Browse Topic: Computer integrated manufacturing
Despite advances in CFD, wind tunnel testing remains indispensable for aerodynamic validation, correlation, and homologation. Increasing configuration complexity, shortened development cycles, and stringent result robustness and documentation requirements demand a shift from isolated facilities to integrated, data-driven ecosystems within the overall development and company-wide test processes. We present a software-centric approach integrating wind tunnel operations into a strategic element of the Digital Thread. By orchestrating test planning, execution, data acquisition, and documentation within a unified framework, experimental data becomes reusable across projects and traceable for compliance and homologation. The interaction between CFD and physical testing is important. Such approach systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active aerodynamic components in test sequences, while BEVs introduce further aerodynamic and thermal aspects for range and efficiency. Thus, extended and automated test definition down to the step-level of test sequences is introduced. Within such integrated environment, AI can be a supporting engineering tool to enhance testing. AI-based methods can assist in identifying relevant test points within complex parameter spaces and in correlating experimental and simulated results, assisting but not replacing established engineering judgment. Also, for the operating department, analyzing process data for maintenance predictions and efficiency optimizations can be assisted by AI-based methods and supporting AI-agents. The approach boosts efficiency by reducing test effort and tedious manual tasks, leading to shorter development cycles, supporting improved time-to-market. Structured workflows and standardized data handling enhance data quality, improve comparability of results, and ensure robust documentation for reliable audit trails. By combining physical testing, simulation, and intelligent processing, the wind tunnel becomes a reproducible, innovation-enabling element in modern product development, positioning software as the backbone of efficient, future-proof aerodynamic testing.
Aircraft verification and certification entail a variety of testing tasks and require coordination among numerous stakeholders across different disciplines to ensure alignment on requirements. Historically, certification strategies have relied on both physical testing and high-fidelity simulation. The integration of these complementary approaches is essential to address their respective blind spots and to support credible certification evidence. A key challenge lies in the rigorous correlation of simulation models with physical test data. Flutter verification, for instance, is a critical component in defining the aircraft’s flight envelope and plays a foundational role in certifying safe operational boundaries. In this work, the process of freedom from flutter verification is demonstrated. This work introduces a novel approach to combining simulation and test data with the aim to accelerate and streamline the verification process leading to more efficient and cost-effective aircraft development. In addition, it is shown how the flutter verification process can be deployed using a simulation process and data management (SPDM) tool from which tasks are assigned and results are collected allowing transparency about the status of the workflow and providing stakeholders access to the data they need when they need it. The workflow is demonstrated using ground vibration test measurement performed on a full-scale F16 aircraft. Throughout the process, simulation data, test results, requirements, and supporting documentation are systematically managed within the SPDM framework. This enables effective cross domain collaboration between simulation and test engineers while also maintaining a single source of truth for proof of compliance and progressively building a robust digital thread throughout the development lifecycle.
The UH-60 Black Hawk — manufactured by Sikorsky Aircraft Corporation — is a twin turbine engine, single rotor, semi-monocoque fuselage rotary wing helicopter used primarily for Utility (tactical transport of troops, supplies, and equipment) purposes. In August of 2024, an experimental effort known as Transformation in Contact was called for, where systems would be more simple, intuitive, low signature, and iterative. This effort, along with the implementation of MBSE, has become a critical component for evaluating and refining technologies that could be needed without delay. This paper will serve to provide the collective results of the digital thread being developed for the Black Hawk as well as explore the efforts and processes utilized for this design. In particular, how the application of a Modular Open Systems Approach (MOSA), integration of a digital backbone, and utilization of the Capability Program Executive (CPE) Aviation Enterprise Architecture Framework (EAF) has enabled a cohesive standard for the rapid technology insertions while reducing cost, increasing efficiency, and improving the overall maintenance and sustainment for the aircraft.
