Aerospace Manufacturing Research Articles

Paper‐based sensors for real‐time cure monitoring in the production of glass fiber‐reinforced phenol‐formaldehyde composites for aerospace interiors

AbstractThe aerospace industries demand for advanced materials necessitates stringent quality control measures, particularly for composite structures to ensure optimal curing and structural integrity. Traditional methods like differential scanning calorimetry (DSC) and dynamic mechanical analysis, while useful, are limited to laboratory settings. This study explores the use of a printed paper‐based sensor for real‐time cure monitoring of glass fiber‐reinforced phenol‐formaldehyde (PF/GF) prepregs. The sensor, composed of a cellulose paper substrate with screen‐printed electrodes, offers flexibility, ease of integration, and cost‐effectiveness compared to commercial polymer‐based dielectric sensors. Real‐time monitoring with the printed paper‐based sensor was conducted at temperatures ranging from 130 to 180°C, both with and without pressure, and validated against DSC and Fourier‐transform infrared (FTIR) spectroscopy. The results showed a strong correlation between real‐time monitoring and the DSC prediction model, as well as FTIR spectroscopy. Notably, the real‐time monitoring revealed a shorter curing cycle on the production line than the predicted cure kinetic model, which is crucial for large‐scale production, such as fabricating honeycomb panels for aircraft interiors. This research underscores the importance of innovative sensor technologies in improving quality control and manufacturing efficiency in aerospace composites.Highlights Cure Monitoring of PF‐based prepregs was carried out using paper‐based sensor. Sensor offered reproducibility and seamless integration in composites. Sensor data showed a strong correlation with DSC and FTIR spectroscopy results. Paper sensors enable online monitoring of composite in aerospace manufacturing.

Aerospace Mutagenesis Increases the Content of Characteristic Metabolites of Tea Tree Leaves and Enhances Taste Characteristics of Tea Leaves

Aerospace mutagenesis can alter the physiological metabolism and growth of tea trees and affect tea leaf quality. In this study, the effect of aerospace mutagenesis on the metabolite content and taste characteristics of Dahongpao fresh leaves and raw tea were analyzed. The results showed that aerospace mutagenesis had little effect on the composition and total amount of metabolites in fresh leaves and raw tea, but significantly changed the content of different metabolites. Aerospace mutagenesis improved the content of lipids, lignans and coumarins, and amino acids and derivatives, which in turn enhanced the aroma and fresh and brisk taste of fresh leaves. Aerospace mutagenesis increased the content of alkaloids, tannins, flavonoids, lignans and coumarins, amino acids and derivatives, and organic acids in raw tea, and enhanced the bitterness, mellowness, and fresh and brisk taste of raw tea. This study provides a basis for the development of aerospace mutagenesis Dahongpao tea products and the establishment of processing techniques.

Techno-Economic analysis of ceramic matrix composites integration in remaining useful Life Aircraft Engine Hot Section Components

AbstractCeramic Matrix Composites (CMCs), specifically SiC/SiC composites, represent a significant innovation in aerospace material technology, offering superior performance over traditional nickel-based superalloys in high-temperature turbine blade applications. This study presents a novel techno-economic assessment, filling a critical gap in the literature by directly comparing the economic and technical viability of CMCs versus superalloys. Unlike previous studies, which primarily focus on technical performance or cost analysis independently, this work integrates both aspects, providing a holistic comparison across key economic metrics, including acquisition, machining, maintenance, and recycling costs. The results demonstrate that SiC/SiC blades offer a 15–20% higher Net Present Value (NPV) and a 17% greater Internal Rate of Return (IRR) over a 20-year lifecycle than superalloys. Despite higher initial costs, CMCs achieve an estimated 2 to 3 years reduction in payback period, mainly due to their superior thermal and creep resistance, leading to fewer maintenance interventions and longer operational lifetimes. Although machining costs for CMCs are higher, these are more than offset by the long-term savings achieved through improved fuel efficiency and lower maintenance costs. A comprehensive sensitivity analysis, incorporating fluctuations in discount rates and material costs, further validates the economic robustness of CMCs in various operational scenarios. This study is the first to compare CMCs and superalloys, offering new insights into the financial implications of material selection in aerospace manufacturing. The findings present critical engineering recommendations that empower aerospace manufacturers and decision-makers to optimise material selection for improved efficiency and cost-effectiveness in high-performance turbine applications.

