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In this article we are going to discuss an advanced friction modeling technique in Abaqus. It is based on combination rules that allows solver to compute effective friction interaction based on two contacting surfaces with different coefficients of friction. As an example, look at the following table:

If someone asks: “what is the coefficient of friction of steel?” There really is no answer to this question. The answer really depends on the other object with which steel interacts. The table shows two different values, one for steel-steel interaction and other with steel-teflon interaction. If the user has NXM matrix of materials interacting with each other and each cell of that matrix has a friction coefficient assigned to it, then modeling in Abaqus is trivial. Define surface interaction with friction coefficient for each cell and use it with corresponding surface pair in the contact property assignment. The example below highlights it.

 

But this straightforward approach is possible only if friction values for all cells are available. However, at times only the diagonal values are available. That means all the non-diagonal cell values are unknown. In that case contact property assignment is not possible.

Abaqus now allows users to define friction as surface property as well. For two different surfaces (A,B) with individual coefficient of friction, the effective friction for pair is computed as follows:

The default value of alpha is 0.3. In case of mixed problems, where surface property and contact property methods co-exist, either method can take precedence. Look at following example.

The approach is an approximation but its worth in situations where user has no access to friction coefficients values for all the contact material pairs. This friction algorithm is available in Abaqus explicit 2018 release and beyond.

 

This topic has always been very popular and this problem has always been very complicated in FEA user community since the inception of Abaqus, or any non-linear FEA code in general. In this brief article, I will highlight few simulation situations where Abaqus standard may not be a good candidate from convergence perspective. Identifying these situations early during pre-processing and working in Explicit right away may save lots of time and efforts that otherwise would be wasted in trying Abaqus Standard.

  • Look at the motion aspect: We always say that simulation is not the complete replacement of physical testing right away. In the beginning physical tests play a critical role in identifying right approach for simulation as well as in data correlation between physical and virtual tests. Look closely at the physical test. Is there a large relative motion between different parts involved? If yes, then Standard is very likely to face convergence problems, even if problem is static by nature. Standard has an option of “small sliding” and “finite sliding”. But user should remember the difference between “finite sliding” and “large sliding”. Attached is the video of wire crimping simulation that ideally is a static problem but numerically not a good candidate for Standard, primarily because of motion.
  • Clock time matters: Apart from magnitude of motion, the duration of motion matters as well. While looking at physical test, closely look at the time in which motion is completed. If too much of motion is covered in too less time, problem is indeed dynamic instead of static as inertia effects cannot be ignored. In such a situation either Standard dynamics or Explicit would be the right way to go. Which one to choose really depends on event duration. If a lot of dynamic phenomenon happens in the order of milliseconds or microseconds, Explicit is only option for this candidate.
  • Is there a severe discontinuous contact: In the status file of Abaqus Standard, there is an undesirable column called SDI’s. It’s called severe discontinuous iterations and too many of these often always leads to convergence nightmare. The reason of SDI’s is discontinuous contact, also known as “chattering”. It’s a phenomenon in which nodes between two bodies in contact continuously change their contact status from OPEN to CLOSE from one iteration to the other as analysis proceeds. If chattering occurs due to modeling errors, it can be corrected but at times discontinuous contact is the nature of problem itself. In such a situation, explicit is the only approach to be taken, even for long duration events with respect to physical time. The attached video is an example of a dynamic event that would only solve in explicit or multi body dynamics, primarily because of severe discontinuous contact.
  • Is there too much Plasticity: Abaqus has material models to capture plasticity but there is a limit on the magnitude of Plasticity Abaqus Standard can handle. If the permanent deformation becomes so high that underlying part completely loses its load carrying capacity then Newton Raphson method of Abaqus Standard would not be able to establish equilibrium and further leading to non-convergence. Ideally, there is no further need to perform simulation as it’s a classic situation of part failure but if further simulation is needed, it should be continued in Explicit using Restart options.

My last blog focused on the need for a Manufacturing BOM (mBOM). When organizations start to embrace the value of mBOM and  decide to invest on solutions to manage a mBOM, the first question is where to master it , PLM or ERP ?

The answer to that question varies depending on the maturity level of PLM and ERP adoption and penetration in the organization .  If both PLM & ERP are at the same or similar maturity level, then there are many good reasons to author & manage mBOM in a PLM system and to make ERP a consumer of the mBOM mastered in PLM.

