Ensuring Seamless Collaboration in Food & Beverage Industry

Posted on June 28, 2021October 4, 2024 by Ritesh Narula

Innovation is very vital for the growth of any company. Anticipating and meeting the needs of consumer creates consumer trust and brand value. Behind a successful brand, there is a team of many individuals contributing with their expertise. However, these efforts from the team members are more often fragmented and results in lower performance and profitability.

Individual departments perform tasks in silos and miss out on connecting with the other departments, which leads to a lot of non-value-added time.

The food & beverage industry, in particular, is facing the below challenges:

Coping up with health & wellness trends – There is a surge in demand for healthier products in the times of COVID-19. For companies, it has become a mandate to expand and modify the existing portfolios to support the demand for products that are natural, botanical & contain plant-based proteins. The formulation needs to be altered as the focus has changed from food to nutrition. Reduction of added salt, sugar, and fats is a must, and there is a need to come up with foods that nourish each body type.

Ever changing consumer behaviour – The recent trends like an inclination towards Hyper-Local food and alcohol-free beverages are here to stay. Consumers are looking for new ways to engage with brands and aspire for products that could reflect their unique identity. Market needs are shifting faster than ever before, and companies must adapt quickly to deliver meaningful innovation as per the changing consumer preferences.

Pressure of earning profits – There is growing pressure from investors to drive sustainable growth and increase profits. Also, the pressure of rising commodity and resources cost needs to be managed. Fragmented IT infrastructure and declining IT budgets are forcing companies to look for more efficient solutions.

Production getting complex – Demand for more personalized products is leading to an increase in SKUs. Also, there is a pressure of coming up with new assortments with more channels to serve. Managing production complexity to provide the right product at the right time is critical.

However, not all companies have the means & resources to manage these challenges in the most efficient way. It is high time for food & beverage companies to adopt the “digitalization” for product development to take innovation and profits to the next level.

The benefits of going digital in the food & beverage industry are as described below:

Seamless Collaboration – In the food and beverage industry, the multiple teams need to collaborate to bring new ideas, products and artwork to market. Gone are the days when companies could afford the inefficiencies and losing market opportunities from disjointed product data. The need of the hour is to tie product innovation, development and commercialization processes together with a holistic platform that provides multiple applications for continuity and connectivity of information, people, and processes.

Developing a successful product that excites consumers requires an integrated approach across formula, packaging and labelling design. Unfortunately, most companies support the development process with a patchwork of point solutions that are stitched together but not integrated, resulting in poor efficiency, product launch delays, and missed customer expectations.

For an organization to manage its work smoothly, the disconnect between departments needs to be dealt with so that everyone has access to accurate and updated information in real-time on a single platform.

Integrated Project Management & Clear Business Dashboards – Launching a new product to market is as fast as your slowest link is. Typically, the project managers who are deprived of the right system need to refer multiple systems and manually collect and collate project statuses from different stakeholders. With the adoption of an integrated project management capability tool, project managers can focus on actual project management activities like evaluating the project progress, identifying and reducing risks, and aligning resources to maintain or accelerate schedules.

When project statistics, assigned tasks, dependencies, risks status, timelines, financials, meeting minutes are all contained on a platform with established processes, managers can improve project execution and performance.

Competing for new opportunities and growing faster than competitors requires real-time data and the insights it provides. Companies are pursuing greater speed, scale, and simplicity across every area of their operations. Real-time data is the catalyst making these three goals and many others possible to achieve.

Immersive Virtual Reality – The need of the hour is to enable food & beverage companies to create high-end visualizations for interactive marketing and sales experiences. It is required to quickly create unique products, digital assets, images and videos to engage the customers with interactive and personalized product experiences. The companies also need to virtually bring together Brand Manufacturers, Artwork Studios, Agencies and Packaging Suppliers to accelerate breakthrough designs. With this, companies can shorten package design cycles, reduce costs, lower the possibility for production issues and virtually eliminate recalls.

