Parallel Kinematic Machines (PKMs) offer enhanced motion dynamics and flexibility, bridging the gap between conventional CNC machines and industrial robots. Stiffness, a key determinant of machining accuracy, is often modelled with limited consideration of gravitational effects, leading to reduced predictive accuracy. This paper introduces a novel stiffness modelling approach that integrates a theoretical model without gravity and gravity-based parameter optimisation through experimental analysis. Comprehensive stiffness measurements were conducted to isolate gravitational effects on the machine structure, enabling precise calibration of the theoretical model for accurate stiffness prediction. A six-dimensional stiffness analysis of the X-Mini machine tool using the optimised model demonstrated improved prediction accuracy, reducing errors by 14 %, 21 %, and 8 % in the X, Y and Z directions, respectively. Predicted stiffness and estimated cutting forces were used to compute workspace deformations, which were then compensated by modifying the depth of cut in slot milling. Experimental validation demonstrated the method's effectiveness, achieving a machined shape error prediction accuracy of 6-9 mu m. This approach can be well applied to shape quality prediction of machined parts by robots and machine tools.

Carbon-fibre-reinforced-polyetherketonketone (CF/PEKK) has attracted increasing interest in the aviation industry due to its self-healing/recycling properties. However, its machining performance is not well understood and there is a lack of optimization study for minimizing its hole damage and improving the production efficiency. Here, we report the first multi-objective optimization study for CF/PEKK drilling. A hybrid optimization algorithm integrating NSGA-II and TOPSIS is deployed to obtain the Pareto solutions and rank the multiple solutions based on closeness to ideal solutions. To highlight the impact of different matrix properties on the optimization outcome, comparative study with conventional thermoset CF/epoxy was carried out for the first time. Experimental validation shows the proposed method can achieve 91.5-95.7% prediction accuracy and the Pareto solutions effectively controlled the delamination and thermal damage within permissible tolerance. The vastly different optimal drilling parameters identified for CF/PEKK and CF/epoxy is attributed to the thermoplastic nature of CF/PEKK with unique thermal/mechanical interaction characteristics.

Point cloud registration is often required in processing measurement data and uncertainty is inevitably brought into this process. However, the effects of registration uncertainties have not been well studied in the literature, especially for high precision applications such as aircraft assembly. Take gap measurement in aircraft wing assembly as an example, using laser scanners and commercial software PolyWorks, this study investigates the registration uncertainty between part-level and assembly-level point cloud data, as well as related factors including system errors, sphere targets, and parameter settings. Results show that: (1) Use of sphere targets cannot improve registration accuracy in PolyWorks but can improve efficiency by reducing 41.94% of iteration times for small-scale point cloud data. (2) The registration process can bring errors and these errors can be affected by the parameter settings during data processing. (3) Systematic errors can be associated with both part-level and assembly-level measurements, these errors will be propagated during the registration process, and their effects on the gap measurement are dependent on the geometrical relationship of the two mating surfaces. A calibration method is proposed to mitigate the effects of systematic errors, and experiments were conducted to validate these methods. It is concluded that laser scanners and computational software can be used for high-precision assembly, and evaluating registration uncertainty is a crucial step for improving assembly accuracy.

Fibre reinforced thermoplastic polymer (FRTP) composites have been used for a wide range of engineering applications (e.g. in transport, construction, energy, etc) due to their excellent mechanical properties and ease of repair and recycling. In recent years, FRTP is increasingly deployed as an alternative to conventional thermoset carbon fibre reinforced epoxy (CF/epoxy) composites, for the purpose of reducing the carbon footprint and contributing to a sustainable manufacturing agenda. Machining of FRTP remains an indispensable process to achieve rapid parts assembly whilst meeting stringent geometric tolerances. However, due to the heterogeneous structure and high thermal sensitivity of FRTP, a range of machining -induced damages (such as matrix smearing, thermal degradation, delamination, burr and surface cavity) often occur, leading to concerns on machined parts quality and reliability. To date, composite machining studies have been mostly focused on conventional thermoset CF/epoxy and there is a lack of an up-to-date, in-depth review of the latest advancement concerning the machining of thermoplastic FRTP. This paper provides a state-of-the-art overview on the recent developments in FRTP machining over the past decade, with a particular emphasis on machining characteristics, damage mechanisms as well as the challenges facing such manufacturing process. The purpose is to present the composite manufacturing community with a timely update, which may guide and inspire further research and development for future FRTP manufacturing.

