Enhanced Door Latch Mechanism for Optimal Performance and Sustainability

Abstract

ABSTRACT Enhanced Door Latch Mechanism for Optimal Performance and Sustainability The invention relates to an enhanced door latch mechanism designed for optimal performance and sustainability. Utilizing generative AI for design optimization, the mechanism integrates advanced algorithms to create robust, durable, and customizable latches with minimal environmental impact. The method includes data collection, AI model training, performance simulation, material selection, prototype development, testing, and iterative refinement. The resulting door latch designs achieve superior strength and functionality while reducing material waste and improving manufacturing efficiency. This approach ensures seamless integration into existing systems, continuous performance monitoring, and ongoing design improvements. Figure 3

Specification

Description:FIELD OF THE INVENTION
The present invention generally relates to the field of mechanical engineering, specifically to a door latch mechanism focused on structural optimization and durability enhancement.

BACKGROUND OF THE INVENTION
Latches find their way into very simple but vital applications in residential and commercial doors, motor vehicle systems, and industrial systems. The major role that a door latch plays is to provide secure fastening of the doors while at the same time giving room for quick release whenever there is a need for it. Their design and development have majorly remained rooted in traditional engineering means characterized by manual design iterations coupled with empirical trials as a means of attaining desired metrics on performance, despite their importance. Current design methods often have the limitation of optimizing multiple controversial criteria related to strength, weight, cost, and environment. Traditional ways can also result in additional material usage and generation of waste aside from long development time by iterating testing procedures.

Developments in artificial intelligence and computational design presented an opportunity for a breakthrough in door latch design. Such a breakthrough can be through generative AI, whereby algorithms create and evaluate many design solutions; if used in designing door latches thus an opportunity would be given toward the full realization of a wider-scope design space and improving performance characteristics and integrating sustainability practices.

US Patent No: 10696039B2: "Multilayer fiber reinforcement design for 3D printing". The patent describes a method including receiving an initial 3D toolpath describing one curved shell of fill material; a set of 2D toolpaths that describe flat shells made of support material; and then subsequently receiving another 3D toolpath for a curved shell from long fiber composite material. The composite material shall include a filament, having a matrix embedding fibers more than twice the filament diameter in length. The method actuates a deposition head for a fill material to trace the first 3D tool path and create the fill material shell, not parallel to the printing substrate. It also actuates a support material deposition head to follow the 2D toolpaths and deposit the support material in a series of nearly flat layers. Finally, it actuates a deposition head for the long fiber composite material to follow the second 3D toolpath, also not parallel to the substrate, to form a curved shell of the long fiber composite that encloses part of the fill material shell.

US Patent No: TWI694343B: "Semiconductor fabrication process control based on assessments of fabrication risk". Pattern-based process control may be defined as: A semiconductor chip layout can be segmented into a number of intended circuit layout patterns. Associated with each such pattern is a corresponding set of fabrication risk assessments associated with different sources of risk. The risk assessments for each pattern of circuit layout are based on various factors such as simulation of the pattern, statistical analysis, and evaluation of empirical data related to similar printed circuit layouts. These risk assessments are used in a scoring formula that computes an overall risk assessment for fabrication risk associated with each of the circuit layout patterns. These patterns are ranked in increasing order of their fabrication risk assessment or overall risk scores. It then outputs ranking information to guide or control the process of semiconductor fabrication.

US Patent No. US10403543B2: "Semiconductor device having groove-shaped via-hole". The semiconductor device has insulating films 40 and 42 formed on a substrate 10. An interconnection 58 is embedded in the surface of these insulating films 40 and 42. Further, the insulating films 60 and 62 are stacked on the film 42 and include a via-hole 60 having a hole shape and a via-hole 66a having a groove shape bent at a right angle. Conductors 70 and 72a are buried in via-hole 60 and groove-shaped via-hole 66a respectively. The width of groove-shaped via-hole 66a is made narrower than that of hole-shaped via-hole 66. This prevents problems such as incomplete filling of the buried conductors and cracking of the inter-layer insulating films. It reduces steps in the conductor Plug, which reduce defective contacts with the upper interconnection layer, reducing problems caused by film formation.

Common issues with conventional design approaches include increased material waste, prolonged development cycles, and difficulties in optimizing for strength, cost, and environmental considerations. Therefore, there is a need for an advanced design approach that can enhance latch performance while reducing the environmental footprint of their manufacture.

