Simulation
Assembly Line
Ansys SimAI: Optimizing Mixing Applications with Artificial Intelligence
DigiSim® Unlmtd Online Seminar
3 Generations of Simulation: The Evolution of Engineering Simulation Tools
Engineering simulation tools have undergone significant transformations over recent decades. From the early days of locally installed, dedicated expert simulation tools to the integration of these tools within CAD systems, and now to the latest generation where online simulation platforms serve as the backbone of engineering operations, the evolution is marked by increased accessibility, efficiency, and strategic value.
With the advent of artificial intelligence (AI) and its convergence with simulation, a new generation of simulation could be knocking on the door: the generation of AI Simulation. By incorporating AI-driven insights and functionalities, AI simulation’s core strength is its capacity to speed up and improve the simulation process, making it more efficient, intuitive, and accessible for engineers and designers throughout the design cycle.
Ansys & Westinghouse: Engineering the Future of Nuclear Space Missions
The Future of Sustainable Design with Mars and Ansys
PowerGEM Welcomes Strategic Growth Investment from TA
PowerGEM, a leading provider of simulation modeling software and technical services for the electric power industry is pleased to announce a strategic growth investment from global private equity firm TA Associates (TA).
Founded in 2000 and headquartered in Clifton Park, NY, PowerGEM’s power systems transmission reliability & market simulation software products serve over two hundred North American power systems stakeholders. The Company’s software is utilized by the nation’s top grid operators, including many U.S. ISOs and electric utilities, generation/transmission developers & engineering firms to assess electric transmission grid reliability, as well as the technical & economic impacts of transmission congestion in competitive U.S. electricity markets.
Electric power industry stakeholders increasingly rely on PowerGEM’s software for simulations, insights, analyses & related technical consulting to optimize decision-making and foster efficient power markets. As the power grid becomes increasingly complex with the shift to renewable generation and the need to maintain reliable performance, this partnership with TA will allow PowerGEM to continue serving its growing client base, accelerate the release of new offerings, and execute high-growth initiatives.
2024 Cosmo Tech GenAI & Simulation Product Demonstration
Using AI to discover stiff and tough microstructures
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
A team of researchers moved beyond traditional trial-and-error methods to create materials with extraordinary performance through computational design. Their new system integrates physical experiments, physics-based simulations, and neural networks to navigate the discrepancies often found between theoretical models and practical results. One of the most striking outcomes: the discovery of microstructured composites — used in everything from cars to airplanes — that are much tougher and durable, with an optimal balance of stiffness and toughness.
Rounding the system out was their “Neural-Network Accelerated Multi-Objective Optimization” (NMO) algorithm, for navigating the complex design landscape of microstructures, unveiling configurations that exhibited near-optimal mechanical attributes. The workflow operates like a self-correcting mechanism, continually refining predictions to align closer with reality.
AI predicts head-impact crash results at blazing speed
Intel GenAI For Yield
Diffusion networks are much better suited to the task. Real samples with added noise are used to train the model, which learns to denoise them. Crucially, diffusion networks in this application were able to replicate the long tails of the sample data distribution, thus providing accurate predictions of process yield.
In Intel’s research, SPICE parameters, used in the design phase as part of device simulation, are used as input for the deep learning model. Its output is the predicted electrical characteristics of the device as manufactured, or ETEST metrics. And the results show the model is capable of correctly predicting the distribution of ETEST metrics. Circuit yield is defined by the tails of this distribution. So, by correctly predicting the distribution of ETEST metrics, the model is correctly predicting yield.
The potential here is clear: better optimization of chip yields at the design stage means lower costs. Fewer mask respins, shorter development times, and ultimately higher yield would all be strong differentiators for foundries and design teams that can implement models into their PDK/design flows.
Optimizing a Chemical Vapor Deposition Process for a High-Performance Tungsten Material
One of the key factors of the CVD process for Wf/W production is the tungsten deposition rate, which depends on the temperature and partial pressures involved. The tungsten deposition rate is hard to predict because it involves a lot of different parameters, including the surface temperature and partial pressure at the reaction sites, which depend on the reactor geometry, heater temperature, gas flow rates, and gas composition.
