering teams as well as BiW-specialists — in order to assess the feasibility and anticipated quality of the assembly as early as possible.

Hemming process simulation capabilities on par with the evolutionary state of stamping simulation would mean, for example, that as soon as initial single-part geometry data are available, the BiW planning process would simultaneously involve the creation of virtual conceptual layouts of hemming processes and the necessary hardware. In a few initial runs, the software would indicate specific tool geometries, hemming process sequences, travel directions, travel paths, and speeds — initially without considering the forming history of the individual stampings — allowing a very early definition and validation of the hemming process intent.

This subsequently would lead to targeting or even completely avoiding  corrective actions carried out by the hemming device designer in the form of redesign tasks and tryout loops later on. Additionally, the feasibility of the individual part geometries w.r.t. the assembly process may be assessed and part change requests formulated and validated at a very early stage.

Simulation of a table-top hemming process

Simulation of a table-top hemming process

In an extended virtual dashboard, hemming failures such as wrinkles and splits at free edges and corners, springback of the hemmed subassembly or hem thickness, roll-in, and deviations are evaluated easily and quickly with regard to necessary modification of the single part, process, or hemming device design. Therefore, in many ways, the application of such software would lead to substantial increases in quality and process transparency and to decreases in costs and lead-times. Finally, this would lead to a reliable prediction of technical solutions and therefore to a more accurate forecast of investments for upcoming car projects.

Now just dreaming about a better future involving such improvements of the hemming engineering process would certainly not make a good AutoForm blog entry. Many of our readers may not be aware that many of the processes imagined in the vision outlined above are already available today. Detailed hemming simulations are already available at AutoForm including  use cases requiring part geometry data with and without forming history of the individual panels.

In the first case, planning-relevant data are generated in an early phase by setting up basic models of the assembly process using part geometry only. These preliminary results allow basic decisions regarding process intent leading to an approximate assessment of process complexity, throughput capacity, and logistics requirements as well as the number of devices and required shop-floor space. Different types of hemming tool shapes and their working directions/angles may be easily evaluated, and part geometry may be assessed including a valid trim line, for example. This early process analysis is sufficiently accurate to generate valuable process and engineering input data for the planning and design of the hemming devices.

In the second case, the hemming simulation – now including the forming history of the individual stampings – allows for a far more detailed analysis, making use of preceding simulation steps like forming and trimming. Starting from the already existing hemming simulation model built earlier, the forming histories of the individual parts are added along with available process refinements and modifications. The consolidated analysis allows a detailed assessment of all hemming-related issues to be anticipated  — in roll hemming, die hemming, or tabletop hemming – as well as advanced analyses such as the springback behavior of the assembly using various clamping strategies.

Springback after Hemming

Springback after Hemming

These new hemming process analysis capabilities may be harnessed by viewing the hemming validation as an extension of the regular stamping process simulation – placing it into the hands of a specialist already familiar with the simulation of sheet metal forming processes. Or, the hemming process simulation and validation are taken care of by specialist BiW teams using AutoForm’s data exchange capabilities. In this way, the software supports the workflow of BiW planning processes in different OEM-departments or even with responsibilities distributed between OEM’s and supplier companies.

So while real-life simulation for hemming processes certainly has not been around for as long as stamping process simulation, it has taken giant leaps in the recent past. And — by leveraging simulation technology already proven in the field of stamping simulation — it got a running start. While there is still some catching up to do, hemming simulation today answers  most of the important questions regarding hemming process planning and engineering – leading to avoidance of planning errors, shortening engineering and lead-times, and cutting costs, benefits that  stamping process engineers the world over are already relying on in their daily work.

One of AutoForm’s presentation highlights at the EuroBLECH 2016 in Hannover, Germany, in October 2016 was the introduction of this exciting new technology to customers and other interested companies. Further information dedicated to hemming simulation may be obtained at: http://www.autoform.com/en/hemming.html

 

How to go beyond educated guessing…

“If at first you don’t succeed, try, try again.”