The use of generative AI to revolutionize door latch design in terms of advanced performance metrics, sustainability through eco-friendly materials, and customization needs remains uncharted. There is a shortage of empirical validation that will have to be in the AI-generated door latch designs for practical industrial applications and inadequate into linking the design outputs with integration to the already existing line of manufacturing processes. In front of these, there will be a focused effort of theoretical advance AI to have practical application in the area of engineering design to make an innovation in door latch design both effective and feasible.

OBJECTS OF THE INVENTION
It is the primary object of the invention to optimize the performance characteristics of door latches, including strength and durability.

It is another object of the invention to reduce material waste and improve the sustainability of door latch production.

It is another object of the invention to provide a flexible design framework that can be customized for various applications.

It is yet another object of the invention to facilitate integration with advanced manufacturing techniques for efficient production.

SUMMARY OF THE INVENTION
To meet the objects of the invention, it is disclosed here a door latch mechanism for optimal performance and sustainability, comprises: a latch housing; a latch bolt; a spring mechanism; and a catch plate; wherein the latch housing is structured to enclose internal components, the latch bolt is configured to move between a locked and an unlocked position within the latch housing, the spring mechanism is operatively connected to the latch bolt to bias it towards a specific position, the catch plate is positioned to engage with the latch bolt in the locked position; wherein the latch housing, latch bolt, and catch plate are designed using generative AI to optimize strength, durability, and material efficiency; and wherein the materials selected for the latch mechanism are eco-friendly, minimizing environmental impact.

Further disclosed here a method for designing a door latch mechanism, comprising steps of: collecting and preprocessing data on existing door latch designs, including dimensions, material specifications, performance criteria, and failure analysis; training a generative AI model on the collected data to learn design constraints and performance parameters; generating a plurality of door latch design alternatives using the trained AI model; simulating the performance of the generated designs to evaluate stress distribution, durability, and functionality; selecting materials based on sustainability metrics, including recyclability and carbon footprint; fabricating physical prototypes of the optimized designs; testing the prototypes under real-life conditions to validate their performance; and iteratively refining the designs based on testing results and user feedback.

BRIEF DESCRIPTION OF THE FIGURES
Figure 1 illustrates the generative design process applied to door latch optimization.
Figure 2 illustrates AI-Generated Door Latch Design Variants.
Figure 3 illustrates Stress Analysis Results for the Optimized Door Latch Designs.
Figure 4 illustrates Comparative Study of Displacement Across Different Door Latch Models.
Figure 5 illustrates Material Distribution in AI-Generated Door Latch Designs.
Figure 6 illustrates Environmental Impact Assessment of Selected Materials.
Figure 7 illustrates Prototype Development Workflow for AI-Generated Designs.
Figure 8 illustrates Testing Setup for Real-World Validation of Door Latch Prototypes.
Figure 9 illustrates the Iterative Refinement Process Based on User Feedback and Testing Results.
Figure 10 illustrates Manufacturing Process Integration for Optimized Door Latches.
Figure 11 illustrates Performance Monitoring and Feedback System for Deployed Door Latches.
Figure 12 illustrates Example of Customization Options in AI-Generated Latch Designs.
Figure 13 illustrates Comparison of Traditional vs. AI-Generated Latch Designs in Terms of Efficiency.
Figure 14 illustrates Sustainability Metrics Incorporated into the Design Process.
Figure 15 illustrates Final Design Ready for Manufacturing Integration.
Figure 16 illustrates deployment of AI-Optimized Door Latches in Real-World Applications.
Figure 17 illustrates Overview of AI-Driven Predictive Maintenance for Door Latches.

DETAILED DESCRIPTION OF THE INVENTION
The determining factor that relates generative AI to evolving door latch design is the potential for the technology to drive performance optimization and sustainability significantly. Generative AI opens up cutting-edge functionalities in the domain of designing parameter optimization against large databases and simulation scenarios, thereby helping develop extreme robustness and efficiency in door latches. It can personalize design for better durability, safety, functionality, etc.; simultaneously, it is able to select sustainability of materials and manufacturing processes. Through the use of AI in refinement and innovation in the design of door latches, improved operational performance can be balanced against reduced environment-related effects, meeting practical demands as well as the ecological ones that modern engineering places upon the industry.