Leonard Raumann, Material Engineer at FZJ, designed an experimental single-fiber setup with very well-known boundary conditions. With the help of the COMSOL Multiphysics® software and a parameter study, he found the rate equations. He then used the equations to model the Wf/W production with multiple fibers. For this, Raumann applied COMSOL Multiphysics® again, followed by a parameter optimization. The resulting parameters were also applied in reality with success.
Scaling up the production process for tungsten-fiber-reinforced tungsten means new possibilities for fusion power. Before this research, producing one layer of the tungsten material took around five hours, but by optimizing the CVD process parameters, it can take just 30 minutes to produce one layer of Wf/W — which is 10 times faster. By optimizing production processes for high-performance materials for fusion reactors, we can ensure that fusion power is both possible and cost efficient.
Accelerating Automation in Logistics with Simulation - A DHL Case Study
Simulation Helps Rockwell Automation Accelerate Digital Transformation
Development of ultra-fast computing method for powder mixing process
Powder mixing is an important operation in many industries. Numerical simulations using the discrete element method (DEM) have been widely used to analyze powder-mixing processes. However, one of the current limitations of the DEM simulation is its high computational cost. Recently, approaches that combine machine learning models and numerical simulations have attracted considerable attention for high-speed computing. However, there has been no research on high-speed computing methods for powder mixing that account for individual particle motions. Here, we propose an original machine learning model, namely, a recurrent neural network with stochastically calculated random motion (RNNSR), which enables a long-time-scale powder mixing simulation with low computational cost and high accuracy. The RNNSR is designed to learn individual particle dynamics with periodicity from short-term DEM simulation results and predict powder mixing for a longer period. The RNNSR combines a recurrent neural network and a stochastic model to predict both convective and diffusive mixing. The simulation results obtained using the RNNSR were quite similar to those obtained using the DEM in terms of the degree of powder mixing, particle velocity, and granular temperature. It was also demonstrated that the RNNSR has the capability of ultrafast computing in powder-mixing simulations. In conclusion, we demonstrated the effectiveness of the RNNSR for ultrafast computation of the powder mixing process.
How Cloud-Native CAE Simulation Boosts Growth & Savings
From CFD analyses of aerodynamics, turbomachinery, and urban microclimates to FEA simulations of structural mechanics in automotive and consumer products, all the way to thermal analyses for indoor environments and electronics cooling, the applications for engineering simulation are numerous and can involve multiple physics. In this article, we dive into cloud simulation and CAE and find out how it strategically impacts business growth and helps optimize financial resources, enabling sustainable growth and profitability while minimizing risks. We explore in numbers how various companies from different industries have benefited from SimScale’s cloud-native simulation platform and how you can, too.
One example is ITW, a global design and engineering firm and a leading global supplier of auto parts. Using SimScale, ITW engineers conducted nonlinear static simulation and analysis to accelerate the development of plastic automotive fastening components, allowing them to minimize the insertion force of their fasteners by up to 85% while saving 10% of their R&D costs.
AI Optimization: New Opportunities for 3D Printing
AI accomplishes this feat by solving the CFD or FEA equations in a non-traditional way: machine learning examines, and then emulates, the overall physical behavior of a design, not every single math problem that underlies that behavior. This uses far fewer computational resources while achieving an extremely robust evaluation of the design in every applicable environment. Hundreds of thousands of design candidates can be simulated and evaluated in less than a day. Bottom line: Applying AI amplifies the typical 10-20% performance improvements of simulation tools alone—up to 30% and higher. (Of course it follows that real-world testing of finished parts remains an essential task to ensure that all quality and performance metrics are met.)