The era of static 2D planograms is nearing its end. By realistically simulating immersive, lifelike 3D store environments, food & beverage product brands and retailers can better imagine, validate and deploy winning shopping experiences while increasing product appeal, in-store execution, compliance, brand & category profitability and equity all while setting themselves apart from the competition.

Eliminate the Prototyping – In the modern design era, leading digital tools introduce modeling and simulation into the design. These tools enable innovators to focus on developing the ideal solution for the consumer. Automating the impact analysis of design decisions gives them the ability to iterate more freely.

To create better product and package designs, expensive prototyping can be eliminated through modelling and simulation. By using digital tools, identifying and eliminating mistakes in the digital stage is done, when changes are least expensive. Companies can digitally model and simulate the product, before committing a dime in the physical world.

To sum up, food & beverage companies need to adopt digital solutions to take innovation and profits to the next level. All the key stakeholders in an organization need to be empowered to make the right decisions by having access to the project insights. These insights must be made available in real-time so that it can be used effectively by the stakeholders of the organization. Informed decisions are always better than snap decisions.

Large Assembly Management in CATIA V5

Posted on June 23, 2021September 19, 2024 by Md. Shahnawaz Ahmed

Working with large assemblies in the CATIA V5 system can be very demanding. Even with the use of extremely powerful machines and workstations, working with large assemblies often leads to the crashing of the system with the error message “Click OK to terminate” appearing. To avoid this error, this blog discusses some recommendations for optimizing the system to minimize the crashing of the program and to make it easy to work with large assembly sets.

Cache System
System’s performance can be increased with the help of cache. When this option is activated, CATIA loads all parts of the set in visualization mode while not loading the whole history of the part. This helps in reducing the load on computer/system memory.

To activate Cache System, click on Tools ➜ Options ➜ Infrastructure ➜ Product Structure ➜ under Cache Management tab, click “Work with the cache system.”

After restarting the program, CATIA will reload parts in visualization mode. If the part needs to be edited, switch to Design Mode which can be done by right clicking the part and selecting Representations and Design Mode.

CGR Management
For large assemblies, CGR formats can be optimized. To optimize CGR formats, click on Tools ➜ Options ➜ Infrastructure ➜ Product Structure ➜ CGR Management tab.

Display Option
The changes in display options can be made in Performance Settings tab which will improve the results.

Click on Tools ➜ Options ➜ General ➜ Display ➜ Performance tab.

It is recommended to turn off Occlusion Culling and set 3D Accuracy to 0.1 (increase in value improves performance), increase Level of Detail while Moving (increasing the value improves performance), increase Pixel culling while Moving (increasing the value improves performance).

Disable Automatic Saving
By default, data is saved every 30 minutes in CATIA. System usually slows down while saving. The automatic data saving can be turned off by clicking Tools ➜ Options ➜ General tab and turning on No automatic backup in the Data Save settings.

Stack Size
The total number of “Undo” operations assigned to the CATIA session is the stack size. Reducing this number increases the memory capacity and thus the performance. Stack size can be changed by clicking on the PCS tab in the General menu.

Product Visualization Representation
The memory utilization will improve if sets are open in such a way that all components are deactivated and subsequently activated as needed. To change this setting, Do not activate default shapes on open option needs to be enabled within the Product Visualization (Tools ➜ Options ➜ Infrastructure ➜ Product Structure) menu.

Enforcing Contact Constraints Using Penalty Contact Method in Abaqus

Posted on May 25, 2021September 19, 2024 by Vignesh Pagadalu

There are two primary methods through which normal direction contact constraints can be enforced in Abaqus/Standard: the traditional direct Lagrange multiplier method and a penalty-based method. The fundamental difference between the two methods is that the Lagrange multiplier method exactly enforces the contact constraint by adding degrees-of-freedom to the problem while the penalty method approximately enforces the contact constraint through the use of “springs” without adding degrees-of-freedom. The penalty method is depicted schematically in Figure 1. The lower surface is the main node and the upper surface is the secondary node. While the overclosure has been exaggerated, it is clear that the spring of stiffness k_p resists the penetration of the secondary node into the main surface.

