The effect of single crystal and welded substrates on the development of braze microstructures

To emulate combined welding and brazing techniques, frequently used during the rejuvenation of turbine components, TIG and SWET weld overlays have been applied to the surfaces of a single crystal nickel-based alloy and subsequently overlain with braze and subjected to a typical brazing cycle heat treatment. Both welds comprised of columnar dendritic microstructures with an epitaxial [001] growth orientation near the substrate surface and equiaxed randomly oriented grains at the weld surface. While the TIG weld showed a sharp columnar to equiaxed transition, that for the SWET weld was diffuse and comprised colonies of columnar structures with their [001] misoriented to the epitaxial columnar directions as a result of shallower temperature gradients. Analysis of the microstructures of the brazed materials showed that the extents of boron penetration were significantly greater for the welds due to enhanced diffusion along grain boundaries between the equiaxed structures compared to y channel limited transport in the single crystal . A striking feature of the diffusion zone microstructures was the development of carbo-boride needles which formed by the reaction of boron, refractory metals and TaC. It is the release of Ta from TaC which stabilises y’ envelope formation around the needles. The post-brazed microstructures of TIG welds used in this study showed considerably fewer detrimental boride precipitates than brazed SWET welds and as a result are expected to show superior mechanical properties

Using open-source microcontrollers to enable digital twin communication for smart manufacturing

One of the key enabling technologies for the smart factory is the Digital Twin, which can be described as a digital model of a physical entity. In smart manufacturing, a digital twin, also referred to as a cyber-physical model of a physical entity. In smart manufacturing, a digital twin, also referred to as a cyber-physical production system, comprises three integrated components: the product, the process and the machine. In this paper, the design, fabrication and development of a proof of principal manufacturing digital twin demonstrator is discussed. The manufacturing process is a V-bending operation of a thin metallic plate. All three components of a manufacturing digital twin are represented: the metallic plate product, the bending process and the bending machine. Low-cost, IoTenabled, open-source microcontrollers are used to communicate between the physical and the digital twins. The microcontroller is used to control machine operations, while also extracting sensor data from the system. It is demonstrated that the digital twin enables real-time stress prediction in the product during the bending process, while also tracking and recording machine performance. An information dashboard has also been developed which presents key performance indicators to the end user

Diffusion braze homogenisation and contraction during re-repair heat treatments of a single crystal nickel-based superalloy

In this work, nickel-based braze reservoirs embedded in single crystal nickel-based superalloy substrates were cycled between one and four braze diffusion heat treatments to analyse the effect on microstructural transformation of the braze and substrate materials. Nickel-based superalloy turbine components can undergo several braze repair cycles during service life, and little has been reported on how previously repaired braze joints are affected in additional heat treatments. 1 mm wide braze joints with an initial composition of 6.8 at.% boron embedded in a single crystal substrate do not homogenise when brazed for 2.5 h above 1150 C, resulting in the formation of eutectic boride phases. Subsequent heat treatments caused braze remelting and contraction leading to surface depressions with the potential to act as a stress raiser for crack nucleation, possibly requiring reworking which impacts on both profits and turnaround times. After four braze diffusion heat treatments, the braze had still not fully homogenised, even though four discrete bands of boride precipitates, separated by boride-free zones, could be observed in g channels, indicating boron removal from the braze reservoir. Analysis of the metallurgy at the brazesubstrate interface and the diffusion-affected zones showed that the most probable reason for controlling boron removal from the braze were: 1) decreasing boron concentration gradient across a thickening braze-substrate interface; 2) restricted boron transport across boride precipitates in the diffusionaffected zones; 3) slower transport of boron in g’, the area fraction of which changes over the braze cycle temperature rang

Using finite element analysis to develop a digital twin of a manufacturing bending operation

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach

Optimisation of ultrasonically welded joints through machine learning

The quality of joint achievable through ultrasonic welding is highly dependent on the process input parameters. In this study an artificial neural network (ANN) is combined with a genetic algorithm (GA) to develop a high-fidelity model for predicting the strength of ultrasonically welded joints. Initial weights of the ANN were optimized using the GA. The model was then trained by the Levenberg-Marquardt algorithm on 27 training experiments and validated on 10 experiments. The model demonstrated a high level of accuracy with a mean relative error of 6.79% on validation data and a correlation coefficient of 0.9827 for all 37 experiments

A model-based approach to automated validation and generation of PLC code for manufacturing equipment in regulated environments