Velo3D requested PhysicsX to design and simulate a solution. PhysicsX has deep experience in simulation, optimization and designing for tight packages (from considerable work in F1 racing and expertise in data science, machine learning and engineering simulation), plus proprietary simulation-validated tools that can automatically iterate on designs using machine learning/AI-based simulations. The PhysicsX approach involves creating a robust loop between the CFD, generative geometry creation tools and an AI controller to train a geometric deep learning surrogate. The surrogate’s speed, producing high-quality CFD results in under a second, is then exploited with a super-fast geometrical generative method in another machine learning loop, which deeply optimizes the design towards whichever multiple objectives the engineer decides are important. The fidelity of the deep learning tools and robust workflow enables a highly accurate solution for final validation of the results against the validated CFD model.
Battery pack assembly line powered by Process Simulate software and the Industrial Metaverse
Data-Driven Design: Leveraging Synthetic Data for Engineering Simulations
A key feature in this recent chapter of the digitization of design is that synthetic data and digital twins have dramatically improved collaboration and communication among stakeholders involved in the product design process. Virtual replicas are far easier to share and visualize than their physical counterparts, and the results of these twins being used alongside synthetic data are far-reaching.
By harnessing the power of synthetic data and digital twins, developers gain deeper insights into product performance. The aviation industry demonstrates a perfect example of this. As a result of using digital twin technologies, Boeing recently saw a 40% improvement in first-time quality of its systems and parts.
Creating comprehensive digital twins that capture the complexity of physical systems may require significant computational resources and integration with IoT devices. At Treble Technologies, acoustic engineers achieve this through benchmark testing. Having successfully simulated a device’s performance in one complex real-life room, the same benchmarks such as geometry detail or boundary conditions can then be used to simulate other hypothetical rooms of similar complexity. To evaluate the authenticity of synthetic data, benchmark datasets comprising real-world data can be created.
Simplify Your Thermal Simulation With Immersed Boundary Method
However, common bottlenecks to simulation have been CAD preparation and the numerical discretization of that model (meshing). Both consume time and manual intervention. The advent of advanced physics solvers and novel meshing techniques, such as the immersed boundary method, means that engineers spend less time making their CAD models simulation-ready and more time on insight-driven design. Skipping the time-intensive CAD preparation also opens up the possibility of doing simulations very early when some components are still in the draft stage and comparing many variants that otherwise would have required repeated CAD simplification efforts.
The Immersed Boundary method addresses the core of this dilemma. It completely removes the CAD preparation or reduces it to a few minutes at most. At the same time, the physics-driven meshing avoids high mesh resolutions on detailed CAD features that are insignificant to the system’s thermal behavior. Yet, it resolves physically relevant regions like power sources or flow channels to the level the user requires. This level might differ significantly based on the current simulation intent.
Lufthansa Technik Reduces Time to Design and Certification with Ansys
Leonardo Labs Implements Ansys Simulation to Develop Cutting Edge Aircraft
🦾 Transferring Industrial Robot Assembly Tasks from Simulation to Reality
By lessening the complexity of the hardware architecture, we can significantly increase the capabilities and ways of using the equipment that makes it financially efficient even for low-volume tasks. Moreover, the further development of the solution can be mostly in the software part, which is easier, faster and cheaper than hardware R&D. Having chipset architecture allows us to start using AI algorithms - a huge prospective. To use RL for challenging assembly tasks and address the reality gap, we developed IndustReal. IndustReal is a set of algorithms, systems, and tools for robots to solve assembly tasks in simulation and transfer these capabilities to the real world.
We introduce the simulation-aware policy update (SAPU) that provides the simulated robot with knowledge of when simulation predictions are reliable or unreliable. Specifically, in SAPU, we implement a GPU-based module in NVIDIA Warp that checks for interpenetrations as the robot is learning how to assemble parts using RL.
We introduce a signed distance field (SDF) reward to measure how closely simulated parts are aligned during the assembly process. An SDF is a mathematical function that can take points on one object and compute the shortest distances to the surface of another object. It provides a natural and general way to describe alignment between parts, even when they are highly symmetric or asymmetric.
We also propose a policy-level action integrator (PLAI), a simple algorithm that reduces steady-state (that is, long-term) errors when deploying a learned skill on a real-world robot. We apply the incremental adjustments to the previous instantaneous target pose to produce the new instantaneous target pose. Mathematically (akin to the integral term of a classical PID controller), this strategy generates an instantaneous target pose that is the sum of the initial pose and the actions generated by the robot over time. This technique can minimize errors between the robot’s final pose and its final target pose, even in the presence of physical complexities.
