Toward a Digital Twin for real-time geometry assurance in individualized production
Introduction
A highly automated production factory for complex assembled products is a huge investment and return on investment requires high product quality, factory throughput, equipment utilization, and flexibility as well as low energy consumption. Geometry related problems, resulting in late changes and delays, usually constitute a significant part of the total cost for poor quality.
Geometry assurance can be described as set of activities that contributes to minimizing the effect of geometrical variation in the final product. Activities take place in all phases of the product realization loop, see Fig. 1.
The design phase: Here, concepts are analyzed and optimized to withstand manufacturing variation. Product requirements are defined and decomposed into locator positions and tolerances on pars and subassemblies.
The pre-production phase: Here, the product and the production system are verified physically. Adjustments are made to correct initial errors and prepare for full production. Inspection preparation and off-line programming of coordinate measurement machines and scanners are performed and all inspection strategies and inspection routines are decided.
The production phase: Here, all initial production process adjustments are completed and the product is running in full production. Inspection data from parts and subassemblies are used to control production and to detect and correct errors.
Most companies today are fully aware of the fact that a change is costlier in production than in the design phase. An effective digital geometry assurances process has the potential to drastically reduced costs and adjustments in production [1].
The area of virtual/digital development of products and production systems has grown extensively the last 20 years. Simulation and optimization are today used for a variety of different products and development tasks. Simulation has been an important tool for shifting expensive product changes, often discovered during production start, to earlier design phases where cost for change is low. Increased number of model programs with shorter intervals drive the needs for simulation. The ability to simulate production ramp-up therefore becomes increasingly important [2].
Increased computer power, faster algorithms and more efficient optimization routines have made simulation and optimization an everyday tool for engineers. Calculation time has gone from weeks to hours and minutes which have made it possible to, not only verify solutions, but also to explore the solution space, searching for the global optimum. Increased focus on sustainability, with reduced waste, is also a driver for a more global view on optimization of design and manufacture [3].
Traditionally, simulation has been used in the design phase with estimated or historical data as input. Increased use of sensors and in-line measuring equipment are making it possible to use (and reuse) simulation models created during product development also in production, now with real data as input. This will allow for adjustment of machine settings for the next product in line based on simulations in the virtual world before the physical changeover, reducing machine setup times and increasing quality. The ability to link large amounts of data to fast simulation makes it possible to perform real-time optimization of products and production processes. The concept of using a digital copy of the physical system to perform real-time optimization is often referred to as a Digital Twin. The concept of a Digital Twin was adopted by NASA for safety and reliability optimizations in [4] and [5]. With an aggressive push toward “Internet of Things”, data has become more accessible and ubiquitous which necessitates the right approach and tools to convert data into useful, actionable information [6]. The vision of the Digital Twin itself refers to a comprehensive physical and functional description of a component, product or system, which includes more or less all information which could be useful in the current and subsequent lifecycle phases [7], [8]. Simulation and seamless transfer of data from one life cycle phase to the subsequent phase are central for the concept of the Digital Twin.
The digital development advancements allow sensors, machines, workpieces, and IT systems to be connected along the value chain beyond a single enterprise. These connected systems (also referred to as cyber-physical systems) can interact with one another using standard Internet-based protocols [9] and analyze data to predict failure, configure themselves, and adapt to changes. Increased availability of data will also open up new possibilities for better maintenance and related service systems [10].
This paper proposes the concept of a Digital Twin for geometry assurance. The paper combines research within variation simulation and quality control to an autonomous self-adjusting system that optimizes quality and allows for individual production. The Digital Twin is developed and used for product and production system design in the concept phase and later on inherited for inspection preparation and process control. Functionality and information needed in each phase/step are specified. How the concept of the Digital Twin allows moving from mass production to more individualized production is discussed, as well as future research challenges.
