Paintshop Pro X9
Aida Mazyck
Parametric standards are used to develop layouts of Geico equipment during the design, construction, and assembly phases, for the management and submission of documentation. The development of 3D parametric parts allows the real-time modification of all engineering components.
IEL is a tool for sharing and marking up 3D layout models optimized for Geico CAD systems. Using a physical room and immersive reality software, it is possible to navigate the 3D system on a 1:1 scale for verification, for example, of dimensions, equipment positions, and so on.
J-Rover is the new concept of body handling within the paintshop on independent shuttles. It allows creating systems with flexible layouts that are easily modifiable, thus allowing a production concept with highly flexible cells customized according to the product.
This is Geico proprietary software designed for the customer to digitize documentation and simplify the consultation of all the information related to the plant to be maintained on a single platform.
By reading the Plant Breakdown Structure, the process tags, and P&I, the program makes it possible to easily obtain all the information relating to the plant part or equipment directly in the field.
The reading of the main information relating to a particular piece of equipment mounted on the plant is done thanks to an infrared tablet included in the package that reads the barcode on the equipment itself.
Live image recognition consists of remote viewing tools that, thanks to advanced AI algorithms, allow the detection of anomalies and the verification of process settings. High-resolution cameras observe areas and equipment while artificial intelligence interprets the feed to identify anomalies and alert operators via messages.
Retrofitting of existing plants to improve classification in terms of eco-sustainability, starting from an analysis of the existing situation, a comparison with the current state of the art, a simulation of the benefits and payback of possible interventions, an evaluation of the feasibility, and the cost of any modifications.
this is the centralized digital platform (Digital Twin) that allows every process and processing phase to be monitored. It simplifies quality control, predictive maintenance, and the management of the paintshop as a whole, thereby progressively improving its performance and efficiency.
All this translates into geolocation of the bodies, safety sensors to prevent malfunctions, Internet of Things to analyze and improve the painting stages in real time, wearable technology for perfect human-machine interaction, augmented reality, and an innovative system for managing energy consumption, all managed in the cloud with J-Suite.
The revolution is comprehensive: it affects the spheres of efficiency, safety, quality, cost saving, and respect for the environment.
Thanks to its Smart Paintshop, Geico has been recognized for the best research and innovation in the world, receiving the highest award: the Innovation Award at SurCar Cannes 2017.
Vehicle manufacturers aim to reduce the total number of prototypes needed. Find the optimum in minimum energy required and corrosion protection. alsim Paint Shop gives full control over the most expensive process in the automotive industry. With the technical know-how gathered from our years of experience and expertise in our principal domain, we have created easy-to-use simulation modules for all the major processes in paintshop.
The task is to paint the cars such that in the end for every pair of cars one is painted in red and the other in blue. The objective of the following minimization procedure is to minimize the number of color changes in the paintshop.
If two consecutive cars in the sequence are painted in different colors the robots have to rinse the old color, clean the nozzles and flush in the new color. This color change procedure costs time, paint, water and ultimately costs money, which is why we want to minimize the number of color changes. However, a rearrangement of the car sequence is not at our disposal (because of restrictions that are posed by the remainig manufacturing processes), but we can decide once we reach the first car of each car pair which color to paint the pair first. When we have chosen the color for the first car the other car has to be painted in the other respective color. Obvious generalizations exist, for example more than two colors and groups of cars with more than 2 cars where it is permissible to exchange colors, however for demonstration purposes it suffices to consider the here presented binary version of the paintshop problem. It is NP-hard to solve the binary paintshop problem exactly as well as approximately with an arbitrary performance guarantee. A performance guarantee in this context would be a proof that an approximation algorithm never gives us a solution with a number of color changes that is more than some factor times the optimal number of color changes. This is the situation where substantial quantum speedup can be assumed (c.f. Quantum Computing in the NISQ era and beyond). The quantum algorithm presented here can deliver, on average, better solutions than all polynomial runtime heuristics specifically developed for the paintshop problem in constant time (constant query complexity) (c.f. Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm).
To be able to solve the binary paintshop problem with the Quantum Approximate Optimization Algorithm (QAOA) we need to translate the problem to a spin glass problem. Interestingly, that is possible with no spatial overhead, i.e. the spin glass has as many spins as the sequence has car pairs. The state of every spin represents the color we paint the respective first car in the seqence of every car pair. Every second car is painted with the repsective other color. The interactions of the spin glass can be deduced proceeding through the fixed car sequence: If two cars are adjacent to each other and both of them are either the first or the second car in their respective car pairs we can add a ferromagnetic interaction to the spin glass in order to penalize the color change between these two cars. If two cars are next to each other and one of the cars is the first and the other the second in their respective car pairs we have to add a antiferromagnetic interaction to the spin glass in order to penalize the color change because in this case the color for the car that is the second car in its car pair is exactly the opposite. All color changes in the car sequence are equivalent which is why we have equal magnitude ferromagnetic and antiferromagnetic interactions and additionally we choose unit magnitude interactions.
We want to execute a one block version of the QAOA circuit for the binarypaintshop instance with p = 1 on a trapped-ionquantum computer of IonQ. This device is composed of 11 fully connected qubits with average single- and two-qubit fidelities of 99.5% and 97.5% respectively (Benchmarking an 11-qubit quantum computer).As most available quantum hardware, trapped ionquantum computers only allow the application of gatesfrom a restricted native gate set predetermined by thephysics of the quantum processor. To execute an arbitrary gate, compilation of the desired gate into available gates is required. For trapped ions, a generic nativegate set consists of a parameterized two-qubit rotation, the Molmer Sorensen gate,\(R_\mathrmXX(\alpha)=\mathrmexp[-\mathrmi\alpha \sigma_\mathrmx^(i)\sigma_\mathrmx^(j)/2]\) and a parametrized single qubit rotation:
QAOA circuits employ parametrized two body \(\sigma_z\) rotations, \(R_\mathrmZZ(\gamma)=\mathrmexp[-i\gamma \sigma_\mathrmz^(i)\sigma_\mathrmz^(j)]\). To circumvent a compilation overhead and optimally leverage the Ion Trap, we inject pairs of Hadamard gates \(H H^\dagger = 1\) for every qubit in between the two body \(\sigma_z\) rotations. This means we are able to formulate the phase separator entirely with Molmer Sorensen gates. To support this, the QAOA circuit starts in the state where all qubits are in the groundstate \(\left 0\right\rangle\) instead of the superposition of all computational basis states \(\left + \right\rangle\),
To find the right parameters for the QAOA circuit, we have to assess the quality of the solutions for a given set of parameters. To this end, we execute the QAOA circuit with fixed parameters 100 times and calculate the average number of color changes.
We optimize the average number of color changes by adjusting the parameters with scipy.optimzes function minimize. The results of these optimsation runs strongly depend on the random starting values we choose for the parameters, which is why we restart the optimization procedure for different starting parameters 10 times and take the best performing optimized parameters.
Note here that the structure of the problem graphs of the binary paintshop problem allow for an alternative technique to come up with good parameters independent of the specifics of the respective instance of the problem: Training the quantum approximate optimization algorithm without access to a quantum processing unit
Once the parameters are optimised, we execute the optimised QAOA circuit 100 times and output the solution with the least color changes.Please replace with your IonQ API key and with the API endpoint.
Note here, that in a future production environment the optimization and execution phase of the QAOA should be merged, i.e. we output in the end the best performing sample gathered during the training phase of the QAOA circuit. For educational purposes, we separated here the training and the evaluation phase of the QAOA.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
e59dfda104