Turbotech Soars with Sustainable Aviation Solutions Powered by Ansys Multiphysics Simulation
READY Robotics and NVIDIA Isaac Sim Accelerate Manufacturing With No-Code Tools
🦾 From Simulation to Reality: A Tale of Robotic Heartache
It’s hard to imagine building any physical object without first fully designing, assembling, and even testing it on your computer. CAD/CAM tools and multi-physics simulations are readily available, allowing you to iterate quickly in the digital world rather than building N-prototypes of your widget and spending a lot of money trying to get it right. Validating a process in simulation is critical because there is very little margin for error on a real robot. Robots can be big, powerful beasts that will obediently destroy thousands of dollars of material just because you were off by 1 millimeter in your calculations. Not to mention the potential danger to personnel who happen to be near the robot during testing. So, the more we can simulate, the safer and more productive we will all be.
DENSO reduce component simulation time by 80 percent using its Simcenter 3D and NX integrated process
A major challenge today is to improve productivity in the design and simulation of automotive parts. Even before the rise of software solutions, designers focused on geometry and turned to analysts to test and validate performance. However, simulation teams have always been much smaller than design teams – creating a bottleneck in the development process.
With Siemens tools, DENSO saw an opportunity to streamline the traditional workflow between design and engineering analysis, uniting the disciplines. This was particularly true for component design and analysis where simulation processes are more routine. DENSO’s goal was to reduce or eliminate the iteration with a new workflow.
Honeywell Collaborates With Borealis
Honeywell announced that Borealis will implement Honeywell’s UniSim Live software as early adopters to build process models for optimizing operations through virtual process simulation. UniSim Live will allow Borealis to extend the utility of process models to near real-time process monitoring and focus on early event detection by using digital twins to improve plant reliability.
Based in Austria, Borealis is a leading international provider of polyolefins, base chemicals, and fertilizers, and also one of the world’s largest producers of polyethylene (PE) and polypropylene (PP). The organization continues to invest in new technology and digitalization tools to improve processing capabilities and efficiencies as part of its Borstar™ program to optimize and transform key operations.
Using simulation technologies to craft material handling in a facility
NVIDIA Robotics Software Jumps to the Cloud, Enabling Collaborative, Accelerated Development of Robots
Robotics developers can span global teams testing for navigation of environments, underscoring the importance of easy access to simulation software for quick input and iterations. Using Isaac Sim in the cloud, roboticists will be able to generate large datasets from physically accurate sensor simulations to train the AI-based perception models on their robots. The synthetic data generated in these simulations improves the model performance and provides training data that often can’t be collected in the real world.
Developers will have three options to access it. It will soon be available on the new NVIDIA Omniverse Cloud platform, a suite of services that enables developers to design and use metaverse applications from anywhere. It’s available now on AWS RoboMaker, a cloud-based simulation service for robotics development and testing. And, developers can download it from NVIDIA NGC and deploy it to any public cloud.
Impeller Design & Optimization for Additive Manufacturing
Wärtsilä’s engineers redesigned the centrifugal pump impeller for additive manufacturing. Not only was the optimized turbomachinery component 44% lighter, but it was generated using an automated design process, enabling customization.
For this collaborative project, engineers from Wärtsilä’s additive manufacturing center in Finland joined forces with nTopology, SLM Solutions, and Oqton to create a digital workflow based on advanced engineering design and additive manufacturing technologies. The primary aim of this project was to replace the traditionally cast impeller.
Deere Invests Billions in Self-Driving Tractors, Smart Crop Sprayers
The company this year is rolling out self-driving tractors that can plow fields by themselves, and sprayers that distinguish weeds from crops. Deere, which helped make satellite-guided tractors ubiquitous in the U.S. Farm Belt over the past 20 years, is investing billions of dollars to develop smarter machines that the company said will make farming faster and more efficient than it ever could be with just farmers behind the wheel.