The solution to revolutionize the design of door latches is one that harness AI algorithms to optimize and innovate latch designs for improved performance and sustainability. Powered by generative AI, deep analysis can be executed from large datasets simulating a number of design scenarios to better enhance key characteristics such as strength, durability, and usability. This methodology, therefore, enhances functional performance in the door latch system and reduces environmental footprint by AI driven optimization, including ecofriendly materials into the design and bringing about efficient manufacturing. Such a result represents quite an all-new generation of door latch systems advanced both in terms of technology and the environment and hence meeting modern high-performance and engineering-design demands.

Methodology
Methodology for Revolutionizing Door Latch Design
1. Data Collection and Preprocessing:
Data Gathering: Huge existing data on earlier door latch designs, specifying dimensions, material specifications used, performance criteria results like strength and durability, and failure analysis. It has to include quantitative data along with user and expert qualitative feedback (Figure 1).
Data Cleaning: Clean the dataset for analysis, removing inconsistencies and missing values, standardizing formats. Ensure the integrity of data to be correct, representative of different design scenarios.

2. AI Model Training and Generative Design:
Model Selection: Select appropriate generative AI algorithms that have the potential to hold up against such complex design tasks, for example, neural networks or evolutionary algorithms. Train these models on the data collected to learn about the constraints of the design and performance parameters (Figure 2).
Design Generation: Using the AI models trained in the previous stage, generate a wide array of design alternatives. Due to its smart configuration, materials used, and structural approaches explored, AI is bound to help designers come up with innovative door latch designs (Figure 3).

3. Performance Simulation and Analysis:
Simulation Tools: Modern simulation software should be utilized in the evaluation of designs produced by Artificial Intelligence based on stress and strain, thermals or functional testing, among others to ascertain their performance criteria (Figures 4 and 5).
Optimization Iterations: These simulations results need to be checked for possible further improvements. Adjust the design by looking at its performance characteristics, for example, making it stronger, more durable or more reliable through repetition (Figure 6).

4. Sustainability and Material Assessment:
Material Selection: Consider how different materials influence the environment negatively in terms of their ability to be recycled, amount of carbon they emit and how easily they can be found (Figure 7).
Sustainability Metrics: When creating AI models there should be a consideration for sustainability metrics in the design process as well as careful selection of materials that leads to least or no wastage and energy consumption during peak performance periods (Figure 8).

5. Prototype Development and Testing:
Prototyping: Bring to life, with the help of techniques like 3D printing or traditional manufacturing, physical prototypes of top-performing designs. Ensure that prototypes represent AI-generated designs adequately (Figure 9).
Testing: The prototypes developed will undergo rigorous testing in order to evaluate their performance under real-life conditions. Test the structural soundness, ease of use, or operational characteristics of the product under varying situations (Figure 10).

6. Iterative Refinement:
Feedback Analysis: The analysis of feedback will be based on both prototype testing as well as real-world usage. Any problem or improvement areas like this will be examined during the analysis process (Figure 11).
Design Optimization: AI applications can improve designs by interpreting test results and recommendations. A continuous process ensures the best efficiency in terms of function and user expectations (Figure 12).

7. Implementation and Integration:
Manufacturing Integration: The ultimate designs need to be adapted to interface into existing production houses. Test if the design being considered is actually produceable at an efficient rate while staying within budget and safety margins (Figure 13).
Quality Control: Apply appropriate measures that will ensure a quality process of production, which will remain stable and reliable. Monitor the entire manufacturing line to handle arising issues or challenges effectively (Figure 14).

8. Performance Monitoring and Continuous Improvement:
Deployment Monitoring: Investigate how efficiently the new latches work on real users' doors. Gather information related to functioning, longevity as well as user's comments (Figure 15).
Continuous Improvement: Employ the performance data to keep improving the designs. Use new data to update the AI models so that they can continuously develop improved designs faster than ever because technology is changing faster than yester years (Figure 16).

According to this method different types of door latch design can be performed completely and systematically within the domains of performance optimization, sustainability and its realization using generative artificial intelligence.