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

A two-dimensional thin film structure with spectral selective emission capability suitable for high-temperature environments

Spectral selective emission materials, characterized by their high emission efficiency within specific wavelength ranges, play a crucial role in various fields such as infrared stealth and radiative cooling. In this study, leveraging the tunneling effect of ultrathin metal layers and the impedance matching principle, we designed a one-dimensional multilayer thin film structure composed of Mo/Ge/Al. This design successfully achieved spectral selective emission within the 3-14 μm infrared spectrum range, showcasing superior high-temperature infrared stealth capabilities. Research findings indicate that the infrared atmospheric window (ε3-5 μm=0.40, ε8-14 μm=0.30) maintains a low emissivity within the temperature range from room temperature to 400°C, while high emissivity in non-infrared atmospheric windows (ε5-8 μm=0.81) enables radiative cooling. The use of high emissivity in the non-infrared atmospheric window reduced the surface temperature of this structure by 15.3°C compared to untreated Si substrate under similar heating conditions. The temperature reduction was 34.1°C when compared to a Si substrate coated with a 200 nm Al film. This straightforward and easily scalable structure not only presents novel solutions for applications in infrared stealth and radiative cooling but also holds vast potential for diverse fields including military, aerospace, and industrial manufacturing, injecting fresh impetus and possibilities into technological advancement.

Impact of frictional heat and electric charge on the thermomechanical contact properties of piezoelectric quasicrystals: A DC-FFT algorithm

Piezoelectric quasicrystals (PEQCs) are quasicrystals with piezoelectricity. It combines the excellent properties of piezoelectric materials and quasicrystal materials, and is expected to be used as actuators in fields such as aerospace, automotive, and intelligent manufacturing. This article investigates the physical and mechanical properties of one-dimensional hexagonal piezoelectric quasicrystals (1D HPEQCs) coating under normal force and frictional heat and current density by using the discrete convolution-fast Fourier transform (DC-FFT) algorithm-based hybrid element method (HEM). The general solution of the model in the two-dimensional Fourier domain is obtained by combining the two-dimensional Fourier transform with strict operator theory and the Almansi’s theorem. The DC-FFT algorithm-based HEM is used to efficiently and accurately cope with the issue of transforming the fundamental solution in the Fourier domain into the spatial domain. Therefore, the numerical solutions and distribution of stress and displacement in the phonon and phason fields are obtained. The results indicate that current density has a significant impact on the phason field, and an increase in current density enhances the mechanical behavior of the phason displacement. The research results can provide theoretical guidance for 1D HPEQCs as intelligent coating materials.

Innovative D-process MIG welding for enhanced performance of thick sections of MDN 250 in aerospace applications

Maraging steel, known for its exceptional strength, toughness and ductility, is widely used in aerospace and hypersonic missile manufacturing. The present study investigates the feasibility of joining 12 mm thick sections of maraging steel using advanced D-Process MIG welding technology. The D-Process integrates different welding modes such as MIG, Single Pulse-MIG and Double Pulse-MIG to optimize welding parameters, minimizing heat input. Additionally, the research investigates the influence of various post-weld heat treatments (PWHTs) on the performance of welded joints. The PWHTs included in the study are Ageing Treatment (AT), Solution Treatment + Ageing Treatment (SAT) and Homogenizing Treatment + Solution Treatment + Ageing Treatment (HSAT). Metallographic and mechanical tests were conducted on as-welded (AW) and PWHT conditions. Results showed the HSAT condition yielded superior properties with an average UTS of 1771 MPa, YS of 1734 MPa, and an average fracture toughness (FT) of 88 MPa√m. This underscores the efficacy of D-Process welding in meeting stringent requirements for maraging steel in aerospace and high-performance applications.

Fatigue limit prediction of 7050 aluminium alloy based on experimental and shallow + deep hybrid neural network

7050 aluminium alloy, as an important lightweight high-strength structural material because of its excellent characteristics, has increasing applications in aviation, aerospace, and launch vehicle manufacturing. Fatigue failure accounts for approximately 80 % of the structural failures in the engineering field, which leads to numerous losses and is highly uncertain and sudden. Therefore, fatigue life prediction of 7050 aluminium alloy has become a prominent research field. In this study, considering the two variables of stress ratio and stress concentration coefficient, fatigue tests of 7050 aluminium alloy under five different working conditions were conducted to evaluate the effect of these factors on the durability of 7050 aluminium alloy. A novel hybrid neural network model was proposed to solve the time-consuming problem of obtaining a large amount of S-N curve data through traditional fatigue tests. The model uses relatively low cycle fatigue test data for training, realizes data derivation, accurately predicts the fatigue limit of materials, and realizes the purpose of quickly evaluating the fatigue properties of 7050 aluminium alloy materials under different working conditions.