Systematic process improvement, as described in several previous posts (Adjusting complex process variables: Drawbead shapes, Virtual Tryout or Process Engineering?,What is passing?, In the rearview mirror, Infinitely adjustable presses, when should we stop adjusting?), is a method of using multiple iterations of simulated forming processes where the design inputs are varied intentionally over a range of potential settings. The intent of this method is to systematically identify what values for the input settings address any forming issues that may arise for given combinations of tool, process, or production settings.

Some of you might wonder if such a process can work in practice. We had the opportunity recently to run a trial of this method, where we asked groups of process engineering professionals to attempt to manually interpret a simulated stamping process, with significant forming issues for visible splitting, challenges with wrinkles, and draw-in beyond acceptable limits. Each engineer was given the chance to define for themselves, based on their experience, the next process combination with which to attempt to address the forming issues.

Sample process simulation result as starting point of issue resolution attempts

Sample process simulation result as starting point of issue resolution attempts

They could choose to edit or define new settings for blank size and shape, drawbead restraints, binder pad pressure, tooling radii, and addendum wall angles. As one might imagine, given rooms full of engineers,  a seemingly infinite number of combinations could be rapidly defined. To find out which combination might yield the best result would require the participants in this study to run and evaluate the results before they could come to any conclusions.

This exercise was repeated in 10 locations with different participants. In all, there were over 340 discrete simulations created by the 137 participants, for an average of 2.5 iterations per participant. When all of these simulations were evaluated, it was found that only seven of the 340 simulations resulted in a resolution of all forming issues – and a “working process.”

Forty-three of the 340 discrete simulations resulted in some level of meaningful improvements to the issues. These near misses could eventually have been combined to further refine any of the forming issues, but that would still require time and effort by the engineers to critically evaluate each combination of their inputs into a new iteration – essentially seeding seeding a new “educated guess” and simulating a 341st iteration to find if all the forming issues had been addressed.

As an alternative to creating another batch of guesses to run, the workshop participants were asked to collect the ranges of the forming parameters that they used in order to define a matrix of upper and lower bounds for the defined forming parameters.

Sample matrix of parameter ranges set up by ten participants in 10 workshops

Sample matrix of parameter ranges set up by ten participants in 10 workshops

This matrix of ranges was then used to define a single input set for an AutoForm-Sigma Systematic Process Improvement (SPI) analysis. When defining an SPI run, users can determine for each process parameter a minimum and maximum value; the software automatically combines the ranges of input parameters into a set of simulation “realizations,” each representing a different combination of inputs.

In this way, the entire range of possible sensible process parameters as defined by the user — and all resulting outcomes — can be analyzed at once. In the end, at each workshop location a single AutoForm-Sigma run was performed based on the combined ranges defined by the participants. In eight of the ten workshops, the SPI method achieved precisely what the participants sought – namely a clear definition of which process parameter values addressed all the forming issues.

At two of the workshops, the Sigma set ranges were proven not to address all the issues. It might be tempting to think that this demonstrates a weakness in the approach. But consider the following: At those two locations, it was shown that the solution does not live within the ranges that the users had defined themselves — in other words, it was shown that NO solution existed within the parameters that they thought should work. How many more manual simulation setups and runs would it have taken to come to the same conclusion? To know that a solution does NOT exist (within the range of parameters deemed reasonable) is possibly even more valuable than being told what the solution is.