The global commitment towards reducing carbon emissions drives the implementation of sustainable carbon-fibre-reinforced-thermoplastic composites (CFRTPs). However, the machining of CFRTPs presents challenges due to the material’s ductile-brittle composition and sensitivity to machining-induced high temperatures. For the first time, we conducted temperature-controlled orthogonal cutting of CFRTP (using CF/PEKK as a demonstrator) to unveil its temperature-dependent cutting physics. Three representative cutting temperatures, 23 ℃ (ambient temperature),100 ℃ (< PEKK’s glass transition temperature (T g )) and 200 ℃ (> T g ) and four typical fibre cutting orientations (0°, 45°, 90°, and 135°) have been investigated. The evolution of chip microstructural morphology and surface/subsurface damage have been analysed by advanced microscopy to reveal temperature-dependent material removal mechanisms. The experimental results were elucidated through a novel microscale finite-element-analysis (FEA) model considering thermal softening of the matrix and interface. Results show the transition of the cutting physics with increasing temperature is associated to the degradation of the thermoplastic matrix stiffness/ultimate strength and interface bonding strength and fracture toughness, especially when > T g .

Digital transformation is at the forefront of manufacturing considerations, but often excludes discrete event simulation and cost modelling capabilities, meaning digital twin capabilities are in their infancy. As cost and time are critical metrics for manufacturing companies it is vital the associated tools become a connected digital capability. The aim is to digitize cost modelling functionality and its associated data requirements in order to couple cost analysis with digital factory simulation. The vast amount of data existing in today’s industry alongside the standardization of manufacturing processes has paved the way for a ‘data first’ cost and discrete event simulation environment that is required to facilitate the automated model building capabilities required to seamlessly integrate the digital twin within existing manufacturing environments. An ISA-95 based architecture is introduced where phases within a cost modelling and simulation workflow are treated as a series of interconnected modules: process mapping (including production layout definition); data collection and retrieval (resource costs, equipment costs, labour costs, learning rates, process/activity times etc.); network and critical path analysis; cost evaluation; cost optimisation (bottleneck identification, production configuration); simulation model build; cost reporting (dashboard visualisation, KPIs, trade-offs). Different phases are linked to one another to enable automated cost and capacity analysis. Leveraging data in this manner enables the updating of standard operating procedures and learning rates in order to better understand manufacturing cost implications, such as actual cost versus forecasted, and to incorporate cost implications into scheduling and planning decisions. Two different case studies are presented to highlight different applications of the proposed architecture. The first shows it can be used within a feasibility study to benchmark novel robotic joining techniques against traditional riveting of stiffened aero structures. In the second case study discrete event digital factory simulations are used to supply important production metrics (process times, wait times, resource utilisation) to the cost model to provide ‘real-time’ cost modelling. This enables both time and cost to be used for more informed decision making within an ever demanding manufacturing landscape. In addition, this approach will add value to simulation processes by enabling simulation engineers to focus on value adding activities instead of time consuming model builds, data gathering and model iterations.

Carbon-fibre-reinforced-polyetherketonketone (CF/PEKK) has attracted increasing interest in the aviation industry due to its self-healing properties and ease of recycle and repair. However, the machining performance of CF/PEKK is not well understood and there is a lack of optimization study for minimizing its hole damage and improving the production efficiency. Here, we report the first multi-objective optimization study for CF/PEKK drilling. A hybrid optimization algorithm integrating Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) is deployed to obtain the Pareto solutions and rank the multiple solutions based on closeness to ideal solutions. To highlight the impact of different matrix properties on the optimization outcome, comparative study with conventional thermoset carbon fibre reinforced epoxy composite (CF/epoxy) is carried out for the first time. Experimental validation shows the proposed method can achieve 91.5–95.7% prediction accuracy and the Pareto solutions effectively controlled the delamination and thermal damage within permissible tolerance. The vastly different optimal drilling parameters identified for CF/PEKK as compared to CF/epoxy is attributed to the thermoplastic nature of CF/PEKK and the unique thermal/mechanical interaction characteristics displayed during the machining process.

