7 results about "Stress minimization" patented technology

A connection device may have a first body portion that may be configured to be rotatingly coupled to a second body portion. The first body portion may have an electrically conductive protruding portion. The second body portion may have an electrically conductive receiving portion. The protruding portion may be received in at least a portion of the receiving portion. The protruding portion and receiving portion may be configured to pass electrical signals between the cable connection portions. The protruding portion may be configured to move in at least a portion of the receiving portion so as to allow rotation of the first cable relative to the second cable while minimizing stress exerted on the first cable and the second cable.

This invention discloses a method and system for optimizing the design of a diesel generator set base structure. The method first establishes a parametric model and a stress simulation model using the position, thickness, width, and number of the base's crossbeams as design parameters and stress minimization as the objective. High-efficiency design parameters are selected through global sensitivity analysis, narrowing the optimization space. Subsequently, orthogonal sampling is performed based on the high-efficiency parameter space, and a radial basis function machine learning model is trained using the stress simulation data of the samples to quickly predict stress. Within the particle swarm optimization framework, candidate samples are generated using a random collaborative strategy, and the optimal sample is selected through the machine learning model for final simulation verification. If the results meet the requirements, the optimal base structure is output. This invention significantly reduces the number of time-consuming simulation calls and improves optimization efficiency through the synergy of parameter selection, surrogate models, and intelligent algorithms.

The application discloses an AI-driven construction process recommendation and mixed reasoning optimization method and system. The method comprises the following steps: receiving project characteristic input, loading expert rule template; taking the project characteristics and the expert rules as the context, calling a large language model to output structured process network data, including process identification, construction period, preposition relationship type, resource demand reference and construction process label; cross-verification checking the integrity of the LLM generation result and the expert rules; supplementing auxiliary processes from the process template library to form a candidate scheme pool; taking the candidate process network as the input, constructing a four-dimensional target model for simultaneously optimizing the minimization of the total construction period, the minimization of the artificial load balance degree, the minimization of the material daily supply pressure and the maximization of the mechanical utilization rate, and solving a Pareto optimal scheme set. The application integrates the LLM structured process network generation, the expert rule mixed reasoning and the multi-dimensional resource optimization into an integrated process, so that the AI-assisted scheme preparation is upgraded from a text suggestion to a deducible process network.

The method for optimizing the forming quality of metal materials based on collaborative distribution of multi-laser data belongs to the field of metal additive manufacturing. The method introduces a regional thermal inertia coefficient (λ) and a dynamic energy density compensation factor (κ) as core control parameters, and constructs a three-level collaborative control system of "feature awareness-time sequence decoupling-dynamic feedback". Through an intelligent data distribution algorithm based on the regional thermal inertia coefficient (λ), the stress minimization planning of the joint is realized; by introducing the time sequence decoupling coefficient (ξ), the laser start-stop interval is accurately controlled, and the heat accumulation is effectively inhibited; by real-time calculation of the κ factor, the energy of the overlapping area and the abnormal area is precisely adjusted. Combined with hierarchical post-processing, the problems of joint accuracy, organizational uniformity and performance anisotropy in multi-laser printing are solved. The present application is suitable for various metal materials, realizes the joint error <0.03 mm, the surface roughness Ra<4 μm, the grain size refinement uniformity, the significant improvement of the room temperature tensile strength, and the forming efficiency is improved by more than 25%.

This invention discloses a parameter design optimization method for gravity-type submersible cages, relating to the field of marine aquaculture equipment structural optimization. It involves collecting multi-period marine environmental data from the target sea area to construct a time-varying environmental load sequence; establishing a three-dimensional coupled finite element model to perform disturbance analysis on multiple structural control parameters; identifying key design parameter sets based on response sensitivity functions; constructing a multi-objective optimization model including floating-sinking time control, maximum stress minimization, and attitude deviation constraints; and employing an adaptive genetic algorithm for global optimization to obtain the optimal parameter solution. This solution is then substituted into the simulation model for dynamic response verification. If the response index converges to the target interval, the optimal parameter solution is output as the final design configuration. This method can achieve systematic optimization of the structural parameters of submersible cages, improving their stability and safety under complex sea conditions, and possesses strong engineering applicability.

The invention relates to the field of white spirit brewing, and provides a multi-wine-retort vinasse distribution scheduling method and system based on dynamic parameter perception in order to improve the dynamic response capability in the production process, a delta value is calculated in real time based on key parameters such as an expansion coefficient, a scheduling strategy is dynamically adjusted, and the problem of plan deviation caused by vinasse discharging volume fluctuation is effectively solved. The production plan adaptability and the matching precision are improved; a delta threshold value is used for dynamically distributing a distribution losing task, so that the maximization of the utilization rate of the cellar and the minimization of the cache pressure are realized, bottleneck overstock and empty material waiting are prevented, and the rhythm stability of a production line is remarkably improved; the system supports task hot start, cold stop and emergency recovery strategies, has the capabilities of task automatic convergence, state safety switching and beat reconstruction, and effectively reduces human intervention and recovery time.

The invention discloses a diesel generating set base structure optimization design method and system. The method comprises the following steps: firstly, establishing a parameterized model and a stress simulation model by taking the position, thickness, width and number of cross beams of a base as design parameters and taking stress minimization as a target; high-effect design parameters are screened out through global sensitivity analysis, and the optimization space is reduced. And then, orthogonal sampling is carried out based on the high-effect parameter space, and a radial basis function machine learning model is trained by using the stress simulation data of the sample so as to rapidly predict the stress. Under a particle swarm algorithm framework, candidate samples are generated in combination with a random cooperation strategy, and an optimal sample is screened out through a machine learning model for final simulation verification. And if the result meets the requirement, outputting the optimal base structure. Through cooperation of parameter screening, the proxy model and the intelligent algorithm, the calling frequency of time-consuming simulation is remarkably reduced, and the optimization efficiency is improved.