Sim2Real AI Helps Robots Think Outside The Box
At Ambi Robotics, our robotic systems learn how to handle diverse items using data generated by advanced simulation. We fine-tune our simulations to the performance of our sensors, our robots, and variations on the items our robots will handle. Our simulations run extremely fast, hundreds of times faster than robots training in the physical world, so we can train our robots overnight. This is what enables our solutions to work reliably from day one.
Bosch’s new partnership aims to explore quantum digital twins
Industrial giant Bosch has partnered with Multiverse Computing, a Spanish quantum software platform, to integrate quantum algorithms into digital twin simulation workflows. Bosch already has an extensive industrial simulation practice that provides insights across various business units. This new collaboration will explore ways quantum-inspired algorithms and computers could help scale these simulations more efficiently.
One of the most promising use cases for the new quantum algorithms is creating better machine learning models more quickly. Hernández Caballer said quantum computing shows tremendous promise in use cases with many combinations of parameters and materials. This early research could give Bosch a leg up in taking advantage of these new systems to improve machine learning and simulation.
Grinding Simulation Enables Growth in Custom Tooling
Even the best grinding simulation has flaws — namely, a reliance on perfection. Real-world scenarios on the shop floor can diverge from the tested parameters, requiring adjustments to achieve the performance promised in the simulation. Gorilla Mill, a toolmaker based out of Waukesha, Wisconsin, relies on ANCA’s CIMulator3D software to control for these differing parameters.
By providing a virtual testing ground for complex custom designs, the software ensures tool quality, prevents scrap and streamlines the process of developing customer prints. A machine-side simulator application reduces setup time by highlighting how differences between ideal and actual circumstances will affect the ground part and by enabling machinists to adjust settings to achieve optimal results rather than regrind wheels.
Improving asset criticality with better decision making at the plant level
The industry is beginning to see reliability, availability and maintainability (RAM) applications that integrally highlight the real constraints, including the other operational and mechanical limits. A RAM-based simulation application provides fault-tree analysis, based on actual material flows through a manufacturing process, with stage gates, inventory modeling, load sharing, standby/redundancy of equipment, operational phases, and duty cycles. In addition, a RAM application can simulate expectations of various random events such as weather, market dynamics, supply/distribution logistical events, and more. In one logistics example, a coker unit’s bottom pump was thought to be undersized and constraining the unit production. Changing the pump to a larger size did not fix the problem, because further investigation showed insufficient trucks on the train to carry the product away would not let the unit operate at full capacity.
The Multi Crane Scheduling Problem: A Comparison Between Genetic Algorithms and Neural Network approaches based on Simulation Modeling
The internal logistics for warehouses of many industrial applications, based on the movement of heavy goods, is commonly solved by the installment of a multi-crane system. The job scheduling of a multi-crane is an interesting problem of optimization, solved in many ways in the past: this paper describes a comparison between the optimization by the use of Genetic Algorithms and the machine learning piloting driven by Neural Networks. A case-study for steel coil production is proposed as a test frame for two different simulation software tools, one based on heuristic solution and one on machine learning; performances and data achieved from reviews and simulations are compared.
Simulating and Optimizing an Electric Vehicle Battery Cold Plate
The efficient and accurate cooling of an electric vehicle battery cold plate is critical to ensure their optimum performance, battery reliability, and lifecycle return on investment. High development costs can be mitigated with access to fast and accurate simulation insights using engineering simulation in the cloud. For example, additional R&D, prototyping, and machining costs are reduced by arriving at an optimized and less complex design, earlier in the design cycle.
This article presents a design and simulation study of battery cold plate technology for electric vehicles. Engineering simulation is used to perform a fully-coupled conjugate heat transfer analysis of a cold plate for dynamic thermal management. Furthermore, using an advanced Subsonic CFD solver, a design study is performed for evaluating pressure-flow characteristics across the cold plate flow channel. Parallel simulations in the cloud are used for scenario analysis both for geometric variants and multiple coolant flow rates. In this sample case, our simulation workflows show users how to set up and run a complete heat transfer and flow analysis of a cold plate, including pressure drop and temperature at various coolant flow rates. Engineers can follow this example to learn how to quickly complete a parametric design study in SimScale and answer key design questions.