There has been an ongoing need to increase the application of industrial robots to complete high-accuracy aerospace manufacturing and assembly tasks. However, the success of this is dependent on the ability of robotic systems to meet the tolerance requirements of the sector. Machine learning (ML) robot error compensation models have the potential to address this challenge. Artificial neural networks (ANNs) have been successful in increasing the accuracy of industrial robots. However, they have not always brought robotic accuracy within typical aerospace tolerances. Methods that have not yet been investigated to further optimize the use ANNs used in ML robot error compensation methods are presented in this paper. The focus of ML compensation methods has dominantly surrounded ANNs; there have been little to no investigations into other types of ML algorithms for their suitability as robot error compensation models. The success of ANNs to date proves the capability of ML algorithms for this task, and therefore other ML algorithms should be investigated to determine their capability to potentially improve industrial robot accuracy. This paper takes a novel approach by investigating the Support Vector Regression (SVR) ML algorithm to compensate for robot error. The ML models in this research were trained using measurement data captured using a laser tracker and collaborative robot. The ANN model reduced the mean error by 46.4%, 94.8%, and 95.8%, in the x, y, and z-axis, respectively. The SVR model reduced the mean error by 42.4%, 95.9%, and 98.4%, in the x, y, and z-axis, respectively, demonstrating its ability to be implemented as a robotic error compensation model. The success of both the ANN and SVR algorithms enforces the need for further research into other ML algorithms as robot error compensation models, and there is also still potential to further optimize the algorithms used.

A new lockable spherical joint is proposed in this paper, and it can be used as a revolute joint, a universal joint or a spherical joint. Three locking methods are introduced to construct the lockable spherical joint. Based on the proposed lockable spherical joint, a new reconfigurable parallel mechanism (RPM) with large positioning workspace is presented. The RPM has a tripod architecture with a lockable joint in each limb, which enables it three types of parallel mechanisms in six motion cases. Mobility analysis of the six motion cases is conducted. The new RPM can realize both translation and rotation by changing operative modes, which can be employed as machine tools, fixtures or manipulators. Based on the new RPM, two modular reconfigurable manufacturing systems are designed for aircraft assembly production, and a reconfiguration strategy is presented.

Although new generation carbon fibre reinforced thermoplastic (CFRTP) such as carbon fibre reinforced polyetherketoneketone (CF/PEKK) is a promising sustainable alternative to the conventional thermoset CFRP, there is a lack of literature regarding its machining performance. This is the first study unveiling the hole wall and chip temperature evolution during drilling of thermoplastic CF/PEKK composite and the resulting material damages have been studied in detail. Through comparative study with CF/epoxy, the disparate drilling performance of the two composites has been uncovered, and the results were found to be closely related to the materials’ thermal/mechanical properties. Specifically, CF/PEKK produces continuous chips due to its excellent ductility and thermal sensitivity, whereas CF/epoxy produces segmented chips due to its brittle nature. CF/PEKK generates up to 40 N (50.5%) higher thrust force, 87.6 ℃ (98.9%) higher hole wall temperature and 61.1 ℃ (48.8 %) higher chip temperature than that of CF/epoxy. This has been correlated to the longer tool-chip contact length of CF/PEKK and its unique chip morphology. Despite the greater thrust force/temperature generation, CF/PEKK shows 55.7% lower delamination damage than CF/epoxy, and this is owning to its excellent interlaminar toughness. This study establishes a more in-depth understanding into the drilling performance of thermoplastic CF/PEKK and thermoset CF/epoxy and also provides guidance on the high performance manufacturing of next generation CFRPs.

Parallel Kinematic Machines (PKM) demonstrate the capability of adapting to modern, flexible manufacturing systems due to their higher flexibility and improved motion dynamics. Compliance of a machine tool has a significant impact on the performance, which directly contributes to the quality of the machined workpiece. Compliance deformations result in inaccuracies in the geometry of the machined part. Therefore, prediction of compliance deformation helps to determine the geometrical quality. To fill in the knowledge gap, this paper presents a compliance-induced geometrical error prediction method based on a semi-analytical stiffness model.