A large class of problems exists where the extra accuracy that is possible with the Lagrange multiplier method is not consistent with the approximations that are made (i.e., coarse meshes). Often times in these problems, adequately capturing load transfer through the contacting interface is more important than precise enforcement of the zero-penetration condition. The penalty method is attractive in such applications because it is usually possible to trade off some small amount of penetration for improved convergence rates.

The penalty method implementation in Abaqus attempts to choose a reasonable penalty stiffness based on the underlying element stiffness. If the default penalty stiffness is not suitable, options to scale the penalty stiffness are available. It is also possible to prescribe the penalty stiffness directly. If the scaled or user-prescribed penalty stiffness becomes very large, Abaqus automatically invokes special logic that minimizes the possibility of ill-conditioning. Advantages and disadvantages of each method are listed in Table 1.

Table 1: Direct Lagrange multiplier vs. penalty method
Direct Lagrange multiplier contact		Penalty contact
Advantages	Disadvantages	Advantages	Disadvantages
· Exact constraint enforcement (zero penetration) · Easy to recover contact forces ·No need to define contact stiffness	· Larger system of equations · Difficult to treat over constraints · Sensitive to chattering	· Number of equations does not increase · Easier to treat over constraints	· Approximate constraint enforcement (finite amount of penetration) · Difficult to choose proper penalty stiffness

Linear and Non-linear Penalty Stiffness

With the non-linear penalty stiffness approach, the penalty stiffness has constant initial and final values; these values serve as bounds for an intermediate overclosure regime in which the stiffness varies quadratically. A schematic comparison of the pressure-overclosure relationships for the linear and nonlinear penalty methods is given in Figure 2:

The various parameters used for defining the non-linear pressure-overclosure relationship are given below:

K_lin– Linear stiffness used for linear penalty contact. The default value is 10 times a representative underlying element stiffness.
C₀– Clearance at which contact pressure is zero. The default value is zero.
K_i– Initial stiffness. The default value is 1/10 of the linear penalty stiffness.
K_f– Final stiffness. The default value is 10 times the linear penalty stiffness.
d – Upper quadratic limit. The default value is 3% of the characteristic length computed by Abaqus/Standard to represent a typical facet size.
e – Lower quadratic limit. The default value is 1% of the characteristic length computed by Abaqus/Standard to represent a typical facet size.
e_r= e/d – Lower quadratic limit ratio. From the default values of parameters d and e, the default value for e_r is 0.3333.

The default values for these parameters are based on the characteristics of the underlying elements of the secondary surface. For two elements with dissimilar materials in contact with each other, the contact penalty stiffness value chosen will be based on the material stiffness of the softer material. User control for changing the default values is provided. The nonlinear penalty method has the following characteristics:

A relatively low penalty stiffness is used while the contact pressure is small. This serves to reduce the severity of the discontinuity in contact stiffness when the contact status changes.
The smooth increase of the penalty stiffness with overclosure helps avoid inaccuracies associated with significant penetrations without introducing additional discontinuities.

The low initial penalty stiffness typically results in better convergence for problems that are prone to chattering with linear penalty contact, and the higher final stiffness keeps the overclosure at an acceptable level for problems with high contact pressure. Nonlinear penalty contact tends to reduce the number of severe discontinuity iterations due to a smaller initial stiffness; however, it may increase the number of equilibrium iterations due to the nonlinear pressure-overclosure behaviour. Hence, it cannot be guaranteed that nonlinear penalty contact will result in a reduction of the total iteration count compared to linear penalty contact.