Influences of Ti3AlC2 content on tribological behavior of cold-sprayed Ni-Ti3AlC2 composite coatings

As a type of lightweight and high-strength materials, aluminum alloys have been widely applied in such fields as aerospace and automobile manufacturing, which, however, easily wear out in harsh and complex environments due to their defects like poor wear resistance, low hardness, and easy deformation. For this reason, it is necessary to prepare a coating on the surface of aluminum alloys to enhance their wear resistance. In this work, Ni-Ti3AlC2 composite coatings were prepared on the aluminum alloy surface through cold spraying technology, and the influences of Ti3AlC2 content on the microstructure, mechanical properties, and tribological behavior of the Ni-Ti3AlC2 composite coatings were explored. It turned out that the mechanical properties and tribological behavior of the composite coatings were significantly improved with the increase of Ti3AlC2 content. Therein, the Ni-50%Ti3AlC2 composite coating exhibited a porosity of 0.384 %, a hardness of 310 HV0.2, and a bonding strength of 51.6 MPa, while its wear rate significantly declined to 1.87 × 10−5 mm3/N∙m. Because of the increase of Ti3AlC2 ceramic content, the compaction effect and shot peening effect of the ceramic hard phase were gradually enhanced, which further aggravated the deformation degree of Ni metal, thus improving the density, hardness, bonding strength, and wear resistance of composite coatings.

Graph neural network-based multiscale thermal modeling for heterogeneous materials with complex structures

This study presents a novel GNN-based model for multiscale thermal characterization of heterogeneous materials with complex microstructures. It incorporates relationships between composition, structure, and thermal transport across multiple scales, leveraging the structure of GNNs. Results demonstrate enhanced performance of the GNN-based model compared with existing methods. Specifically, the GNN model improved thermal conductivity prediction with a mean absolute error of 0.18 W/mK (15 % improvement over the Bayesian neural network-based model) and provided a 30x speedup in computation time. The model successfully connected atomic-scale interactions to macroscale properties, achieving less than 5 % error in predicting effective thermal conductivity from one scale to another. Furthermore, it demonstrated the ability to capture nonlinear thermal transport phenomena with 92 % accuracy. The model also improved the prediction of the transient thermal responses by 20 %, accurately capturing the time-varying behaviour of materials. This work also proved the model’s ability to handle new material systems with transfer learning, achieving 88 % accuracy. Finally, Bayesian neural networks integrated into the model provided uncertainty quantification, with 95 % of experimental measurements falling within the predicted bounds. This work demonstrated the ability of the framework to handle complex microstructures, nonlinear phenomena, and transient responses while maintaining a computationally efficient model that can enable real-time applications in design and optimization needed in modern materials innovation. It represents a powerful tool for data-driven multiscale mechanics that could accelerate materials research and development for the aerospace, electronics cooling, and additive manufacturing industries.

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

Research on Disruptive Technology and Future Manufacturing Developments

Industrial transformations, that are triggered by disruptive technologies, have the property of persisting over time. They may represent new ways to do existing activities, or they may represent genuinely new activities. In order to occupy a dominant position in future developments, many different countries, around the world, are actively embracing disruptive technologies. This is manifested by the pre-emptive incubation of new industries. The present paper is based upon an extensive literature search. It is argued that disruptive technologies, in particular, those that are associated with synthetic biology, aerospace manufacturing, and zero-carbon manufacturing, have been selected from a strategic perspective. Through a combination of qualitative descriptions and case studies, the significance and importance of the roles played by biomanufacturing, aerospace manufacturing and green/low-carbon manufacturing, in modern life, are elaborated upon. This allows the case to be made about the three modes of manufacturing that are noted above, that they are subverting traditional manufacturing. Indeed, they are gradually (and sometimes quickly) becoming new manufacturing models. In addition, we note that manufacturing based on new energy sources, intelligent (i.e., robotic) manufacturing and virtual (i.e., data based) manufacturing, (delete the space) are also likely to become highly important topics, and they will be worthy of attention in future considerations.

Synthesizing ternary filler of multi-walled carbon nanotubes/silica/carbon for heightening fluororubber’s mechanical performance

Fluororubber (FKM) is recognized for its outstanding performance, yet there is a pressing need for materials with superior mechanical, thermal, and electromagnetic shielding properties to meet the demands of high-tech industries. The existing research is limited by the lack of a filler that can significantly enhance these properties without trade-offs. This study introduces the innovative synthesis of a ternary composite filler, multi-walled carbon nanotubes/ silica/amorphous carbon (MWCNTs@SiO2-C), through an in-situ polymerization and carbonization method, which is incorporated into FKM to systematically improve its performance. The unique silicon-coated MWCNTs@SiO2-C, optimized at 7 phr (parts per hundred parts of resin), has been shown to increase tensile strength by 206 % and reduce compression set by 17 %, while also elevating the initial decomposition temperature and achieving maximum electromagnetic interference shielding effectiveness in the X-band. Plasma treatment further enhances these properties, with a 175 % increase in tensile strength and improved surface wettability. The integration of MWCNTs@SiO2-C thus addresses a critical gap in material science, offering a substantial advancement for applications in aerospace, military, and semiconductor manufacturing where high-performance materials are essential.