Parallel Kinematic Machine (PKM) is a new type of machine tool, which has the potential to fill in the gap between traditional CNC machines and industrial robots, due to its flexibility and superior motion dynamics. Stiffness is an essential property of a machine tool, as it will affect the machining capability. Although much research has been conducted on stiffness modelling and analysis, most of them employ simplified models and gravity effects have not been well considered or characterized for PKMs. To fill in the knowledge gap, this paper introduces a new experimental stiffness measurement method considering the effect of gravity on the machine tool. An experimental procedure was developed in order to separate the gravity effects on stiffness from the machine structure.

There has been an enormous increase in using of carbon fiber reinforced thermoplastic (CFRTP) especially carbon fiber reinforce polyetherketoneketone (CF/PEKK) in automotive and aeronautical industries. However, fundamental material removal mechanism of such material has never been elucidated in the literature. In this work, finite-element (FE) method is deployed and microscale numerical model considering fiber, matrix and interface has been established to understand the mechanisms of chip formation in orthogonal cutting of unidirectional (UD) thermoplastic CF/PEKK composites. Chip formation and subsequent surface / subsurface damage with different fiber orientations (0°, 45°, 90°, 135°) are modelled and compared. Results suggest that, for CF/PEKK, the chip formation mechanism is significantly affected by the fiber orientation and the most severe subsurface damage can be seen at fiber orientation 135°, as a result of bending fracture below the ideal machined surface.

Thin-walled parts with double-sided features are widely used in many industrial sectors but their machining is particularly time consuming and challenging. Collaborative machining is a new paradigm in the development of industrial 4.0. It can potentially revolutionize the existing thin-walled part machining methods, leading to higher productivity, flexibility and sustainability. For this end, this paper introduces a novel concept with dual parallel kinematic machines (PKMs) collaboratively performing synchronized and asynchronized cutting and support from both sides of a thin-walled part, without changeover/re-clamping of the workpiece. Compared to the conventional single-sided machining, this study shows that static and dynamic performances of the workpiece are significantly improved under the dual PKM collaborative operation. A case study of milling a thin-walled part with double-sided features was conducted by PKMs under three comparative strategies, namely, double-sided synchronized milling, alternative single-sided milling, and sequential single-sided milling. Experimental results show that the novel double-sided synchronized milling strategy by dual collaborative PKMs produced the best dimensional accuracy and satisfactory surface quality due to the improved static stiffness and dynamic performance, and balanced deflections. More importantly, a two-fold greater productivity has been achieved as the novel strategy doubles the material removal rate while eliminating the cumbersome in-process steps used in conventional single-sided machining.

This work reports a comparative study on different hole making methods, namely conventional drilling, peck drilling and helical milling, for Al 2024-T3/Ti-6Al-4V stacks in aircraft applications. The impacts of different hole making methods with constant or varied machining parameters across the stacked structures have been investigated. The resulting exit burr, hole surface roughness/microstructural change and fatigue behaviour of the machined stacks have been characterized in detail. Results show that the exit burr formation is most severe for conventional drilling and least burr is produced in helical milling coupons. Deploying varying parameters (i.e. optimal parameters for each individual metal layer) across the stacks can effectively reduce the burr formation in conventional drilling and peck drilling. 3D surface morphology shows that Al 2024-T3 hole surface contains multiple scratches and trenches, while Ti-6Al-4V hole surface features regular feed marks. Helical milling leads to the highest Al 2024-T3 hole surface roughness, which can be attributed to the abrasion caused by the evacuated Ti-6Al-4V chips. Sub-surface microstructural analysis shows that the Ti-6Al-4V layer is more prone to machining-induced microstructural change (i.e. white layer formation and/or grain plastic deformation along machining direction). The relatively low fatigue performance of stacks produced by conventional drilling and peck drilling with constant parameters can be related to the presence of the brittle Ti-6Al-4V white layer in these coupons. Deploying varied parameters across stacks in conventional drilling and peck drilling can effectively eliminate Ti-6Al-4V white layer formation and improve the stacks fatigue life by 72% and 38%, respectively. Helical milling leads to the longest stack fatigue life (~ 100% and 40% greater than conventional drilling and peck drilling, respectively).