First, in PLM mBOM is integrated with the eBOM and design process . eBOM integration and reuse enables front loading, and helps manufacturing team to lower cost of mBOM authoring and management and shorten time to market.  Manufacturing users can also leverage the 3D visualization data in mBOM for better decisions and  better quality. With the master model approach being adopted by leading organizations, there is lot of Product Manufacturing Information (PMI) on the 3D Master Model, which can be leveraged in both mBOM and downstream process planning.  mBOM can also act as the starting point for detailed process planning to create the Bill of Process (BOP) inside PLM . BOP or Routing can also leverage the 3D visualization data to produce visual work instructions , which will always remain updated with the upstream design changes. The process plans can  also be simulated and validated (feasibility, human ergo, collision etc) before actual execution.  The validated Routing then get sent to Manufacturing Execution Systems (MES) along with the visual work instructions. That way there  is full traceabilty from CAD to eBOM to mBOM to BOP and eventually to MES.

The traceability enables users to run where used queries among all products and plants during a change process. This ensures all product changes are evaluated for impacts in both engineering and manufacturing contexts.

 

In previous blog articles on 3D Experience simulation roles, we primarily discussed platform configurations, concept of personas and roles as well as simulation capacity of the platform. In this blog article contains detailed information about three primary structural simulation roles: MDS, DRD and SMU.

To begin with, lets recapitulate that simulation roles are categorized in groups based on personas of users working on such roles. In terms of complexity and functionality, offerings range from based to intermediate to advanced.

 

Engineer profile: The is the simplest and easiest to use simulation offering primarily meant for designers with low to intermediate simulation knowledge. Their primary job is product design and they perform simulations very occasionally. Roles for this profile are CAD centric and are associated with a guided workflow. Simulation tokens are embedded in the role.

Analysis engineer profile: This profile is one level above the engineer profile and is suitable for structural analysis engineers associated with product engineering. Their simulation knowledge is of intermediate level which means they understand simulation process in terms of meshing, BC, Loads, result visualization etc. but don’t have any hands-on experience of advanced simulation tools. Usually there is no guided workflow. Simulation tokens are embedded in the role.

Analyst profile: This role is for full time analysts who primarily perform intermediate to advanced level simulations. They have in depth expertise in at-least one simulation domain and often hold Masters or Doctorate level credentials. This role requires extensive knowledge of pre-processing, solver terminologies such as statics, dynamics, non-linearity, convergence schemes, as well as post processing etc. There is no guided workflow. Simulation tokens are procured separately.

 Research Specialist profile: This is a complex simulation offering primarily for experts who develop novel simulation workflows and processes. The simulation requirements often span across multiple physical domains and involves advanced Physics such as vibrations and noise. The pre-processing aspect may include complex meshing of assemblies and assemblies of meshes.

Let’s look at one role from each of first three profiles:

Stress Engineer role (MDS)

 

It’s a role from engineer profile and has a guided workflow. The snapshot shows apps available in MDS role. It performs routine strength and deflection calculations under static loading conditions. It can also compute product fatigue life for very simple loads. The CATIA and SOLIDWORKS associativity is well maintained. Local solver execution up to 4 cores is included.

Structural analysis Engineer role (DRD)

 

It’s a role from analysis engineer profile that has no guided workflow. It is used to access the structural integrity of products subjected to wide range of loading conditions. The snapshot above shows available apps in this role. It works on MSR concept available in advanced simulation tools i.e.  Model-Scenario-Results. Many advanced settings are exposed to the user. This role can perform multi step simulations. Local job execution of up to 8 cores is available.

Mechanical Analyst role (SMU)

 

It’s a role from analyst profile and it does not include a guided workflow. The snapshot above shows available apps in this role.  It uses advanced finite element techniques to simulate and validate complex engineering problems. It offers multiple advanced meshing techniques such as Octree, surface, sweep and RBM. Both single step as well as multi step scenarios are included. Supported analysis steps include static perturbation, non-linear static, frequency, buckling, implicit dynamics, explicit dynamics, steady state heat transfer, transient heat transfer etc. Most of the non-linear materials and complex engineering connections are included.

While we discussed one prominent role from each profile, the south quadrant of 3D Experience platform offers numerous simulation roles. To know more, please contact us.

Embracing a true PLM platform and solution is not an easy endeavor for many companies, even with the reckoning of the potential value and ROI offered by a rightly architected PLM solution.  Success in any Enterprise software implementation like PLM often requires careful planning, dedicated resources , right technical expertise, executive sponsorship, and a receptive culture, among other things.  When done the right way the results of such efforts are transformational, producing significant business benefit which can be measured and validated.

