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.
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.