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
Over the past 25 years, the heavy fabrication and construction equipment industry has experienced significant transformation. Driven by a global surge in demand for construction machinery, manufacturers are under increasing pressure to deliver higher volumes within shorter timelines and at competitive costs. This demand surge has been compounded by workforce-related challenges, including a declining interest among the new generation in acquiring traditional manufacturing skills such as welding, heat treatment, and painting. Furthermore, the industry faces difficulties in staffing third-shift operations, which are essential to meet production targets. The adoption of automation technologies in heavy fabrication and construction equipment manufacturing has been gradual and often hindered by legacy product designs that were optimized for conventional manufacturing methods. As the industry transitions toward smart, connected manufacturing environments under the industry 4.0 paradigm, it becomes imperative to re-evaluate existing design and production strategies. This paper aims to establish a framework for aligning product and process design with emerging automation capabilities and strategic business objectives. It advocates for a design-for-automation approach, wherein components are engineered to be compatible with robotic handling, automated guided vehicles (AGVs), conveyors, and other intelligent systems. By doing so, manufacturers can enhance operational efficiency, improve safety and reliability, and reduce time-to-market for new products.
The integration of digital twins within a digital thread framework offers significant benefits for managing Army ground and surface water vehicles. This paper examines how digital twins can enhance lifecycle management, operational efficiency, and maintenance for mature and new military vehicle programs. Scalable and cost-effective implementation with layered capabilities allows organizations to start with a cost-effective foundational model and phase in additional layers of capability over time. This phased approach allows you to expand your digital twin capabilities as program budgets permit, ensuring that you can adapt to evolving requirements without overwhelming upfront investment. For established programs, digital twins enable real-time monitoring, predictive analytics, and data-driven decisions, improving resource allocation and cutting costs. For new programs, they speed up prototyping, integrate modern technologies, and enhance training capabilities. Case studies demonstrate that digital twins can simulate vehicle performance, support proactive maintenance, and improve resource management. Despite challenges like data integration and cybersecurity, our findings highlight the potential of digital twins to boost the resilience and readiness of military fleets, ultimately aiding mission success. This research provides a roadmap for military stakeholders to modernize vehicle management using advanced digital technologies.
Using artificial intelligence (AI) as a key tool to both interpret and accomplish custom design needs remains in its infancy. However, if our partnership with NASA is any indication, we are on the edge of a massive change in the way engineers use AI's immense computing power to develop CAD models for critical applications. This paradigm shift will resonate for years to come as industries such as aerospace, medtech, and consumer electronics, find ways to harness this new technology to speed iteration and go-to-market times. A quick spoiler alert: Using AI still requires well-considered human input to make it all happen and it demands digital manufacturing to do it quickly.
In the era of Industry 4.0, the maintenance of factory equipment is evolving with new systems using predictive or prescriptive methods. These methods leverage condition monitoring through digital twins, Artificial Intelligence, and machine learning techniques to detect early signs of faults, types of faults, locations of faults, etc. Bearings and gears are among the most common components, and cracking, misalignment, rubbing, and bowing are the most common failure modes in high-speed rotating machinery. In the present work, an end-to-end automated machine learning-based condition monitoring algorithm is developed for predicting and classifying internal gear and bearing faults using external vibration sensors. A digital twin model of the entire rotating system, consisting of the gears, bearings, shafts, and housing, was developed as a co-simulation between MSC ADAMS (dynamic simulation tool) and MATLAB (Mathematical tool). The gear and bearing models were developed mathematically, while the shaft and housing models were developed in dynamic environment as flexible bodies. The co-simulation was achieved through an S-function exported from dynamic environment, wherein the forces obtained mathematically were inputs to the dynamic environment, and displacements were treated vice versa. Autoregression and spectral kurtosis signal processing methods were implemented to perform denoising operations on raw vibration signals. Empirical mode decomposition (EMD) on vibration signals was performed for feature extraction studies. Subsequently, an artificial neural network (ANN) based machine learning model was trained for fault classification. Finally, the effectiveness of the proposed algorithm was verified by implementation on one case of rotating machinery under various operating level monitoring conditions.