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

Моделирование и оптимизация процесса накатывания роликом AL6061-T6 для достижения минимального отклонения от круглости, минимальной шероховатости поверхности и повышения ее микротвердости

Introduction. Roller burnishing is one of the most common methods of improving the surface quality of parts, wear resistance, microhardness, and corrosion resistance. The process involves compressing and smoothing the workpiece using the pressure of a hardened roller. It is often used to improve part performance and lifespan in sectors including automotive, aerospace, and medical equipment manufacturing. The literature reviewed shows that the roller burnishing process effectively improves the overall surface quality and hardness of the workpiece. In addition, roller burnishing is considered as an affordable method to enhance the functionality and robustness of machined parts by reducing the likelihood of surface defects such as like scratches and cracks. However, very few studies have been reported on the modeling and optimization of roller burnishing of Al6061-T6 for minimum surface roughness, better microhardness, and roundness. The methods of investigation. In the current work, roller burnishing of Al6061-T6 is modeled and optimized for superior microhardness, roundness, and minimal surface roughness. Under dry-cutting conditions, the performance of roller burnishing of Al6061 specimens is assessed in terms of process factors such as cutting speed, feed, and number of passes. Mathematical models to predict the surface roughness, microhardness, and deviation in roundness are developed based on the experimental results. Results and Discussion. The coefficient of correlation for the developed models is found to be close to 0.9, which indicates that it can be reliably used to predict and optimize the roller burnishing of the Al6061-T6. According to this study, the use of the following cutting parameters leads to the lowest variation in roundness (4.282 µm), the better microhardness (119.2 Hv), and the lowest surface roughness (0.802 µm): cutting speed 344 rpm, feed 0.25 mm/rpm and four passes. Further, the study reveals that increasing the number of passes (beyond four) does not significantly improve the surface roughness or microhardness. However, it does lead to a slight increase in the roundness deviation. Therefore, in order to achieve optimal results, it is recommended to use a maximum of four passes during roller burnishing of Al6061 specimens under dry cutting conditions. These results imply that roller burnishing can effectively improve the overall quality and hardness of the workpiece surface. In addition, roller burnishing is considered as an affordable method to increase the functionality and robustness of machined parts by reducing the likelihood of surface defects like scratches and cracks.

Review of the Use of Metals in Biomedical Applications: Biocompatibility, Additive Manufacturing Technologies, and Standards and Regulations

Advanced manufacturing techniques such as Additive Manufacturing (AM) have grown rapidly in major industries such as aerospace, automotive, and biomedical device manufacturing. Biomedical industry has benefitted immensely from AM because of its flexibility in design and its rapid production cycle. Powder bed processes are the major production technique for metal-based AM implants. This paper serves as a comprehensive review on the research efforts being made using AM to develop new patient centered medical devices. This review focuses on AM of the most common metals for biomedical applications, Magnesium alloys, Cobalt-Chromium alloys, pure Titanium, Titanium alloys. Several different aspects are discussed including biocompatibility and osseointegration, application of specific metals in different types of implants, their advantages and disadvantages, mechanical properties in comparison to bone, and their production technologies. Regulatory and quality assurance hurdles that are facing new innovations made using AM are discussed.

Task-driven geometric synthesis method of a bistable compliant mechanism for the rigid guidance problem

Abstract. Compliant bistable mechanisms are specialized mechanisms that have specific self-locking characteristics in two positions. They are widely used in aerospace, micro-electromechanical systems, and high-precision manufacturing. The coupling of kinematic with elastomechanical behaviors of compliant mechanisms, known as kinetostatics, increases the difficulty of synthesizing compliant mechanisms. Currently, most research relies on optimization approaches to find compliant mechanisms that meet motion requirements. To address this challenge, this paper proposes a geometric synthesis method for compliant bistable mechanisms to solve the rigid guidance problem. The pole similarity transformation characteristics of planar beams and the static equilibrium characteristic of bistable mechanisms at stable positions are utilized to decouple the kinematic synthesis and static analysis. The proposed method introduces a task-driven synthesis process, where the critical structural parameters in compliant mechanisms are determined based on the desired guidance positions of motion tasks. This approach eliminates the need for a tedious and time-consuming iterative optimization process. The resulting bistable mechanisms have two stable positions that correspond to the desired guidance positions of the motion task. To illustrate the effectiveness of the geometric synthesis method, a two-position problem of a compliant bistable mechanism is provided as an example.