Section snippets
The Digital Twin in the design phase
In the design phase, different product concepts are developed and optimized to withstand the effect of manufacturing variation. From a geometry assurance perspective, three basic activities are performed:
- •
Specification of product requirements/tolerances.
- •
Specification of locating schemes.
- •
Specification of part tolerances.
A Digital Twin, supporting robustness and tolerance analysis in the design phase, uses geometry representations of the parts, kinematic relations (locating schemes and
The Digital Twin in the pre-production phase
In the pre-production phase, the Digital Twin is used as a basis for inspection preparation and off-line programming (OLP) of coordinate measure machines (CMMs) and scanners. It also contains the definition of the final inspection points and a link to the inspection database.
The Digital Twin in the production phase
In the production phase all production process adjustments are completed and the product is in full production. In this phase, the virtual assembly model (variation simulation model) is used together with inspection data to control production and to detect and correct errors. Future possibilities to fast capture a large amount of data allow for in-line inspection, analysis and control of batches or even individuals, adding a new dimension to mass production.
The Digital Twin – a sheet metal assembly example
In a highly automated production factory for complex assembled products there could be up to several hundreds of robots organized into lines and stations for handling and joining operations. Geometry related problems, resulting in late changes and delays, usually constitute a significant part of the total cost for poor quality.
To scan and analyze inspection data of parts and subassemblies fast and in real-time allow for new possibilities to adjust the process and the equipment to compensate for
Conclusion and discussion
A highly automated assembly line is a huge investment. Return on investment requires high product quality, factory throughput, equipment utilization, and flexibility as well as low energy consumption. Today, geometry related problems, resulting in late changes and delays usually constitute a significant part of the total cost for poor quality.
Therefore, a Digital Twin for geometry assurance is proposed. The Digital Twin contains geometry representation of the assembly, kinematic relations, FEA
Acknowledgement
The work was carried out in collaboration within Wingquist Laboratory and the Area of Advance Production at Chalmers within the project Smart Assembly 4.0, financed by The Swedish Foundation for Strategic Research. The support is gratefully acknowledged.
References (27)
- et al.
Virtual Geometry Assurance Process and Toolbox
Procedia CIRP
(2016) - et al.
Modeling of Manufacturing Technologies During Ramp-up
Procedia CIRP
(2016) - et al.
Optimization and Lifecycle Engineering for Design and Manufacture of Recycled Aluminium Parts
CIRP Annals – Manufacturing Technology
(2016) - et al.
Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment
Manufacturing Letters
(2013) - et al.
About the Importance of Autonomy and Digital Twins for the Future of Manufacturing
IFAC-Papers OnLine
(2015) - et al.
Continuous Maintenance and the Future – Foundations and Technological Challenges
CIRP Annals – Manufacturing Technology
(2016) - et al.
Variation Simulation of Stress During Assembly of Composite Parts
CIRP Annals – Manufacturing Technology
(2015) - et al.
An Industrially Validated CMM Inspection Process With Sequence Constraints
Procedia CIRP
(2016) - et al.
An Investigation of the Effect of Sample Size on Geometrical Inspection Point Reduction Using Cluster Analysis
CIRP Journal of Manufacturing Science and Technology
(2010) - et al.
The Influence of Spot Weld Position Variation on Geometrical Quality
CIRP Annals – Manufacturing Technology
(2012)
Rapid Deployment of Remote Laser Welding Processes in Automotive Assembly Systems
CIRP Annals – Manufacturing Technology
Reengineering Aircraft Structural Life Prediction Using a Digital Twin
International Journal of Aerospace Engineering
The Digital Twin Paradigm for Future NASA and US Air Force Vehicles
Cited by (457)
A Digital Twin-based on-site quality assessment method for aero-engine assembly
2023, Journal of Manufacturing SystemsThe rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop
2023, Robotics and Computer-Integrated ManufacturingSequence-to-sequence digital twin model in chemical plants with internal rolling training algorithm
2023, Applied Soft Computing