 

 

Technical Seminars 2016 reached 229 participants in China

AutoForm Technical Seminars 2016 were held in the major automotive hubs of China in Shanghai, Guangzhou, Changchun and Beijing in November. The aim was to link expertise from AutoForm with expertise from the Chinese market. An impressive number of 229 participants from automotive OEMs, manufacturing companies, components and parts suppliers together with related college teachers attended the events this year to engage in discussions and gave highly positive feedback.

dr-bart-carleer-was-introducing-autoform-r7-highlights

Dr. Bart Carleer was introducing AutoFormplus R7 highlights

Dr. Bart Carleer, AutoForm Technical Director, gave his lecture on “The next level of process simulation”; the motto of AutoFormplus R7.

customers-were-absorbed-in-the-lecture

Participants got absorbed in the lecture

He talked about significant highlights of the latest released AutoFormplus R7 software including Progressive Die Application, material modeling and springback compensation. He also gave a brief introduction of our new family member –TriboForm, a company offering software solutions for the simulation of friction, lubrication and wear, through a more realistic consideration of tribological effects, a new level of simulation accuracy can be achieved.

autoform-china-2016_04_news

autoform-china-2016_06_news

autoform-china-2016_05_news

Seminars in Shanghai, Guangzhou, Changchun and Beijing

Some of the winners with smile

Some of the lucky winners with a big smile

In addition to the giveaways we prepared for every customer, customized Swiss Army knives and watches with AutoForm logo were given to our lucky draw winners to express our gratitude for their trust and support.

All customers learnt some new functions and highlights of AutoFormplus R7, exchanged software operation experience with our technical experts, and established business contacts and relationships via the events.

We do hope and believe it was worthwhile for every customer to participate in the events, if you were not able to attend this time, no need to worry, let’s meet in 2017!

 

What is passing?

After the recent posts on failure prediction based on FLCs, several inquiries arrived through the blog and social media, regarding interpretation of other outputs for predicting failure modes. Commenters wanted to know the values, for various result variables, that might define the upper and/or lower limits, and how one interprets results to determine part/process feasibility. This brought to mind the fact that stamping engineers spend a major portion of every week analyzing the results from the simulation that they run and looking for any failures.

By plotting the strains measured on a current panel against the FLC we can predict relative formability of that part or process

By plotting the strains against the FLC we can predict relative formability

This often means switching between several result variables and reviewing result color scales plots looking for values that exceed the limit for each variable. Precisely what values constitute failure, is something that we can leave for a later (and possibly contentious) post. What does bear mentioning, right now, is how we can streamline this activity of applying specified limits for key result variables. Continue reading

 

Adjusting complex process variables: Drawbead shapes

In Finite Element Analysis, it is common to treat the metal restraint due to draw beads as numerical factors applied along a curve or set of curves. The use of these factors makes defining boundary conditions simple and computationally effective.

  • Need to increase material flow? Decrease the restraint factor
  • Want to tighten up the material flow? Increase the restraint factor

The application of a line bead restraint offers efficiency as it eliminates or at least greatly reduces the computational costs associated with running 3-D geometrical beads and eliminates the need to make any CAD adjustments if additional iterations are necessary to arrive at a safe result.

Adjusting the forming of  a part may require editing of countless bead shapes

Adjusting the forming of a part may require editing of countless bead shapes

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Hemming feasibility

The Auto/Steel Partnerships finding on hemming High Strength Steel

The Auto/Steel Partnerships finding on hemming High Strength Steel

We had the honor to be selected as the computer simulation partner for a recent Auto/Steel Partnership (A/SP) study that considered the feasibility of using higher strength and advanced high-strength steels to achieve weight reductions of hemmed closure panels: project #AS-8004. One of the A/SP mandates is to research, develop, and promote steel applications to achieve the fuel economy goals of the North American Auto industry, rather than the use of materials like aluminum or carbon fiber.

  Outer material Inner material
Supplier A BH-280 0.55mm−       Bake hardenable grade 280 DC04 0.7 mm
Supplier B BH-440 0.55mm−       Bake hardenable grade 440  DC04 0.7 mm
Supplier C DP-490 0.50mm−       Dual-phase grade 490  DC04 0.7mm

While advanced high-strength steels, or AHSS, are used extensively in the body-in-white, applications for the exterior “Class-A” panels have been limited to mild steel and some dent-resistant classes of material (~210 MPa yield strength). For this project, the steel-making partners provided three developmental materials of higher yield strengths to see if parts produced from these grades could, in fact, achieve panels of acceptable quality.