Experimental investigation and micromechanical analysis of glass fiber reinforced polyamide 6

Achieving process stability in the thermoforming of fiber reinforced polymer materials (FRPs) for aerospace or automotive manufacturing is usually associated with a costly trial-and-error process, where experimental boundary conditions and other influencing factors, such as, for example, material composition, need to be adjusted over time. This is especially true when material phenomena on the microlevel, such as the crystallization kinetics of the polymer matrix or resulting stresses from temperature gradients, are the cause of the process instability. To reduce the experimental effort and reliably predict the material behavior during thermoforming, finite element simulation tools on multiple scales are a useful solution. Hereby, incorporating micromechanical phenomena into the model approaches is crucial for an accurate prediction by further reducing the deviation between simulation and experiment, in particular with regard to the underlying nonlinear material behavior. In this work, unit cell simulations on the microscale of a unidirectional glass fiber reinforced polymer (UD GFRP) are conducted to predict effective thermomechanical properties of a single material ply and ascertain the effect of individual ply constituents on the homogenized material behavior. The polymeric matrix material model used was identified in a prior publication with experimental data at various temperatures for polyamide 6 blends with varying degrees of crystallinities. Various randomization methods are tested to generate the unit cells and replicate the composites’ random fiber distribution, with a focus on process automation. The simulative results are successfully compared to an experimental study on glass fiber reinforced polyamide 6 tested at various temperatures, demonstrating the potential of the approach to reduce both time and cost required for material characterization. Finally, the unit cells are used to generate a database to predict untested load cases that will be used in future work to characterize a homogenized macroscopic material model.

Digital twin technology facilitates precision improvement in complex product assembly: A progressive deduction method of data-driven tolerance allocation

Advancements in aerospace technology have significantly increased the demands on assembly precision, particularly for large aircraft models. These developments require sub-millimeter precision to be achieved in assembly, posing new challenges for conventional tolerance allocation methods, which rely heavily on theoretical models and static simulations. To address these limitations, this paper proposes a digital twin system for online deduction of assembly tolerances (DTS-ODTA) using point cloud registration and free-form deformation (FFD). The proposed method includes three key innovations. First, a digital twin (DT)-based assembly process control framework supports the simulation of assembly deviations by integrating virtual and actual data. Second, a data-driven method models the actual surface shapes, ensuring synchronization between physical and digital states. Third, a progressive deduction strategy for tolerance allocation combines virtual and actual information, providing effective inputs for real-time assembly process control. This approach was validated through an industrial case study, achieving a prediction accuracy of 91.64 % for assembly precision and effectively preventing out-of-tolerance issues during product assembly. By promptly identifying and addressing potential assembly discrepancies, this method enhances the overall assembly quality and provides critical support for design decisions. In summary, this paper introduces a novel DT-based tolerance allocation method that shifts from traditional open-loop design to closed-loop control, thereby meeting the high precision demands of modern aerospace manufacturing.

Bill-of-materials visualization for aerospace & defense: A digital transformation retrospective

We look back at seven years of work to reduce test cost in complex aerospace manufacturing organizations using a bill-of-materials visualization, describing the evolution from a data integration team to a data analytics team, and several of the innovations we developed to be successful. We illustrate a maturity path for digital transformation using open-source tools that visualize over a hundred products, comprised of thousands of parts and bills-of-material to optimize factory flow. We aggregate past work for reducing test cost in aerospace manufacturing, reinforcing and extending work of Deming, and place it in the context of evolving data science capabilities, specifically so others can evaluate how they might accelerate their own digital transformations. We follow the development of a single product from its conception through its deployment and use, a production flow visualization of bills-of-materials plus operations, and discuss how it facilitates analytics and decision-making, depicting complementary visual analytics capabilities built to support it. Reducing cost of testing in complex manufacturing environments is our primary focus by understanding how testing contributes to product cost, and to gain insight into the testing process to reduce testing – and ultimately product – cost. Complex manufacturing organizations considering digital transformations can evaluate the principles, approach, and problems we encountered throughout the team's evolution, specifically, how to extend Deming's work complemented by standard bill-of-materials plus operations visualizations to make critical manufacturing optimizations.