As discussed above, Abaqus attempts to choose reasonable penalty stiffness values based on the underlying element stiffness. Experience has shown that for stiff or blocky problems the default penalty stiffnesses chosen by Abaqus produce results that are comparable in accuracy to results produced using the direct Lagrange multiplier method but usually at less expense in terms of memory and CPU time. Experience has also shown that for bending-dominated problems the default linear penalty stiffness can often be scaled back without any significant loss of accuracy. Furthermore, scaling back the penalty stiffness for bending-dominated problems has been seen to sometimes dramatically increase the convergence rate. These experiences are demonstrated by the following examples.

Example: Hertz contact

This example consists of two elastic cylinders in contact as depicted in Figure 3. The node-to-surface formulation with matching meshes was used. Cases were run using the direct Lagrange multiplier method, the linear penalty method with default stiffness, and the linear penalty method with default stiffness scaled back by two orders of magnitude. Results from the three cases are presented below in Table 2.

As expected, the direct Lagrange multiplier case produces zero penetration while the cases that use the penalty method produce finite penetrations. In this example, scaling down the penalty stiffness by a factor of 100 results in a 55x increase in the penetration. It can be seen that for this example the default penalty stiffness predicts a peak stress that differs by only 1.5% from the peak stress that is computed using the direct Lagrange multiplier method. If the penalty stiffness is scaled back by a factor of 100 then the predicted peak stress decreases considerably and differs by 47% from the peak stress that is computed using the direct Lagrange multiplier method. The significant decrease in peak stress is due to the combination of displacement controlled loading and the compliance at the contact interface with the penalty method.

Table 2: Hertz contact results

	Penetration	Peak Stress
Direct Lagrange	0	1.201E5
Linear Penalty, default stiffness	4.482E-6	1.183E5
Scaled Linear Penalty, S_f = 0.01:	2.492E-4	6.334E4

Example: Bending-dominated contact

This example consists of a three-point bending test of an elastic-plastic beam as depicted in Figure 4. The node-to-surface formulation and half symmetry have been used. Cases were run using the direct Lagrange multiplier method, the linear penalty method with default stiffness, and the linear penalty method with default stiffness scaled back by two orders of magnitude. Results from the three cases are presented below in Table 3.

As expected, the direct Lagrange multiplier case again produces zero penetration while the cases that use the penalty method produce finite penetrations. In this example however, it can be seen that both cases that use the penalty method predict a peak stress that is practically identical to the peak stress computed using the direct Lagrange multiplier method. The behaviour that is seen in this example where a relatively small penalty stiffness produces quite accurate stress results generalizes to a very large class of bending-dominated problems. It can also be seen that in this example the penalty method produces a more economical solution as measured by iteration counts that decrease as much as 14%.

Table 3: Bending dominated contact results

	Penetration	Stress	Iterations
Direct Lagrange	0	2.416E4	130
Linear Penalty, default stiffness	1.004E-7	2.416E4	117
Scaled Linear Penalty, S_f = 0.01	1.015E-5	2.416E4	112

Usage

The penalty method is applicable to all contact formulations. The linear penalty method is used by default for the finite sliding surface-to-surface formulation (including general contact) and for three-dimensional self-contact; the direct Lagrange multiplier method remains the default constraint enforcement method in some cases. In order to activate the penalty method use the options listed below.

Abaqus/CAE

In the Interaction module, open the Interaction Property Manager by selecting Interaction → Property → Manager. Select the appropriate interaction and click Edit to receive the Edit Contact Property dialog. Select Mechanical → Normal Behaviour → constraint enforcement method: Penalty (Standard) → behaviour: [Linear | Nonlinear] as shown below:

Input file

The penalty method is selected with the following keyword options:
*SURFACE BEHAVIOR, PENALTY = [ LINEAR | NONLINEAR ]
The data lines can be used to modify the default settings for either the linear or nonlinear approaches.