A/SP project geometry was created to emulate problematic geometry from real closure panels

A/SP project geometry was created to emulate problematic geometry from real closure panels

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Generic CAD or Dedicated Surfacing tools for Die Face design

Fifteen years ago, AutoForm introduced a novel idea to the sheet metal stamping industry: AutoForm-DieDesigner, a sheet metal simulation integrated-draw die-face development product. With this product, we upended a long-standing process engineering paradigm, where draw die-face concepts had to be meticulously surfaced in CAD prior to import and meshing for FEA analysis—a time-consuming and arguably sub-optimal process.   For the first time, die-face development within the formability engineering environment allowed for rapid iteration and improvement cycles, reducing the time spent to develop tooling concepts while improving sheet metal quality and feasibility.

This paradigm shift created an entirely new work flow for stamping process method planning in which the engineering of the die face—die tip, binder curvature, addendum geometry—and process settings like draw bead configuration, blank shape, and binder force could truly be simultaneously engineered and validated. Starting with part data only, an addendum for draw and line die concepts are created, for immediate process validation and improvement when necessary. Rapidly iterating between die face creation and evaluation of the process engineering concepts, represented a significant improvement in engineering practices, enabling stamping process engineers to design optimized stamping processes. This shift—from a process using FEA simulation as a CAD design validation tool to one where simulation is used to drive die-face engineering—created a new challenge as well: “How do we efficiently transfer the feasible die-face concepts  into fully faced CAD objects?”

Rapid DieFace development during process engineering necessitates recreation of CAD/CAM ready surfaces

Rapid DieFace development during process engineering necessitates recreation of CAD/CAM ready surfaces

With AutoForm-ProcessDesignerforCatia, our customers—like Fontana Pietro, whose success story we shared in an earlier post—rapidly recreate machinable surfaces based on their AutoForm-DieDesigner concepts.  AutoForm-ProcessDesignerforCatia brings a new approach for rapid die face creation to the CATIA Environment, creating repeatable and reliable machine-ready die faces that translate directly to and from AF-ProcessExplorer for final validation and compensation.

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Die Cost Estimation Accuracy: Apples to Apples

A colleague shared this story with me:

At a demo of AutoForm-CostEstimatorplus, he performed a live tool cost estimate using two similar parts planned for production with similar processes at the same plant. The cost estimate for the first part was roughly 900k€ (900,000 euro). The prospective customer was impressed by the speed of the result, but pointed out that the actual cost of the tool was 600k€. My colleague pointed out that cost results are based on a estimation standard that might not reflect the local charge rates for labor and resources. He then showed the prospect the resource estimations—hours of engineering, machining time, construction effort, tryout time, mass of cast materials and estimated cost of purchased components. Given that additional information, the prospect agreed that the resource estimate was reasonable and once appropriate charges for resources were factored in the cost would be accurate.

Tool cost estimates should reflect the process plan, the manufacturing resource requirements of tools supporting the plan, and cost of the resources

Tool cost estimates should reflect process plan, manufacturing resource requirements, and cost of resources

My colleague then repeated the demo for the “sibling” component, arriving at a cost of roughly 900k€. The parts were very similar in size and design complexity and followed a similar stamping process. Naturally, it fits that the resource requirements should be the same. But when presented with these results, the prospect frowned and said that this second component cost 1.2 million €. Their explanation: The second component was for the luxury line, while the first one was for the economy model.

Two versions of the same part for two different vehicle lines, with a total cost of 1.8 million €. Would the two stamping processes really require so great a difference in tool manufacturing resources, or is the difference a post justification for the luxury model? Were the number quoted price or actual costs? Did this organization compare their price expectations to their true costs? Were costs reported to fulfill expectations?

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