Validation is a critical stage of the equipment design process as it provides documentary evidence that the equipment is performing as per specification and ensures consistent product quality is maintained at all times. The advent of Industry 4.0 has led to a requirement for reconfigurable manufacturing systems as manufacturers adapt to an increased customer demand for personalised products. As equipment control software becomes increasingly complex to accommodate these equirements, a new approach to equipment validation is required. This paper presents a methodology for the design and validation of equipment in regulated manufacturing environments, using a model-based design platform (MathWorks® Simulink®) to model and digitally validate the Programmable Logic Controller (PLC) code required to control manufacturing equipment. A workflow is presented detailing the steps required to implement this approach and a demonstration model was developed as a proof of concept. Validation documentation and PLC code are automatically generated based on the system model and the functionality of the generated PLC code was successfully verified on a physical demonstrator, proving the feasibility of the proposed approach. Adoption of the approach outlined in this work would enable manufacturers in regulated industries, such as medical devices and pharmaceutical products, to rapidly design, build, reconfigure and revalidate manufacturing equipment as required to accommodate an increased demand for customised products. </p

Digital twin: origin to future

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology

3D printing of photocurable resin reinforced by functionalised graphene nanoplatelets

The influence of functionalised graphene nanoplatelets with melamine on the thermal and mechanical properties of a 3D-printed photopolymerisable resin is investigated. In this work, a liquid-based 3D printer and stereolithography were employed to fabricate the 3D-printed parts, and a commercial dimethacrylate-based resin was used. The 3D-printed parts were subjected to ultraviolet and thermal post-curing stages to improve thermal and mechanical behaviour. The quality of the graphene nanoplatelets’ functionalisation was characterised by Fourier transform infrared spectroscopy and thermogravimetric analysis. Thermal and mechanical characterisations were performed via thermogravimetric, tensile, and Izod impact tests. The fractured surfaces were observed via scanning electron microscopy. The degree of graphene nanoplatelet dispersion in the polymer matrix is enhanced by bonding with melamine via π–π interactions and inhibited surface defect formation. Results show property enhancements of up to 35% in tensile strength, 78% in impact strength, and 38% in residual weight at 400 ◦C.</p

A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model

Robotic drilling has advantages over traditional computer numerical control machines due to its flexibility, dexterity and the potential for rapid production and process automation. The dexterity and reach of the robotic drill end effector enables the efficient drilling of large composite components, such as aircraft wing structures. Due to the anisotropy and inhomogeneity of fibre reinforced polymer composite materials, drilling remains a challenging task. Inspection of the drilled hole is required at the end of the process to ensure the final product is free from defects. Typically, such inspections require the parts to be transferred to a dedicated inspection station, which is a time-consuming non-value-added task and impractical for large components. In the interest of an efficient and sustainable manufacturing process, this work proposes a hybrid classification model implemented with a robotic drilling system to investigate the quality of drilled holes in-situ. The classifier is trained and tested with a random selection of drilled holes and the most accurate classifier is implemented. The selected classifier returns 90% overall prediction accuracy on unseen drilled holes. This machine learning based approach, using a convolutional neural network and support vector machine classifier, can significantly improve inspection reliability while reducing production time for drilled composite components. This is the first study that demonstrates a hole quality assessment technique for robotic drilling of composite material in-situ at scale.</p

Investigation of thermal, mechanical and shape memory properties of 3D-printed functionally graded nanocomposite materials †

In this study, a 3D-printed photocurable resin was developed by incorporating graphene nanoplatelets functionalised with melamine to investigate the thermal, mechanical, fracture and shape memory behaviours. The objective of this work was to produce a printed functionally graded nanocomposite material that has a smart temperature-responsive structure; presents good thermal stability, strength and fracture toughness; and can demonstrate shape-changing motions, such as sequential transformations, over time. The functionalised graphene nanoplatelets were examined via thermogravimetric analysis, Fourier transform infrared spectroscopy, Raman spectroscopy and ultraviolet–visible spectroscopy. Thermogravimetric analysis showed that the degradation temperature of the nanocomposite containing 0.1 wt% of functionalised graphene nanoplatelets at the weight loss of 5% was 304 ◦C, greater than that of the neat one by 29%. Dynamic mechanical analysis results showed property enhancements of the storage modulus and glass transition temperature. Fracture toughness, tensile strength and impact resistance were improved by 18%, 35% and 78%, respectively. The shape memory tests were performed to obtain the temperature-time recovery behaviour of the 3D-printed structures. The addition of functionalised graphene nanoplatelets demonstrated an enhancement in the shape recovery ratios. Generally, the five subsequent cycles were notably stable with a high recovery ratio of 97–100% for the flat shape and circular shape of the M-GNP specimens. On the other hand, these values were between 91% and 94% for the corresponding neat specimens.</p