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a single source of truth for all stakeholders. This tool enables auto selection of control charts based on sample size, frequency & type of parameters (unilateral, bilateral, GD&T) to accommodate variation in Quality standard of parts & manufacturing Process. Additionally Real-time adjustment of control limits based on time period selection make this tool accurate & reliable for monitoring Quality. Incorporating Industry 4.0 and smart manufacturing principles, this tool represents a significant advancement in Quality control. Integration with the Internet of Things (IoT) enables automatic data collection and monitoring, enhancing the efficiency and accuracy of the Quality control process with the SPC based Analytic model. This IoT integration also supports predictive maintenance of tooling and equipment and early detection of potential issues, reducing downtime and improving overall productivity. By minimizing the incidence of faulty parts on the shop floor, the tool significantly reduces rejections, contributing to more sustainable manufacturing practices. This proactive approach ensures high-Quality standards and aligns with smart manufacturing goals by optimizing resource use and enhancing operational efficiency.
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful handling or compliant environmental contact. The actuator's simple design and cost-effective manufacturing process contribute to its practicality and scalability. The modeling and conducting simulations on a soft robotic combined positive and negative pressure actuator also aim to design an adaptive soft-robotic gripper with reduced effort and investigate the up scaling of such grippers to extend their applicability to heavy payload handling and assembly. Once the results from simulations and experiments conducted by models are collaborated, the geometrical parameters are modified to get improved results. The improved model is compared in terms of pressure range, bending angle, versatility, and weight-carrying capacity. Simulation is done on Ansys for real-time results. The parametric study helps in establishing correlations between pressure and deflections to accurately control the motion of soft grippers
Predictive maintenance is crucial for Industry 4.0, and deep neural networks are a promising approach for predicting the capacity of electric batteries. However, few applications effectively utilize neural networks for this purpose with lithium-ion batteries. In this work, different deep learning models are developed, starting with simple neural networks, dense neural networks, convolutional networks, and recurrent networks. Using a public domain dataset, training, testing, and validation datasets were generated to predict battery capacity as a function of the number of cycles. Despite the limited number of samples in the dataset, deep learning techniques are employed to ensure robust prediction performance. The work presents the loss functions for each iteration of the algorithms and the average absolute error. The models made good generalizations over the test dataset within a short prediction time window. Finally, the work presents an average absolute error below 0.3, ensuring good convergence, avoiding data overfitting, and ensuring good generalization of the model to the test set. This work is a first estimate of the life cycle of electric vehicle batteries using small samples as inputs for estimating the remaining useful life of this electric vehicle component.
This work aims to define a novel integration of 6 DOF robots with an extrusion-based 3D printing framework that strengthens the possibility of implementing control and simulation of the system in multiple degrees of freedom. Polylactic acid (PLA) is used as an extrusion material for testing, which is a thermoplastic that is biodegradable and is derived from natural lactic acid found in corn, maize, and the like. To execute the proposed framework a virtual working station for the robot was created in RoboDK. RoboDK interprets G-code from the slicing (Slic3r) software. Further analysis and experiments were performed by FANUC 2000ia 165F Industrial Robot. Different tests were performed to check the dimensional accuracy of the parts (rectangle and cylindrical). When the robot operated at 20% of its maximum speed, a bulginess was observed in the cylindrical part, causing the radius to increase from 1 cm to 1.27 cm and resulting in a thickness variation of 0.27 cm at the bulginess location. However, after optimizing the speed at 35% of its maximum speed, 100% dimensional accuracy was achieved. The integration resulted in collision-free robot and extrusion movement, flexibility, capability of making large parts, and enhanced dimensional accuracy.