Digital twin: Empowering power systems with real-time training and predictive simulation
Uncontrolled operation and neglected maintenance of electrical systems increase safety and financial risks in such facilities, often resulting in unplanned outages that can cause equipment damage and injuries to on-site personnel.
Consider the average cost of power outages in the following critical industries:
- Oil and Gas- $800K to $3M per outage event (per Schneider Electric’s internal Voice of Customer study).
- Semiconductor- $3.8M for a single electrical event
- Data Center -30% of all reported outages cost more than $250,000, with many exceeding $1M
Leveraging digital twin technology, fully digitized electrical single-line diagrams can help address these concerns by boosting operational efficiency and reducing safety exposures. This is an example of the same digital twin technology used during the design phase of an electrical system being applied in the operation and maintenance phases of the lifecycle.
Industrial dataOps capabilities to truly scale Simulation Digital Twins
For some time, the notion of digital twins has been ubiquitous in exemplifying the potential of digital technology for heavy-asset industries. With a digital representation of a real-world system of assets or processes, we can apply simulation and optimization techniques to deliver prescriptive decision support to end-users.
Simulation Digital Twins help industries to make decisions in an increasingly complex & uncertain environment, to balance competing constraints (revenue, cost, efficiency, resiliency, carbon footprint, ++), and to react quickly and adapt with agility to real-world changes.
In this article we are describing solutions that combine the capabilities of Microsoft Azure Digital Twins, Cognite Data Fusion and Cosmotech Simulation Digital Twins. In an integrated solution, Azure Digital Twins provides a digital twin model that reflects real time state from sensors and other real time source and orchestrates event processing. Cognite Data Fusion (CDF) delivers integration of schemas and metadata from IT, OT and ET data sources, including the generation of models and twin graphs for Azure Digital Twins. The Cosmotech Simulation Digital Twin platform adds deep simulation capabilities in a scalable, open framework.
Visual Components Connector for NVIDIA Omniverse: The future of Manufacturing Planning
Advanced simulation in manufacturing
A simulation-based approach to design an automated high-mix low-volume manufacturing system
In this paper, we address the profit optimization problem of an automated high-mix low-volume manufacturing system, which originates from a real-world problem at our industry partner. The manufacturing system includes buffer units from which jobs are automatically transported to workstations, i.e., using automated material handling devices. We consider three different automation concepts for the system: (1) a configuration with parallel buffers and a dedicated robot to work them, (2) a configuration that employs shared buffers that are tended to by automated guided vehicles (AGVs), and (3) a proposed hybrid configuration that takes elements of both aforementioned configurations. We propose a simulation-based approach, which uses simulated-annealing (SA), enriched with the reduced variable neighborhood search (RVNS), to determine the best system configuration for a high-mix, low-volume manufacturer. Decisions concern the choice of automation equipment and the capacity of both parallel and shared buffers. We illustrate the efficacy of the proposed hybrid concept and the proposed SA-RVNS approach with an industry case study using real-world data from our industry partner. Our analysis shows that the proposed concept increases the profit by around 10–30% compared to the others, and the AGV travel time plays an important factor in the proposed concept to yield its true potential.
Expanding the robotics toolbox: Physics changes in Unity 2022.1
Simulate sophisticated, environment-aware robots with the new inverse dynamics force sensor tools. Explore dynamics with the completely revamped Physics Debugger. Take advantage of the performance improvements in interpolation, batch queries, and more.
Improving the design process with simulation
The cheapest way to develop a new product is for the process to be as linear as possible between concept and manufacture. Each time designers make decisions on the project, there’s a chance that the path they take may lead them off on a tangent. Integrating simulation tool sets into the process means that these decisions can be validated sooner rather than later.
Ultimately, most companies will still use a physical test model as their measure of success. Using simulation to mimic this is certainly possible, but not many will invest enough to make this realistic. However, employing simulation-driven design from the outset of the project means that errors are more likely to be caught earlier on. And that means the expensive stage of physical prototyping and testing is more likely to be successful.
