Process & Robotic Simulation
Process Simulation enables you to validate assembly plans virtually, from concept through to the start of production and beyond to help you mitigate risks. The ability to leverage 3D data of products and resources facilitates virtual validation, optimization, and commissioning of complex manufacturing processes, resulting in faster launches and higher production quality.
AMAP can use virtual manufacturing and assembly to simulate and validate your assembly sequences, including all required human and machine interaction. When you use such planning tools to digitally validate production systems, you can reduce tool installation time and minimize system try-out costs. This ability to digitally optimize assembly processes and validate assembly feasibility can also significantly increase productivity. Optimizing the assembly process upfront, prior to the start of production, results in right-first-time manufacturing plans and improves time-to-volume-production. It has been proven that such techniques can reduce overall planning process time, shorten production setup, achieve faster ramp-up and deliver high-quality products right the first time.
Process Simulation can verify the feasibility of an assembly process by various means depending upon the type of study, it can validate process flows, volumes, cycle times, ergonomics, reach and/or collision clearance. This can be done by simulating the full assembly sequence of the product and the required tools. Tools such as sections, measurements and collision detection allow the detailed verification and optimization of assembly scenarios.
Across all major industries, market pressures and costs are requiring manufacturers to build more automation and increased flexibility into their production facilities. Individual plants need to increase the number of products they can build, while exceeding their current quality targets and optimizing their shop floor footprint. Manufacturers must rely, more than ever, on robotics and automation systems to boost production efficiency and flexibility.
Robotic simulation and programming solutions, allow offline development of robotic and automated production systems and programs. These tools address multiple levels of robot simulation and workstation development, from single-robot stations to complete production lines and zones. Using collaborative tools, it is possible to enhance communication and coordination among manufacturing disciplines, enabling smarter decision-making. This allows manufacturers to bring robotic and automation systems online much faster and with fewer errors. Robotics simulation systems are widely used to simulation and develop offline programming for industry applications such as material handling, cutting and arc welding, amongst others.
Using a process or robotic simulation can provide the opportunity for:
- 3D kinematic simulation
- Static and dynamic collision detection
- 2D and 3D sections
- 3D measurements
- Sequencing of operations
- Automatic assembly path planning
- Resource modelling
- Production line or robot programming carried out offline reducing set-up time by allowing simultaneous development of programs before a physical line exists.
- Line and workstation design including ergonomics and reach studies
- 3D interactive documentation, including electronic work instructions
The benefits of using a simulation driven approach to process and robotic design include:
- Accurate design information
- Software-produced process flow diagrams
- Multiple design cases at a fraction of the cost
- Process optimization, finding the process' maximum performance point
- Sensitivity analyses, determining the process' key control variables and degree of operating stability
- Avoiding costly mistakes or bottlenecks
- Reduce risk of production changes late in implementation
- Reduce planning time through automatic sequencing and validation tools
- Reduce cost of change with early detection and communication of product design issues
- Ensure ergonomically safe processes
- Select the best production method by simulating several manufacturing alternatives
This page was published on 11 January 2017