One of the biggest challenges to adopting PLM is organizational change management given the breadth and scale of a true PLM solution . Many companies approaches it in phases and rightly so; but the key is how the phases are architected, tracked and measured.  PLM involves managing and linking Data, Processes  and People together as the product goes through it’s lifecycle from inception to design to manufacturing to support and eventually end of life.   The first step of this is often managing Data; specifically Engineering CAD data.  Most solutions start with a way to vault the CAD data along with some basic part numbering schemes and revision rules . Sometimes engineering documents are also vaulted along with the CAD data.   Yes data  vaulted in a central repository brings  lot of benefits like elimination of duplicates , basic check-in-checkout / access controls and  added search capabilities as opposed to it scattered across multiple locations.  But the measured value of this alone may not substantiate the heavy PLM IT investment companies needs to make for a true scalable PLM platform.   Sometimes there is an expectation misalignment on the full PLM value and just the data vaulting value . This at times sends companies to a long and lull “PLM assessment” period  after data vaulting.  Sometimes cultural resistance or organizational change overturns any momentum.  Maybe a technical glitch or integration shortfall previously overlooked becomes a deal breaker . Over-scoped and under supported initiative can also run out of money or time.

Companies make a considerable amount of IT investment on the PLM platform upfront, so that they have a scalable solution for all phases and not just CAD vaulting.  Most of the time they can add more capabilities and processes on the PLM platform without additional IT investments .  So it’s very important to get past the initial data vaulting phase and move to the next phases to maximize the utilization of existing IT investments.  Now the question is where do we go after CAD vaulting. This is where upfront PLM Roadmap definition is so important in terms of  how the phases are architected, tracked and measured.  For companies who have successfully completed data vaulting but do not have a formal PLM Roadmap defined yet, some of the next focus areas to consider can be Engineering process management, BOM Management,  Change management , Requirements management , Project and Program management , in no specific order.

One thing common between SIMULIA roles of 3DExperience platform and the standalone Abaqus products is that both require an Abaqus solver to perform computations. It further means that both solutions require Abaqus tokens to complete or speed up the computation part of the simulation. For standalone abaqus product, we know that the calculation is straight forward. Abaqus requires a minimum of five tokens to execute a single core non-linear job. Large models require more number of cores to solve in real time and more number of cores require more tokens as follows:

The computation capacity of 3D Experience platform, however, cannot be defined by a single equation. Unlike Abaqus solver, that is available as an integrated all-in-one license for all types of simulations such as standard, CFD, explicit etc., 3D Experience offerings are in form of roles. Each role is a sellable license that includes either some or all Abaqus solver capabilities. Offers are made further flexible by on premise vs. on cloud offerings. Let’s have a look at solver offerings in different configurations and roles.

    Engineer role vs. Analyst role

While most of design engineer roles have embedded Abaqus tokens, most of the analyst roles do not have any compute capacity at all. The number of tokens embedded in designer role depends on the level of simulation complexity a role can accommodate. For example

Stress Engineer role has 8 embedded tokens to accommodate up-to 4 cores job

Structural analysis engineer role has 12 embedded tokens to accommodate up-to 8 cores job

It is possible to submit jobs on more number of cores than what embedded solver permits but in that situation external tokens need to be utilized and embedded solver takes no credit at all.

                Tokens vs. Credits

In case of analyst roles such as stress analyst, fluid mechanics analyst etc., the role itself does not have any compute capacity which should be procured either in the form of tokens or credits. Tokens are renewable form of compute capacity which means they can be used over and over. 3D Experience uses tokens in a very similar fashion as does standalone Abaqus. The token consumption with respect to number of cores is the same for Abaqus as for 3D Experience platform. On the contrary credits are a non-renewable form of compute power. It means that credits, just like the talk time over phone, can be consumed only once.

               Why credits at all!!!

In general credits is an expensive preposition for customer but there are exceptions. Credits are utilized to meet unexpected and rare increase in peak usage. This is somewhat more common in engineering consulting firms that can face high demand of simulation capacity due to influx of many short duration simulation projects at any time. To meet this sudden spike in demand, one-time credit bundle offering makes more sense than increase in perpetual tokens. Once peak demand is over and credits are consumed, simulation capacity is returned to normal levels.