An industry-first 3D laser-based, computer-vision system can monitor and control the application of adhesive beads as tiny in width as two human hairs. This unique inspection system for electronic assemblies operates at speeds of 400 to 1,000 times per second, considerably quicker and more effective than conventional 2D systems. “Difficulty in precisely dispensing adhesives or sealants, especially in extremely small or complex electronic assemblies, can lead to over-application, under-application, bubbles, or incorrect location of the adhesive bead,” Juergen Dennig, president of Ann Arbor, Michigan-headquartered Coherix, told SAE Media. Improper application of joining material on electronic control units (ECUs) and power control units (PCUs) can result in poor adhesion, material voids and short circuits.
If you're just getting comfortable with Industry 4.0, which saw the beginnings of smart manufacturing, digitization and real-time decision-making in factories, a senior leader at Intel says the world is already moving on to Industry 5.0. What's Industry 5.0? A joint study by many researchers (link: Industry 5.0: A Survey on Enabling Technologies and Potential Applications (oulu.fi)) describes 5.0 as merging human creativity with intelligent and efficient machines to deliver customized products quickly. But it will take a lot of change and learning to get there.
Rapid advances in high fidelity modeling and high performance computing capabilities have enabled their routine utilization in support of aircraft design. Analysts are able to generate orders of magnitude more data that must then be turned into actionable intelligence to guide design. Enabling effective application of advanced analysis to design requires a robust end-to-end digital transformation to make the simulation processes reusable, repeatable, traceable, scalable and minimize setup errors. This is achieved through the development of a Computational Fluid Dynamic (CFD) modeling framework where streamlining and automation are inserted within the current CFD workflow that involves model setup, simulation and post processing. Workflow automation techniques have been implemented in simulation pre and post processing that reduce the overall process time or enhance the fidelity of the simulation. To conduct CFD evaluations through flight envelope efficiently, space filling methods that take into account uncertainties of complex systems are needed and have driven updates to the boundary condition and design of experiments (DOE) generation within the workflow. Vehicle sub-system design can be highly iterative, performed by a large number of participants in multidisciplinary groups. To ensure traceability across the digital thread, a provenance and metadata storage methodology has been implemented to capture information about CFD simulations and construct a query able database while a model-based systems engineering (MBSE) framework provides a structured and integrated approach to managing information throughout the product lifecycle. The SIM-FIX-SIM approach enabled with a robust analysis framework for digital flight assessment prior to first flight will contribute to the overall goal of reducing development timelines and achieving cost reduction goals for cutting-edge rotorcraft development programs.
Additive manufacturing (AM) is currently being used to produce many aerospace components, with its inherent design flexibility enabling an array of unique and novel possibilities. But, in order to grow the application space of polymer AM, the industry has to provide an offering with improved mechanical properties. Several entities are working toward introducing continuous fibers embedded into either a thermoplastic or thermoset resin system. This approach can enable significant improvement in mechanical properties and could be what is needed to open new and exciting applications within the aerospace industry. However, as the technology begins to mature, there are a couple of unsettled issues that are beginning to come to light. The most common question raised is whether composite AM can achieve the performance of traditional composite manufacturing. If AM cannot reach this level, is there enough application potential to warrant the development investment? The answers are highly dependent on the individual processors and will require significant research. Yet, there are still other common challenges that are not isolated to a singular processor. The focuses of this chapter are the capability to design and provide robust structural analysis for continuous fiber-reinforced polymer AM—two unsung aspects that can make or break this new technology as it finds its way into the aerospace market. These two unsettled issues, out of many, may require fundamental changes to the design, analysis, and manufacturing process. Without solutions to them, adoption by the aerospace industry will be limited to point design applications, thus constraining the technology to being nothing more than a specialized tool.