The invention involves the following key aspects:
Generative AI Driven Design Optimization:
• Advanced Algorithms: To find new and innovative designs for door latches, the company would use advanced generative algorithms that are based on artificial intelligence. Therefore, it would create improved designs by considering data which is available at its disposal as well as various constraints imposed on them so as to achieve optimal solutions and better key performance attributes such as durability, strength and functionality.
• Design Simulation: This section contains tools that simulate performances of the AI generated designs in order to show they meet industry standards or exceed them. Multiple design scenarios go through iterations in AIDriven optimization process refining and perfecting latch designs.

Sustainability Integration:
• Material Selection: The process involves selecting materials that are environmentally friendly and generate minimal pollution. In order to get an optimal result, the AI system evaluates the sustainability indicators of various components like recyclability and carbon footprint, so that high-performing and environment friendly buildings can be designed.
• Manufacturing Efficiency: A model-guided efficient manufacturing process minimizes waste materials and energy use. In addition, a generative artificial intelligence method is going to help in the design of locks that will be produced using purifying techniques which maintain the principles of sustainability.

Customization and Adaptability:
• Tailored Designs: Provide door latch design suitable for user specific needs and application requirements. The AI system will change designs based on different parameters ensuring the latches will be appropriate for different environments and functions.
• Flexible Solutions: Design flexibility that can match different desires enables functional versatility of door latches.

Real World Validation and Testing:
• Prototype Development: This process shall require the development of physical prototypes based on these optimized designs, to validate their performances in real-life conditions. This will include certain tough tests for mechanical integrity, ease of use, and general functionality.
• Feedback Integration: The collected feedback from prototype testing makes it possible to analyze necessary refinements and improvements. The iterative process ensures that the final product meets high standards of performance satisfaction.

Seamless Integration into Existing Systems:
• Manufacturing Integration: The final designs of the door latches were tailored to work with the existing manufacturing processes for efficient and cost-effective production within the current production framework.
• Quality Control: Has in place control measures regarding quality in production to maintain uniformity and reliability in accordance with the design and performance criteria specified for each latch.

Continuous Improvement and Innovation:
• Performance Monitoring: After deployment, follows up on the performance of door latches for collected information about their efficiency and durability. Uses such data in implementing changes for further improvements on design and manufacturing processes.
• AI Model Enhancement: Continually updates and refines AI models with new knowledge and insights, assuring that any future design iteration shall align with advances in technology or user feedback.

Table 1: Summary of Results from AI-Driven Optimization for Different Generative Models

Recommendation Processing status Generative model Material Manufacturing method Visual similarity Volume (mm³) Mass (kg) Max von Mises stress (MPa) Factor of safety limit Min factor of safety Max displacement global (mm)
0 Converged Generative Model 1 Aluminum 6061 2 axis cutting Unique 15130.27117 0.040851732 0.388735622 2 707.4217 0.002324338
68.29646167 Converged Generative Model 1 Aluminum 6061 3 axis milling Unique 2733.674561 0.007380921 1.019906834 2 269.6325 0.011407672
60.73730597 Converged Generative Model 1 Aluminum A356 T6 Casting Unique 2735.991723 0.007305098 1.027075323 2 160.6503 0.013611327
98.00854432 Converged Generative Model 1 Aluminum AlSi10Mg Unrestricted Unique 2774.38384 0.007407605 0.83078988 2 288.8817 0.007671762
71.14779159 Converged Generative Model 1 Aluminum AlSi10Mg Additive Unique 4129.141329 0.011024807 0.831010622 2 288.805 0.007130073
70.45860541 Converged Generative Model 1 Aluminum AlSi10Mg Additive Unique 4156.730175 0.01109847 0.903735136 2 265.5645 0.007184639
27.03636365 Converged Generative Model 1 Aluminum AlSi10Mg Additive Unique 3899.891894 0.010412711 1.024764408 2 234.2002 0.014775271
72.44462557 Converged Generative Model 1 Aluminum AlSi10Mg Additive Unique 4272.383471 0.011407264 0.829129649 2 289.4602 0.006877768
68.59156408 Converged Generative Model 1 Aluminum AlSi10Mg Additive Unique 4051.555438 0.010817653 0.883414599 2 271.6731 0.007445278
26.21408649 Converged Generative Model 1 Aluminum AlSi10Mg Additive Unique 3890.55613 0.010387785 0.992926218 2 241.7098 0.018201295

In this study, this developed a generative design framework, which enabled the generation of optimized models of the door latches based on expressed needs from the user and design requirements.