On premise vs. on cloud

Design engineer as well as analyst roles are available in on premise as well as on cloud formats. There are three ways of utilizing cloud resources: store the models on cloud, stores the results on cloud and solve on cloud. The first two offerings require only cloud storage and are available at no additional charge with cloud based license. However, the third offering requires cloud compute resources that consumes compute credits in addition to cloud based license.

Need to know more about SIMULIA 3D Experience platform compute capacity! Please approach us and we are ready to help.

 

Does your organization struggle to produce CAD and digital definitions of product? Is the CAD development of product a bottleneck in your process? If the answer is yes, you could benefit from a Digital Engineering Benchmark.

The Digital Engineering Benchmark assessment captures the opinions of senior and knowledgeable personnel in your organization on the current state and future Digital Engineering requirements for your business. In addition, a priority for improvement and an assessment of current effectiveness is recorded. It centers on 17 key Digital Engineering “Pillars” ranging from 3D CAD Standards, through to CAD Extensions. The pillars are listed below:

  1. 3D CAD Standards
  2. Drawing Standards
  3. CAD Templates
  4. 3D Standard Features
  5. Standard Parts Library
  6. Materials Library
  7. Automated Drawing Generation
  8. 3D Master
  9. Automated Designs
  10. Automation Scripts
  11. Digital Mockup
  12. Spatial Analysis
  13. Special CAD Extensions
  14. Design for Manufacturing
  15. CAD Checking Tools
  16. Intellectual Property Protection
  17. Publications

After the 17 pillars have been covered, senior and knowledgeable personnel are also invited to “spend” an assumed benefit in value areas within your business. The areas identified are improving time to market, increasing the portfolio of the company and improving product quality.

Finally, the tool produces a comprehensive report showing the customer’s current state of maturity and a benchmark comparison with the industry.

Participants have found this process to be very useful as it allows them to prioritize their initiatives, gives a high level view of their roadmap to success and provides them with industry benchmark information

Organizations invest huge sums of money in simulation software to avoid expensive and disruptive physical testing processes. But how long it really takes to make this transformation happen! One thing is sure; it does not happen in a day. The flow chart below explains the reason pictorially. The last two blocks “compare and improve model” and “compare and improve theory” make this transformation a longer process than expected.

 

Let’s explore the reasons behind it. Comparison is needed to make sure that simulation results mimic the physical testing results before latter can be discarded, partially or fully. The difference in results can be due to three main factors: lack of user competency, limitation of software used, lack of sufficient input data.

Lack of user competency: FEA analysts are not born in a day. The subject is complex to learn and so are the software associated with it. The ramp up time really depends on analyst background along with complexity of problem being simulated. Organizations usually make a choice between hiring expert and expensive analysts who can deliver the results right away or producing analysts of its own through class room and hands on trainings. First option saves time while the second saves money. CAE software development companies are also making big stories these days by introducing CAD embedded simulation tools that require nominal user competency. Nevertheless, the competency builds up over time.

Limitation of software used: Initial investment in simulation domain is usually small. It means two things: either number of users are less or software functionality is limited. With time, complexity of problems goes up but the software remains the same. A common example I have seen is of a customer starting with simple linear simulation workbench in CATIA and over period trying to simulate finite sliding contact problems with frictional interfaces in the same workbench. Users don’t realize that their problem complexity has exceeded the software capacity to handle and it’s time to upgrade. It’s always recommended that analysts get in touch with their software vendors whenever they anticipate an increase in simulation software capacity or functionality. A certified simulation software vendor is a trusted advisor who can really help.

Lack of sufficient input data: “Garbage in – Garbage out” is a very common phrase in simulation world. However, at times it is very difficult to get the right input for software in use. The complexity of input data can arise either from complex material behavior or from complex loading conditions. Example of complex material may be hyper-elasticity or visco-elasticity observed in elastomeric materials. Examples of complex loading may be real time multi block road load data to estimate fatigue life. Sometimes simple metallic structures exhibit complex behavior due to complex loading. Examples are high speed impact or creep loading. With time many material testing labs have come into existence that can perform in house testing to provide right input data for simulation.

Conclusion: You will come out of the vicious loop of physical and simulation results comparison after couple of iterations if you have three things in place: right people, right software product and right input data. If you need help in any of the three aspects, we are always available.

Anyone who has dealt with Bill of Materials (BOM) knows about the challenges and complexities involved with it. Sometimes we get asked, managing a single BOM itself is cumbersome, then why do we even need another one in the form of a Manufacturing Bill of Material (mBOM)..?