At Amazon Robotics, simulation gains traction
“To develop complex robotic manipulation systems, we need both visual realism and accurate physics,” says Marchese. “There aren’t many simulators that can do both. Moreover, where we can, we need to preserve and exploit structure in the governing equations — this helps us analyze and control the robotic systems we build.”
Drake, an open-source toolbox for modeling and optimizing robots and their control system, brings together several desirable elements for online simulation. The first is a robust multibody dynamics engine optimized for simulating robotic devices. The second is a systems framework that lets Amazon scientists write custom models and compose these into complex systems that represent actual robots. The third is what he calls a “buffet of well-tested solvers” that resolve numerical optimizations at the core of Amazon’s models, sometimes as often as every time step of the simulation. Lastly, is its robust contact solver. It calculates the forces that occur when rigid-body items interact with one another in a simulation.
Riven Ramps Up Accurate Part Production with 3D Reality Intelligence
Riven is a cloud software company specializing in 3D reality intelligence that accelerates product introduction of high-accuracy, end-use additive manufactured parts. Riven’s software, using 3D reality data and proprietary algorithms, allows engineering and manufacturing teams to cut iterations and time to good parts while improving the customer experience.
Now, Riven has gone further and corrects these deviations by introducing Warp-Adapted-Models (WAM); Riven’s WAM corrects systematic warp, scaling and offset from all causes in minutes from a first printed part. Additive manufactured parts using Riven WAM are up to 10X more accurate than those printed with CAD. WAM is also scalable from singular high-value parts to series production. This improved accuracy helps solve the customer pain and problems from out-of-spec parts and enables exciting new end-use product applications for AM.
Nonlinear Static Analysis: Snap-Fit Assembly
Cloud-native engineering simulation enables engineers to test the structural performance and structural integrity of their designs earlier and with accuracy. Advanced solvers that account for thermal and structural behavior can be accessed to provide robust assessments of deformation, stresses, and other design critical output quantities. In this article, we analyze the structural performance and integrity of a casing snap-fit assembly using cloud-native nonlinear static analysis. The focus of this analysis was to detect the peak stress regions, and therefore better understand the likelihood of permanent deformations. After analyzing the structural behavior, the design goal was to ensure safe snap operations, while minimizing the material yielding.
Four Ways to Connect Digital Threads with Simulation and Realize the Promise of Industry 4.0
The winners in this new age of manufacturing will be those that can connect the right digital threads of data to get to market faster, avoid downtime, quickly respond to supply-chain disruptions, and address sustainability issues.
Simulation is critical to connecting those threads in a two-way communication network that fully uses Industry 4.0 to achieve four advantages: accelerate time to market, reduce manufacturing downtime, take advantage of just-in-time additive manufacturing, and support sustainability initiatives.
Debottlenecking Takes A Broader View
AspenTech’s strategy is to seek more innovative and lower-cost debottlenecking solutions by looking at the system in a broader way, considering whole plant operation from a process and energy point of view as opposed to addressing each bottleneck in isolation.
One such case study involves a 39,000-tonne/yr Reliance Industries’ acrylonitrile plant in India. Here, AspenTech’s modeling tool, Aspen Plus, was used to develop a steady-state model of the total plant in an effort to address a number of processing challenges. The simulation so far has spurred a 50% reduction in hydrogen cyanide emissions, a 75% decrease in effluent color and a 15% increase in acetonitrile concentration. An ongoing study at the same site also might lead to a cut in flare losses that currently are running the equivalent of about $22,500/yr.
Another project spotlighted at the conference involves cryogenic unit number one at Pemex’s Ciudad Pemex gas processing plant in Mexico. It had been operating at an efficiency of 76.72% for C2+, well below its originally designed capability of 81.94%. Once updated to reflect the plant’s current operating conditions, the Aspen Plus model pinpointed low efficiency in a heat transfer unit. Adjusting that unit gave a production improvement worth $7.6 million/yr.