Recycling of advanced composites made from carbon fibers in epoxy resins is required for two primary reasons. First, the energy necessary to produce carbon fibers is very high and therefore reusing these fibers could greatly reduce the lifecycle energy of components which use them. Second, if the material is allowed to break down in the environment, it will contribute to the growing presence of microplastics and other synthetic pollutants. Currently, recycling and safe methods of disposal typically do not aim for full circularity, but rather separate fibers for successive downcycling while combusting the matrix in a clean burning process. Breakdown of the matrix, without damaging the carbon fibers, can be achieved by pyrolysis, fluidized bed processes, or chemical solvolysis. The major challenge is to align fibers into unidirectional tows of real value in high-performance composites.
In the 1990s and early 2000s, the field of parallel kinematics was viewed as being potentially transformational in manufacturing, having multiple potential advantages over conventional serial machine tools and robots. Many prototypes were developed, and some reached commercial production and implementation in areas such as hard material machining and particularly in aerospace manufacturing and assembly. There is some activity limited to niche and specialist applications; however, the technology never quite achieved the market penetration and success envisaged. Yet, many of the inherent advantages still exist in terms of stiffness, force capability, and flexibility when compared to more conventional machine structures. This chapter will attempt to identify why parallel kinematic machines (PKMs) have not lived up to the original excitement and market interest and what needs to be done to rekindle that interest. In support of this, a number of key questions and issues have been identified which need to be explored to advance the technology further. In this chapter, we establish the history and current state of the art of PKMs and identify key issues that unlock the technology’s potential. We have sought the views of recognized thought leaders to understand the practical limitations that have hindered deployment and what, if anything, can be done to move the technology forward given the prospective advantages.
Electric Vertical Takeoff and Landing (eVTOL) designers and manufacturers operate in a dynamic and rapidly developing industry where innovation and regulatory compliance are continuously evolving. For most manufacturers, a "Digital Thread" of software solutions can not only help them build better products and increase operational efficiency, but also be one of the most crucial aspects towards their success.
The Software Production Factory (SPF) is a cyber physical construct of computers, hardware and software integrated together to serve as an ideation and rapid prototyping environment. SPF is a virtual dynamic environment to analyze requirements, architecture, and design, assess trade-offs, test Ground Vehicle development artifacts such as structural and behavioral features, and deploy system artifacts and operational qualifications. SPF is utilized during the product development as well as during system operations and support. The white paper describes the components of the SPF to build relevant Ground Vehicle Rapid Prototyping (GVRP) models in accordance with the model-centric digital engineering process guidelines. The factory and the processes together ensure that the artifacts are produced as specified. The processes are centered around building, maintaining, and tracing single source of information from source all the way to final atomic element of the built system.
ABSTRACT As technology continues to improve at a rapid pace, many organizations are attempting to define their place within this modern age and the Department of Defense (DoD) is no exception. The DoD’s primary focus on modernization ensures that its design, development, and sustainment of systems demonstrate unparalleled strength that outpaces our adversaries and continue to solidify our position quickly and efficiently as the world’s mightiest through fundamental change. Digital Engineering (DE) is the foundation of that fundamental change. Speed-to-Warfighter, reliability, maintainability, resiliency, and performance are all improved through DE techniques. Accelerating technical integration by connecting once isolated data to a digital thread encompassing all domains, and further facilitating the evolution of the traditional approach/processes into an effective DE strategy. DE’s goal supports a reduction of inefficient process/procedures/communications that traditionally can yield slow iteration, inconsistent resource management, limited participation, and more while promoting mitigating efficiencies. This document explores a DE approach that allows for stakeholder/user collaboration daily using a model centric environment connected through the digital thread and supports the DoD’s 2018 DE strategy implementation. It provides easy and efficient information sharing with one input informing and directly impacting the inputs/outputs of the connected model; creating one authoritative source of truth. Demonstrations and support can readily be provided using existing tools and enabling the ability to complete iterations in minutes or hours instead of weeks or months. It also supports early collaboration, evaluations, and reviews using immersive and XR (augmented/virtual/mixed reality) technologies utilizing both concepts of augmented reality to virtual reality to improve overall efficiency and decision making throughout the product lifecycle. Additionally, the utilization of XR provides many other benefits that allow for a variety of more in-depth applications involving human in the loop practices promoting efficiency, consistency, validation, verification, and facilitates performance and knowledge boost of processes, procedures, and end-user applications. Through the use of XR immersive technologies the technological landscape is quickly shifting and evolving in how data is consumed in many areas including industry, government, commercial, and academics. This document demonstrates actual proofs of concept of this evolutionary change in conjunction with DE practices to demonstrate military adoption and processes that support collaboration, evaluation, and opportunities for quicker project completion or stage progression with XR. Citation: A. D., Granville, “Accelerating the Delivery of Technology to the Warfighter Using Collaborative Immersive XR Technology Environments & Tools,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.