What we have seen with our customers  is that, when there is only one BOM then it is usually owned by the engineering department (CAD BOM/ eBOM) and will be available for  the Manufacturing Department as  a “read only”. This is not good enough for the manufacturing teams as they need to author and add data specific to manufacturing , for example  manufacturing specific consumable parts like glue, oil or Tool Fixtures and such. Another key factor is how the BOM is structured; typically eBOM is structured around organization systems and functions and represent the product architecture, but for manufacturing team a mBOM needs to be  organized according to the manufacturing assembly order.

When customers need to work towards the industry 4.0 goal, they need to have  smarter manufacturing  solutions and systems that provide more ways to capture the manufacturing business intelligence and then suggest solutions based on the previous patterns. With this in mind they need to invest in  manufacturing BOM authoring and management area. During a mBOM adoption, the key is not to recreate the data that’s already in eBOM, but to reuse the eBOM and add additional information specific to manufacturing. That way there is both reuse and traceability of the data.

At a high level mBOM creation automation solutions exist in multiple flavors

  1.  Recipe based mBOM:  In a recipe based mBOM, customers can initiate the mBOM creation via pre-configured  templates pointing to eBOM. Based on the recipe stored with the template it will automatically fetch the engineering parts into mBOM. This kind of solution helps customers who have heavy standardization in their product offerings.
  2. Reusable Manufacturing Assembly: In such a solution, customers can leverage the same manufacturing assembly across multiple product lines to reduce the design, development and procurement costs
  3. New Offline Processing Solutions: This approach is to tailor the mBOM creation process and application to the customer need using customization. This standardizes and automates the process to capture the business intelligence and its reuse via customization.
  4. Smarter Validations: Such solutions suggests what’s next to the business users, that way users spends less time discovering the problem and more time solving it.

Over all value of such solutions is not just the flexibility it offers the manufacturing team, it also reduces manufacturing process planning and execution lead time with improved structure accuracy and significant reduction in change reconciliation processing time.

More often PLM starts as a CAD/Design data vault for many companies, later evolving to a design data exchange platform .  Most successful companies are taking PLM beyond just a design data exchange and access control platform; to a knowledge driven decision support system.  This means PLM not only needs to manage the multitude of information generated at various stages of the product lifecycle , but also capture the product development knowledge and feed it back to the product lifecyccle. For example, the requirements and design for a newer version of a product  needs to be also driven by the knowledge elements captured from the previous version’s lefecycle, from inception to design to manufacturing and service.

When PLM stays just in the Design Engineering world, it’s constrained to exchange information and capture knowledge from downstream stages managed by disconnected, silo based systems. This results in engineers spending huge amount of time in data acquisition tasks. Industry studies shows that information workers spend 30-40% of their time only for information gathering and analysis, thus wasting time in searching for nonexistent information, failing to find existing information, validating the information or recreating information that can’t be found.

Quality escapes is another challenge with such disconnected systems when product doesn’t confirm with the engineering definition. Non-conformances found on the shop floor  are costly to review and dispose and even more severe when the product is already on service. Reconciling change is also extremely challenging, especially its downstream propagation, resulting in significant productivity losses. Slow change processing along with quality escapes cause delays in new product introduction affecting the overall ability of the companies to compete.

The first step towards transforming PLM to a true knowledge driven decision support system is to extend it to the CAD/CAM/CNC process chain, thus taking it to the shopfloors. Such a solution helps to establish a  continuous loop from Engineering into the shop floor for operations management and manufacturing execution systems (MES). Such a continuous loop system provide more ways to capture the business intelligence and then suggest solutions based on the previous patterns. Then it’s much easier to capture information and use analytics to synthesize valuable knowledge elements compared to the fragmented solutions many companies have today.  It’s also a foundational element for establishing a Digital Twin per Industry 4.0 vision

 

Other key benefits of extending PLM to manufacturing include

Reducing the time to market

  • Enhanced collaboration between Product and Manufacturing Engineering
  • Enhanced Traceability and Faster Change Management

Enhancing Flexibility

  • Manufacturing plans comprehend product variability/complexity
  • “What if” scenarios for optimized decision making

Increasing Quality

  • Manufacturing Simulation and validation integrated in PLM
  • Up-to-date 3D work instructions delivered to the shop floor

Increasing Efficiency

  • Ongoing process optimization based on Closed loop feedback of utilization data
  • Reuse of common methods/tooling

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