Here, we report the first study on the hole making performance of thermoplastic carbon fibre/polyetherketoneketone (CF/PEKK) composite. Different hole making methods (conventional drilling vs. helical milling) have been compared and the effect of different feed rates has been investigated. The effect of thermal-mechanical interaction on the resulting hole damage has been elucidated for the first time for carbon fibre reinforced thermoplastics (CFRTPs) hole making. In the material science dimension, advanced material characterization techniques have been deployed to reveal the material removal mechanisms at microscopic scale and unveil the underlying material structural change at a molecular level. Results show that the delamination damage of CF/PEKK is a result of the thermal-mechanical interaction. For conventional drilling, the high machining temperature (at low feed rate <0.1 mm/rev) has a stronger influence on the delamination damage and the delamination starts to show stronger dependence on the thrust force at high feed rate >0.1 mm/rev. In contrast, helical milling generates a much higher machining temperature which plays a more predominant role in the associated delamination damage. Microstructural analysis shows that all the hole surfaces feature matrix smearing, as a result of combined in-plane shear stress and high machining temperature. Conventional drilling leads to more severe hole wall microstructural damage (matrix loss and surface cavity) as compared to helical milling. Finally, thermal analysis reveals that the hole making process has led to significantly increased crystallinity in the PEKK matrix as a result of the strain-induced crystallization under the combined effect of shear stress and high temperature.

This work introduces a cost modelling architecture in order to determine the cost-effectiveness of the latest joining technology developments. Riveting is the conventional joining method in the aerospace industry, but is a time-consuming, expensive process that adds excessive weight to a structure. As part of the JTI Clean Sky 2 Joint Technology Initiative, the OASIS project (“Optimization of Friction Stir Welding (FSW) and Laser Beam Welding (LBW) for assembly of structural aircraft parts”) aims to demonstrate the feasibility and cost-effectiveness of novel joining technologies. The technologies being investigated are LBW, FSW and Friction Stir Spot Welding (FSSW). Physical demonstrators, simulation studies and access to industry leading technical expertise from OASIS project partners have helped develop detailed production process maps and input accurate process metrics to determine manufacturing costs. To this end, an activity-based cost modelling architecture has been developed to predict the cost-effectiveness of the joining technologies and assess them against both manual and automatic riveted solutions. The model has been designed in a manner that enables integration into current manufacturing eco-systems, has scalability for large aerospace companies and the ability to perform multi-fidelity process cost models that can be integrated with one another as required.

Point cloud registration is necessary to join multiple laser scanned data, but researchers have not treated point cloud registration in aircraft assembly in much detail. When applying laser scan metrology into industries, the effects of registration uncertainty on high precision assembly accuracy are not negligible. Based on the use of laser scanner and commercial software PolyWorks, this study investigates the registration uncertainty between part-level and assembly-level point cloud data in aircraft wing assembly. A spar-and-skin assembly with sphere artefacts is used as a case study. Registration uncertainty in gap measurement is also investigated. Results show that: (1) the use of sphere artefacts cannot improve registration accuracy in PolyWorks, but can improve efficiency. (2) Registration process could bring errors and these errors would be affected by the parameters settings during data processing. (3) Systematic errors would be associated with both part-level and assembly-level measurements, and calibration should be applied to eliminate their effects on the measurement of the desired dimensions, i.e. the gap size in this case. It is concluded that laser scanner and computational software can be used for high precision assembly, and evaluating registration uncertainty is a crucial step to improve assembly accuracy.

In this work, two methods for predictive shimming based on laser scan technology and point cloud processing software have been presented. The first one is direct measurement method in PolyWorks, and the second one is feature-based modelling method in SolidWorks. Firstly, both component-level and assembly-level scans are collected by a laser scanner. Then, the point clouds are post-processed and registered in PolyWorks. Thirdly, gap information (shape and thickness) is obtained either in PolyWorks or in SolidWorks. Results show that the proposed methods can obtain gap information successfully based on current hardware and software.