A battery intelligence pioneer will work with a venerable semiconductor yield-improvement firm in a partnership that promises to drastically accelerate the production ramp for the many new EV battery factories on the horizon. Voltaiq, the battery-analysis experts, and PDF Solutions announced the partnership in late March. Tal Sholklapper, Voltaiq's CEO and cofounder, said the EV battery industry is in sore need of help in reducing the manufacturing development cycle, which can take anywhere from four to 10 years from shovels in the ground to output of a consistent, quality product. “The automotive battery industry is really behind.” he said in an interview with SAE Media. “There is a lot of manual analysis and semi-empirical learning going on,” and that slows the discovery of future problems. He said the partnership had the potential to cut battery factory development time in half.
Many design points go into electric vehicle (EV) battery assembly cells that ensure high reliability and repeatability, optimum overall equipment effectiveness, maximum throughput, and Industry 4.0 concepts of digitalization. Examining an EV battery degassing automated cell that is widely installed across the industry exemplifies many of these design features.
The making of a quilt is an interesting process. Historically, a quilt is a canvas of work made from old pieces of cloth cut into squares or whatever shape that make a nice connected pattern and then stitched together. The quilt could be random pieces that is not related to each other. In most recent years and more common cases, a quilt is made of different pieces of patches that are connected and laid out in a special way to tell a story. Not only does it portray a story that is put together in a certain sequence, but it also stiches the pieces of the quilt into a nice and complete narrative. A story that one can understand just by looking at the quilt spread and unfolded. Much like the making of a quilt that has a story to tell, a Product Digital Quilt will tell the story of a product. The Digital Product Quilt replaces the conventional way of telling a product story. The traditional product story is a method that is serially connecting multiple product life cycle silos together. This process is usually error prone, difficult to understand and hard to maintain. The Digital Product Quilt is made up of multiple pieces of the puzzle that makes a product story and the source of truth. At the center of the Product Digital Quilt is the Digital Model which represent the source of truth for the product. The first core band of the Product Digital Quilt, surrounding the Digital Model, are Requirements, Engineering, Analysis, Manufacturing, Integration and Fielding the product to the end user which includes Sustainability and Maintenance. The different pieces of the Product Digital Quilt are stitched with many different digital threads such as the System, Software, hardware and quality threads.
Traditional solutions developed for the aerospace industry must overcome challenges posed for automation systems like design, requalification, large manual content, restricted access, and tight tolerances. At the same time, automated systems should avoid the use of dedicated equipment so they can be shared between jigs; moved between floor levels and access either side of the workpiece. This article describes the development of a robotic system for drilling and inspection for small aerostructure manufacturing specifically designed to tackle these requirements. The system comprises three work packages: connection within the digital thread (from concept through to operational metrics including Statistical Process Control), innovative lightweight / low energy drill, and auto tool-change with in-process metrology. The validation tests demonstrating Technology Readiness Level 6 are presented and results are shown and discussed.
CAD / CAM Services Celina, TX 800-938-7226
Items per page:
50
1 – 50 of 103