The AI-empowered optimization algorithm improved design efficiency by 30% and reduced material usage by 25%. The design generation approach is found useful in regard to making a solution for intuitive and accessible door latches while considering user testing and feedback (Figure 17).

Expert interviews and codesign workshops verified the potential of AI-powered design to support collaborative innovation in the design process. It points to the potential of AI-based optimization and generative design to actually bring into play a paradigm shift in door latch design for increased efficiency, sustainability, and user experience. This ensures that the solutions would meet user needs and design requirements by being able to integrate users' feedback and design principles into the generative design framework.

This potentially helps in increasing design efficiency and decreasing amounts of material used-thus enabling the capability of the optimization algorithm performed by the AI in producing designs that are more sustainable. Results of the study suggest that AI powered design can be used to facilitate a collaborative and innovative design process, in a cumulatively enhancing manner with human-center design approaches.

Future directions include to expand the generative AI approach for additional material options, to integrate advanced manufacturing techniques such as additive manufacturing, and incorporate real-time performance monitoring for continuous improvement of designs. One focus for future work could be AIdriven predictive maintenance and adaptive design that may further enhance functionality and lifespan of door latch designs while shifting the dial on sustainability improvements.

Generative AI in door latch design is a game-changer for performance optimization and sustainability. This paper describes an approach that enables the development of highly optimized, innovative latch designs that move beyond conventional limitations of manufacturing, applying advanced algorithms. It iteratively refines the designs to meet rigorous performance criteria while including eco-friendly material and manufacturing techniques of reduced environmental impact. Designs can be validated for practicality and effectiveness through comprehensive simulations and testing in the real world. In sum, this generative AI-driven methodology improves the functionality and aesthetics of door latches while fulfilling today's quests toward sustainability by offering a solution that benefits both manufacturers and end-users alike.
 
, Claims:We Claim:

1. A door latch mechanism for optimal performance and sustainability, comprises:
a latch housing;
a latch bolt;
a spring mechanism; and
a catch plate;
wherein the latch housing is structured to enclose internal components, the latch bolt is configured to move between a locked and an unlocked position within the latch housing, the spring mechanism is operatively connected to the latch bolt to bias it towards a specific position, the catch plate is positioned to engage with the latch bolt in the locked position;
wherein the latch housing, latch bolt, and catch plate are designed using generative AI to optimize strength, durability, and material efficiency; and
wherein the materials selected for the latch mechanism are eco-friendly, minimizing environmental impact.

2. The door latch mechanism as claimed in claim 1, wherein the latch housing, latch bolt, and catch plate are fabricated using additive manufacturing techniques.

3. The door latch mechanism as claimed in claim 1, wherein the generative AI design process includes performance simulation and iterative refinement to ensure optimal stress distribution and minimal displacement under load.

4. The door latch mechanism as claimed in claim 1, wherein the mechanism comprises a customizable latch interface designed to accommodate various door thicknesses and configurations.

5. The door latch mechanism as claimed in claim 1, wherein the spring mechanism is optimized for enhanced durability and consistent performance over repeated use cycles.

6. A method for designing a door latch mechanism, comprising steps of:
collecting and preprocessing data on existing door latch designs, including dimensions, material specifications, performance criteria, and failure analysis;
training a generative AI model on the collected data to learn design constraints and performance parameters;
generating a plurality of door latch design alternatives using the trained AI model;
simulating the performance of the generated designs to evaluate stress distribution, durability, and functionality;
selecting materials based on sustainability metrics, including recyclability and carbon footprint;
fabricating physical prototypes of the optimized designs;
testing the prototypes under real-life conditions to validate their performance; and
iteratively refining the designs based on testing results and user feedback.
7. The method as claimed in claim 6, wherein the data collection step includes both quantitative data and qualitative feedback from users and experts.

8. The method as claimed in claim 6, wherein the performance simulation step utilizes modern simulation software to evaluate stress, strain, and thermal characteristics of the door latch designs.

9. The method as claimed in claim 6, wherein the material selection step involves choosing eco-friendly materials that minimize environmental impact while maintaining performance standards.

10. The method as claimed in claim 6, wherein the method comprises the step of integrating the final door latch designs into existing manufacturing processes for efficient production.

Documents