How to Enforce Contact Constraints using Augmented Lagrangian Method in Abaqus/Standard?

Posted on May 25, 2021October 3, 2024 by Vignesh Pagadalu

There are three methods of contact constraint enforcement that are available in Abaqus/Standard:

The direct Lagrange multiplier method,
The augmented Lagrangian method, and
The penalty method

In this blog, the differences between direct and augmented Lagrangian methods are focussed.

Within the context of the classical “hard” contact problem, the direct and augmented Lagrangian methods differ as follows:

Direct method:

Enforces the contact constraint exactly so that no penetration of the slave nodes into the master surface is allowed.

Augmented Lagrangian method:

Enforces the contact constraint approximately using the penalty method. The penalty stiffness is scalable.
Non-zero penetration of the slave nodes into the master surface is allowed and penetration tolerance is adjustable.
Once solution convergence is obtained, if a slave node penetrates the master surface by more than the relative penetration tolerance of 0.1% (default setting) of the characteristic interface length, the contact pressure is “augmented”. Iterations continue again until convergence. The solution is accepted when the penetration tolerance is accepted.
Can sometimes be more expensive than the Lagrange multiplier approach as a result of the augmentation scheme and additional iterations.

In general, the approximate nature of the augmented Lagrangian contact constraint can simplify the resolution of difficult contact problems and sometimes allow a solution to be found when the exact, Lagrange multiplier constraint is too restrictive. With the ability to scale the penalty stiffness and the penetration tolerance, the contact constraint can be relaxed to facilitate convergence; however this must be done with caution and the results must be carefully checked for excessive penetration of the contact surfaces. More specific situations where this approach can help include:

Very different mesh densities on contact pair surfaces

Non-uniform contact pressure distributions are more likely to occur when very different mesh densities are used on the two deformable surfaces making up a contact pair. If the Lagrange multiplier method is used, the non-uniformity can be particularly pronounced, and oscillations and spikes in the contact pressure may occur, when both surfaces are modelled with second-order elements (including modified, second-order tetrahedral elements). Smoother contact pressures may be obtained for surfaces modelled with second-order tetrahedral elements by using the augmented Lagrangian approach.

Over-constraint problems

An over-constraint occurs when a contact constraint on the displacements, temperatures, electrical potentials or pore fluid pressure at a slave node conflicts with a prescribed boundary condition or other kinematic constraint on that degree of freedom at the node. Specified boundary conditions on the master surface nodes typically do not cause over-constraints. Specified boundary conditions on slave nodes may create an over-constraint.

Over-constraints can be avoided only by changing the contact definition or the boundary conditions. Over-constraint problems may also be alleviated by using the augmented Lagrangian contact constraint enforcement method. While this may help in certain difficult situations, it is generally preferable to remove the source of the over-constraint.

How to model a structure undergoing global instability in Abaqus?

Posted on May 25, 2021September 19, 2024 by Vignesh Pagadalu

A nonlinear static problem can be unstable as a result of global buckling or material softening. If the load-displacement response of the model seems to be reaching a load maximum and there is the possibility of global instability or negative stiffness, two approaches to solving the problem can be used — static or dynamic analysis.

If the structure is reaching a buckling load in a static analysis, perform a Riks analysis.
The Riks method assumes that the global instability can be controlled by modification of the applied loads. This means the loss in stability cannot be too severe; that is, there cannot be a sharp bifurcation in the load-displacement curve. Therefore, structures such as flat sheets, cylinders, and spheres that have a sudden significant loss of stiffness after buckling must have some imperfection built into the original geometry.

This can be done by using the *IMPERFECTION option to modify the original geometry by adding imperfections. The best approach is to use experimentally determined imperfections; however, since these measurements may not be available, the *IMPERFECTION option can use combinations of the eigen modes from a previous buckling analysis as the imperfections to the original geometry.

