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Development of a 3D Machine Simulation and Control System for Manufacturing Optimization
  1. case
  2. Development of a 3D Machine Simulation and Control System for Manufacturing Optimization

Development of a 3D Machine Simulation and Control System for Manufacturing Optimization

blackthorn-vision
Manufacturing
Automation

Challenges in Machine Control and Production Simulation for Manufacturing Automation

The client faces difficulties in accurately simulating and controlling high-volume manufacturing machines, leading to increased downtime, costly collisions, and inefficiencies. Their current systems lack real-time reactive capabilities, comprehensive simulation tools, and integrated diagnostic features, which hampers process optimization and error prevention.

About the Client

A mid to large-scale manufacturing enterprise specializing in high-volume production of wire forms and sheet metal components, aiming to enhance machine control and production efficiency.

Goals for Developing an Advanced Machine Simulation and Control System

  • Create a software platform capable of real-time simulation and monitoring of manufacturing machines to prevent collisions and optimize operations.
  • Enable users to design, modify, and validate machine programs digitally, reducing reliance on trial-and-error methods.
  • Incorporate advanced event-driven architecture to simulate machine events and workflows accurately without requiring machine operation.
  • Provide automatic interference detection and self-correction functionalities to minimize operational errors and machine downtime.
  • Offer an intuitive user interface for easy navigation, program editing, and visualization of 3D machine motions.
  • Allow for productivity estimation during the design phase to inform operational planning and resource allocation.

Core Functional Specifications for the Manufacturing Simulation Software

  • Real-time 3D motion simulation of production machines and workflows
  • A library of pre-configured components for rapid simulation setup
  • Design and editing of machine control programs with graphical tools
  • Simulation of execution programs to predict machine behavior prior to physical operation
  • Automatic detection of potential collisions and interferences with visualization tools
  • Self-correcting mechanisms to adjust tool paths for interference-free machining
  • Import/export capabilities for machine control data and tool geometries
  • Simulation of cycle times with adjustable parameters for throughput analysis
  • Intuitive user interface with flexible controls, undo/redo, and configurable data visibility
  • Generation of control commands or code (e.g., G-code) from simulation data

Recommended Technical Stack and Architecture for the Simulation System

.NET Core
C#
WPF for desktop UI
Open Cascade Technology (OCCT) for 3D geometry processing
Event-driven architecture principles for simulation responsiveness
Modular library of components for rapid deployment

External Systems and Data Sources Integration Needs

  • Machine control system interfaces for data exchange
  • Geometry files and tool data input (e.g., tool geometry files)
  • G-code or other control program exports for machine execution
  • Manufacturing planning systems for productivity forecasting

Performance, Security, and Reliability Expectations

  • System scalability to support multiple concurrent simulations
  • High-performance real-time processing with minimal latency
  • Robustness to handle complex geometries and large datasets
  • Secure data handling and user authentication mechanisms
  • User-friendly interface with customizable views and controls
  • Compatibility across major Windows platforms

Anticipated Business Benefits of the Simulation and Control Platform

The implementation of this system is expected to significantly reduce machine collision incidents, enhance process accuracy, and decrease downtime by enabling proactive diagnostics and corrections. It aims to streamline program development workflows, improve machine productivity estimations, and lower operational costs associated with errors and equipment damage, ultimately boosting manufacturing efficiency and product quality.

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