For 25 years, AKKA has been an active player in promoting advanced systems engineering approaches for aircraft development and certification.
AKKA has accompanied major aircraft and systems OEMs in devising and applying processes to master the increasingly complex and interconnected systems that comprise modern aircrafts.
Fully engaged in the digitalization of engineering processes, AKKA has developed advanced capabilities in Model Based System Engineering (MBSE) as well as in data analytics for aircraft development.
AKKA can help you:
Document your processes: helpful for detecting bottlenecks, reducing delay and improving production processes.
Improve your specifications and prototype development: by focusing on granularity architecture and reusability & innovation, by easing alternative development and simulating prototypes earlier in the cycle.
Design your components: with an MBSE approach: from top (HLR, HL architecture) to down (physical behavior) with links between models and requirements.
Simulate behavior: for earlier feasibility testing of prototypes.
Globally accelerate your production cycle: by favoring genericity, capitalization and reuse.
Accelerate your V&V: by managing test plans.
Detect and repair inconsistencies easily
Tools: Enterprise Architect, Capella, Simulink/StateFlow, Scade, Scicos, Design Prover, Topcased, Papyrus, Rhapsody, MagicDraw
Standards: Arcadia, UML/SysML/MARTE, BPMN, ArchiMate, Stimulus
Skills: Analysis, stakeholders requirements, system & software engineering, project management, formal technologies
Tools: design, specification development & benchmark for core business and/or custom
Skills: Specification HLR/LLR, traceability, V&V
Tools: Doors, Excel, Simulink Annotations, Scade Design Prover
Tools: Checkstyle, MISRA
Standards: DO178B, ISO 26262, ISO15288
They trust us
Data science for aircraft engineering
Accessing deeper information, translating complex cognitive processes, generating predictive models: these techniques can transform several key aircraft engineering activities.
The strategy for implementing Data Science in engineering processes is based on a combination of technical experience and data analytics knowledge, and can be defined in three phases.
The first step consists of the identification of the technical constraints and “show-stoppers” in the current process. It requires a good vision of the activities: actors, constraints, and dataflows in particular.
The second phase then focuses on the analysis of the available data to explore the differences to extract added value from them: data transformation, data visualization, statistics, machine learning.
The final step requires collaboration between data scientists and technical experts to deploy solutions after they are validated and the adjustment of the data analytics techniques to best fit the context.
This approach can deliver:
- Step-by-step development of “quick-win” solutions
- A deep transformation of the process: less time consuming and targeting higher quality, previously non-explorable solutions
Many examples can substantiate this strategy:
- Anomalies automatic detection
- Predictive model to reduce the number of time-consuming CFD calculations
- Multi-variable optimization
- Cloning of technical expert assessments