If the Riks method fails to converge near a limit or bifurcation point (buckling load), the problem may be that the loss in stiffness is too severe. Instability problems that exhibit a sharp transition often require a limit on the maximum incremental arc length to get past the transition point or to have larger imperfections built into the geometry.

If a dynamic analysis is desired, Abaqus/Explicit should be considered as the most robust approach, particularly in the presence material failure, extreme deformation, or rapid changes in contact state. If the loss in stability is not too severe, or only the load maximum is to be computed rather than a fully collapsed configuration, then a dynamic analysis in Abaqus/Standard may be completed with less run time. Choose the APPLICATION parameter on *DYNAMIC to control the amount numerical damping that is applied to the integration operator. If a dynamic analysis is used in Abaqus/Explicit, the structure will vibrate once it has passed the instability and you must decide how to damp the vibrations if a quasi-static solution is required.

Global instabilities can also be stabilized in a static analysis with viscous forces. Although not intended as a primary solution technique for global instabilities, automatic stabilization can be used in the static, coupled temperature-displacement, soils and quasi-static procedures. Automatic stabilization will add viscous damping to the structure, which may allow the solution to go beyond the instability point.

Discrete dashpots can also be used to stabilize a problem of this type.

With either technique, the energy dissipated by the artificial viscous forces (output variable ALLVD for discrete dashpots or ALLSD for automatic stabilization) should remain small compared to the total internal energy (output variable ALLIE) in the problem. The nodal viscous forces should also be small when compared with typical forces in the problem (use nodal output variable VF).

How to Design Complex Aero Parts using the 3DEXPERIENCE Platform?

Posted on May 25, 2021September 13, 2024 by Goutham S R

Designing aircrafts is becoming an increasingly complex task these days. As it becomes increasingly sophisticated, the wiring needed to power and control the electrical systems are also becoming complex.

Large OEMs and companies are dependent on electrical CAD systems which will enable them to execute the electrical diagram which will help in 3D Mock-Up and manufacturing preparation. This will help these OEMs and companies to lower production costs and also to reach the market faster than before.

What are the typical challenges with respect to Wire Harness?

Multi applications and different data sources
Time consuming, repetitive and error prone creation of electrical system
Lack of 3D integration with electrical system schematics which will lead to difficulty in understanding the overall design quality
No associativity between 3D wire harness design and its manufacturing

Due to the complexity and stringent safety regulations in the Aerospace sector, virtual testing and validation plays a significant part in design and manufacturing.

How 3DEXPERIENCE addresses these challenges?

Large OEMs and companies have adopted Dassault Systèmes’ 3DEXPERIENCE platform which accelerates the process from design to manufacturing with compliance to required standards.

The 3DEXPERIENCE platform provides end-to-end electrical development environment from schematics through 3D design up to manufacturing.

Some of the solutions which the 3DEXPERIENCE platform offers with respect to Wire Harness are described below:

CATIA 3DEXPERIENCE – 3D Wire Harness Design

Shared electrical systems data model which enables schematic to 3D Design synchronization
Best-in-class 3D wire harness design solution
In-context wire harness modelling and simulation

CATIA 3DEXPERIENCE – Harness Manufacturing Preparation

World class wire harness layout solution
Automatic generation of full-scale drawings for form-board manufacturing
Synchronization of flattened harness and automatic drawing updates

Large aerospace OEMs and suppliers have reaped multiple benefits by adopting Dassault Systèmes’ 3DEXPERIENCE platform. Some of these are mentioned below:

Integrated process coverage to reduce cycle time from design to manufacturing
Detect issues in the early phase of design by validating virtually and avoid expensive errors in manufacturing
Automatic propagation of modification from design to documentation
Quick electrical space reservation within the product to avoid physical prototypes

Thus, the 3DEXPERIENCE platform and its various applications help OEMs as well as suppliers to design and manufacture complex parts rapidly and with accurate precision while adhering to safety standards.

Category: Blog