Chatter model for enabling a digital twin in machining

This paper presents the development of a new chatter model using measured cutting forces instead of a mathematical model with empirical nature that describes them. The utilisation of measured cutting forces enables the prediction of real-time chatter conditions and stable machining. The chatter model is validated using fast Fourier transform (FFT) analyses for detection of chatter. The key contribution of the developed chatter model is that it can be incorporated in digital twins for process monitoring and control in order to achieve greater material removal rates and improved surface quality in future industrial applications involving machining processes

Comparative study of stability predictions in micro-milling by using cutting force models and direct cutting force measurements

Chatter vibration in micro-milling is critical for the breakage of the cutting tools. Therefore, the dynamic stability is important and chatter vibration should be avoided. This paper presents a comparative study between two models for prediction of chatter. The difference between the two models is in the source of cutting forces. The first chatter model uses a mathematical cutting force model while the second chatter model uses direct measured cutting forces. Both chatter models are solved in the time domain and the same criteria for chatter is applied. The results showed that the chatter model using direct measured cutting forces was in better agreement with fast Fourier transform analyses

A comparison of predicted distortion of a manifold fabricated by laser powder bed fusion using solid and shell element-based finite element models

This paper compares the predicted distortion of a manifold geometry fabricated by laser powder bed fusion between an established finite element model using solid elements and a newly developed in this paper finite element model using shell elements. The developed finite element models utilized two methods to induce a strain field (inherent strain and analytical thermal methods). The predicted distortions from these models were also compared with experimentally measured distortions. The results showed that the predicted distortion using solid elements is more suitable to predict the buckling effect on the manifold geometry. Despite that, the model using shell elements showed an accurate prediction of distortion in many areas of the manifold, and it proved to be computationally more efficient (2.4 times faster), this model showed lower accuracy in the prediction of distortion generated by buckling. However, shell elements could be used in other applications where the bucking is not the driving mechanism for the prediction of distortion in laser powder bed fusion or in applications where the accuracy of distortion is not a requirement (e.g., support structures)

Modular reconfiguration of flexible production systems using machine learning and performance estimates

YesThis paper presents an agent-based framework for reconfiguring modular assembly systems using machine learning and system performance estimates based on previous reconfigurations. During a reconfiguration, system integrators and engineers make changes to the machine to meet new production requirements by increasing capacity or manufacturing new product variants. The framework provides a method for automatically evaluating these changes in terms of impact on the performance of the production system, and building a knowledge base. Such knowledge is used to support future reconfigurations by recommending changes that are likely to improve the performance based on previous reconfigurations. The agent architecture of the framework has two levels, one for individual assembly stations and one for the entire production line. Knowledge bases of changes are built and utilised at both levels using machine learning and performance estimates. A prototype implementation of the proposed framework has been evaluated on an assembly production system in an industrial scenario. Preliminary results show that framework helps to reduce the time and resources required to complete a system reconfiguration and reach the desired production objectives.This work was supported by the SURE Research Projects Fund of the University of Bradford and the European Commission [grant agreement n. 314762]

Maturity assessment of laser powder bed fusion process chain modelling and simulation

This paper presents case studies of additive manufacturing process chains including laser powder bed fusion and post-processes. The presented case studies are used to assess the maturity of the manufacturing process chains using a Modelling and Simulation Readiness Level Scale. The results from the assessment have shown that the maturity of the modelling and simulation of laser powder bed fusion process chains lies between the stage of applied research and development and the stage of being instrumental, with high reliance on modelling and simulation experts. This means that the laser powder bed fusion (L-PBF) process chain modelling and simulation can support low-risk development, with high reliance on modelling and simulation experts, making them suitable for qualitative assessment, alternative design/solution ranking, defining the design structure, constraining the design space and replacing some experimental trials. This shows that further maturation is required before the modelling and simulation methods and codes are well recognised as best practice in the industry and are part of operational process control at any stage of the supply chain

Process chain simulation of laser powder bed fusion including heat treatment and surface hardening

