Kimberly-Clark Wins with Simulation Lifecycle Management
The products we design at Kimberly-Clark may be feather-light, but they’re as demanding to develop as any bike frame or truck chassis. Our company has always focused on thin, “soft” products, from paper and newsprint in the 1870s to today’s multi-billion dollar global brands — Huggies®, Kleenex®, Scott® and Depend®, among others. Unlike the newsprint of yesteryear, however, these products often depend on accurate, comprehensive simulation to ensure optimum performance.
The materials we use now (paper, cloth, polymers, and custom composites) can be highly nonlinear, anisotropic and undergo large strains while in use. Predicting the complex deformation of our products as they interact with people and the surrounding environment generates an immense volume of simulation files and data. In recent years this had built into a daunting challenge: How could we explore, manage, and re-use it most efficiently?
The data we needed to organize was not just high-volume; it was decentralized between a CAE modeling team in Neenah, Wis., and analysts in Roswell, Ga., and Seoul, South Korea. Initially we tried to administer everything with simple methods such as defined file structures and naming conventions, but it was still difficult to find and confirm the latest version of a simulation. Clearly, we needed robust data management if we wanted to track and review previous analyses.
Finding SLM
Since we already used Abaqus as our primary FEA software, it made sense to approach SIMULIA about implementing its Simulation Lifecycle Management (SLM) solution. With SIMULIA’s help, we went through a checklist of our criteria: easy configurability, out-of-the-box capability, data search and retrieval, compatibility with our existing hardware and infrastructure, and customer support.
The new SIMULIA solution clearly offered us a way to streamline our processes, automating the steps that didn’t require expertise and freeing analysts to focus on the simulations themselves. We designed our analysis process so that it would be scalable and as efficient as possible.
Building a Simulation Cycle
To organize our analyses, Kimberly-Clark used SLM to set up a series of process templates, all based on a simple format for building models, running them, and post-processing the results. Our system works like this:
Someone brings us a proposed design of a diaper, for example. Ideally they have already created a digital model and specified a material; that gives us the data we need to go forward.
Next we combine the diaper geometry and the material characteristics with a human torso simulation. We can re-use a virtual human from the existing SLM library and, increasingly, we can modify existing 3D product designs and material characteristics files to create a full, well-defined model for analysis.
The analyst also chooses an SLM template that most closely fits the task. Each is based on a generic template of our workflow: Build Model, Run Model, and Auto Post (Process). One option is selecting a template that allows a Restart Analysis (to consider multiple-use cases or to change loads, interactions, and boundary conditions). During the Build Model phase, SLM can automatically load the required model into Abaqus/CAE and extract data from input files, based on file import and export rules defined by the user. Outputs from the Build Model phase include the completed model file for running and the Abaqus input file(s).
Run Model manages the actual analysis. Because SIMULIA SLM is adaptable to multiple template formats, the analyst can re-configure the template for incremental changes in workflow that best suit the current simulation. In the case of the diaper, there may be gasket components that need to be motion-tested to ensure that they don’t “gap away” from the skin and create a pathway for leakage. There are many different stresses and loads on the material, and a number of shape changes to be considered as the human body moves. The SLM application automates the analysis and can run it on a remote cluster for maximum computing speed.
Auto Post returns the results to the analyst’s workstation and, using Abaqus Viewer and either a fully automated, semi-automated, or user-interactive procedure, creates output in forms defined by the template for the convenience of the end-user, normally a product developer.
Multiply this single example by thousands of simulations, and you’ll see why SLM has radically transformed and simplified our analysis methodology.
From Months to Minutes
Now we can perform more tasks at once, confident that the software is automatically tracking changes and versions accurately and organizing revisions for later review and use.
SIMULIA SLM has helped us speed up our modeling through process standardization and greatly improved search capabilities. Formerly, developing and assembling a complex model could take up to a month. With the tools and techniques we’ve developed and the data libraries we’ve accumulated, we can build these models in minutes.
But our greatest time savings isn’t in the multi-tasking, nor in the FEA simulations we’ve streamlined. It’s in the ones we don’t have to repeat at all, because a previous analysis is available with pedigreed data and retrievable results.
With our entire team having transitioned from our old tool set, we see additional potential opportunities for using SLM. We plan to integrate it with our home-grown code and with other simulation software. We’ll also continue to scale up SLM, adding more users and compiling more data. Now that we have efficient simulation data management, our huge libraries of information have gone from a daunting challenge to a treasured, time-saving resource. Many Kimberly-Clark products may be disposable but, with SLM, our analyses of them can now be recycled. We feel this offers us significant cost and time savings in our product development.