Additive manufacturing (AM) has enabled the creation of geometrically complex parts for a range of industries. However, the nature of AM often requires multiple post processing techniques to be carried out to reach the desired material properties or required surface finish. This often involves heat treatment (HT), shot peening (SP) or laser shock peening (LSP). To date, hardly any process chain modelling has been carried out on manufacturing applications with AM. This investigation focuses on the finite element (FE) modelling of the complete manufacturing process chain of an AM impeller made of IN718, including the AM, HT, LSP and SP processes. The particular AM process applied to build the impeller is laser powder bed fusion (L-PBF). Each FE process is validated individually against experimental data before being applied to the impeller process chain. The validated data from each process is mapped to the next process in the chain to investigate the combined effects of manufacturing and post processing techniques. Results have shown that high tensile residual stresses induced by AM can be reduced by approximately 75% by applying HT. SP and LSP processes can further modify remaining tensile residual stresses after HT by inducing a layer of compressive stresses at the surface. In summary, this research work has demonstrated that the simulation of AM process chains using finite element techniques is sufficiently mature to support the product and process development of industrial AM components

A review of challenges and framework development for corrosion fatigue life assessment of monopile-supported horizontal-axis offshore wind turbines

Digital tools such as machine learning and the digital twins are emerging in asset management of offshore wind structures to address their structural integrity and cost challenges due to manual inspections and remote sites of offshore wind farms. The corrosive offshore environments and salt-water effects further increase the risk of fatigue failures in offshore wind turbines. This paper presents a review of corrosion fatigue research in horizontal-axis offshore wind turbines (HAOWT) support structures, including the current trends in using digital tools that address the current state of asset integrity monitoring. Based on the conducted review, it has been found that digital twins incorporating finite element analysis, material characterisation and modelling, machine learning using artificial neural networks, data analytics, and internet of things (IoT) using smart sensor technologies, can be enablers for tackling the challenges in corrosion fatigue (CF) assessment of offshore wind turbines in shallow and deep waters

A methodology for precision manufacture of a nozzle using hybrid laser powder-bed fusion: a case study

This paper presents a methodology for precision manufacture of a nozzle using the hybrid laser powder-bed fusion (L-PBF) technology. A new distortion predictive model for hybrid L-PBF was developed using finite element techniques. The model was validated against experimentally measured distortion using optical scanning. The validated model was utilised to mitigate distortion by applying additional stiffeners to the nozzle geometry where the distortion was reduced by 60% to acceptable tolerance of ±200 µm. Micro-milling trials were conducted to identify optimum cutting parameters delivering high material removal rates while maintaining the average surface roughness of less than 1 µm. Finally, a nozzle with reduced distortion and desirable surface finish was manufactured, which has been used in industrial research context for process and product development of food products

Data-driven train set crash dynamics simulation

© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency

Adaptive reversible composite-based shape memory alloy soft actuators

This research demonstrates how a combination of two-way shape memory alloy (SMA), low-temperature liquid epoxy cure composites, and fibre reinforced plastic (FRP) may be utilised to create a novel reversible actuator with built driven functionality. The novelty of this work is that the actuator can reverse its original shape and be mounted on different customized structures. The strategy is based on a knowledge of SMA wires and the manufacturing principle underlying composite structure, as well as experiments to see how soft SMA-FRP can be programmed to bend. The folding mechanism is studied in terms of fabrication factors such as SMA training and strong interfacial bonding between SMA and epoxy resin, which influence the programming process and shape change. The two-way SMA wires are trained using the pre-straining method to programme the SMAs. The technique has been used to assemble the SMA wires with bond reliability to enhance the actuator interface's thermal behaviour. The SMA elements are directly inserted into FRP strips and epoxy resin is used as an adhesive, resulting in dynamic hybrid composites. The module is actuated using an electrical board with a current value between 3 and 6 A. The robustness, controllability, mechanical properties, and 500 life cycles of the actuator are tested. Results indicate a bending angle of 58° with 30 mm of deflection in 7 s after actuating the module. Also, 3D printing is used to print a gripper inspired by human fingers and a structure to lift various weights. The actuator’s performance as a soft gripper is reliable in terms of grasping objects of different shapes