Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Nov 24, 2022
Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to in... more Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to induced cutting forces resulting in machining error (dimensional inaccuracy of the machined surface). The development of smart fixtures is seen as an enabler for reduction of machining error. To reduce lead times, the smart fixtures need to be designed in an efficient way by using more virtual simulations and less physical iterations. This paper presents the development of a novel methodology for machining error prediction in milling of a fixture-workpiece system. The methodology integrates a cutting force model, a finite element based fixture-workpiece system and a multi-step error predictive approach. The methodology was first validated on a flexible thin-wall Ti6Al4V slender part where less than 6% difference was achieved between predicted and measured machining error. The difference between predicted and measured cutting forces was approximately 6%. After the gained confidence, the methodology was applied to the flexible thin-wall Ti6Al4V slender part encompassed by a fixture with three actuators acting as supports. The predicted machining error was reduced from the range of 0.2–0.33 mm (no actuators) to the range of 0.12–0.14 mm (with three actuators). This demonstrated the capability of the developed methodology to aid the design of future smart fixtures with the potential to reduce lead times during their development.
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to in... more Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to induced cutting forces resulting in machining error (dimensional inaccuracy of the machined surface). The development of smart fixtures is seen as an enabler for reduction of machining error. To reduce lead times, the smart fixtures need to be designed in an efficient way by using more virtual simulations and less physical iterations. This paper presents the development of a novel methodology for machining error prediction in milling of a fixture-workpiece system. The methodology integrates a cutting force model, a finite element based fixture-workpiece system and a multi-step error predictive approach. The methodology was first validated on a flexible thin-wall Ti6Al4V slender part where less than 6% difference was achieved between predicted and measured machining error. The difference between predicted and measured cutting forces was approximately 6%. After the gained confidence, the met...
This article shows how changing 3D printing parameters and using bio‐inspired lattice chambers ca... more This article shows how changing 3D printing parameters and using bio‐inspired lattice chambers can engineer soft pneumatic actuators (SPAs) with different behaviors in terms of controlling tip deflection and tip force using the same input air pressure. Fused deposition modeling (FDM) is employed to 3D print soft pneumatic actuators using varioShore thermoplastic polyurethane (TPU) materials with a foaming agent. The effects of material flow and nozzle temperature parameters on the material properties and stiffness are investigated. Auxetic, columns, face‐centered cubic, honeycomb, isotruss, oct vertex centroid, and square honeycomb lattices are designed to study actuators’ behaviors under the same input pressure. Finite‐element simulations based on the nonlinear hyper‐elastic constitutive model are carried out to precisely predict the behavior, deformation, and tip force of the actuators. A closed‐loop pneumatic system and sensors are developed to control the actuators. Results show...
A novel defect-based fatigue model for the prediction of S–N (stress versus number of cycles) dat... more A novel defect-based fatigue model for the prediction of S–N (stress versus number of cycles) data points and curves is proposed in this paper. The model is capable of predicting the material fatigue performance based on defect size and location from the surface. A defect factor was introduced and obtained based on notch theory, which considers the notch sensitivity of the material as well as the stress concentration obtained using the finite element method. A newly developed equation was applied to represent the relationship between the defect factor, defect size and defect location from the surface. AlSi10Mg samples were manufactured using laser powder bed fusion, and then machined. The samples were tested under rotational bending cyclic loading until failure. The failed samples were analysed using scanning electron microscopy and it was found that cracks initiated from defects located at the surface. The measured defect size and location were used to predict the number of cycles ...
The International Journal of Advanced Manufacturing Technology
Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufactu... more Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been eval...
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
This paper presents numerical analyses of a welding simulation of a bogie frame side beam. The si... more This paper presents numerical analyses of a welding simulation of a bogie frame side beam. The simulation is based on an analytical thermal model coupled with a non-linear structural finite element model using shell elements enabling the welding simulation of large structures. The predicted clamping forces, distortions, and residual stresses for different clamping conditions and plate thicknesses are analysed in terms of manufacture. A new fatigue model based on the endurance limit approach is proposed using residual stresses to predict the S-N curves. The predicted S-N curves with the proposed model showed close correlation with the S-N curves for class F and class F2 welds of the BS7608 standard, demonstrating its validity and potential use in design.
The International Journal of Advanced Manufacturing Technology, 2021
This paper presents the design of a multi-agent framework that aids engineers in the adaptation o... more This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify...
This paper presents case studies of additive manufacturing process chains including laser powder ... more 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 model...
International Journal of Automation Technology, 2011
The paper investigates the effects of turning-induced and mapped Residual Stresses (RSs) for a Ti... more The paper investigates the effects of turning-induced and mapped Residual Stresses (RSs) for a Ti6Al4V disc subjected to centrifugal loading. The turninginduced RSs are predicted in orthogonal cutting using the Finite Element Method (FEM). The FE predicted RSs are validated after performing face turning followed by hole drilling and x-ray diffraction measurements. Numerical analyses are carried out at different Cutting Velocities (CVs) to obtain the RS profiles. The results show that the compressive RSs increase by increasing the CV and the depth of cut. A disc subjected to a centrifugal load is modelled using the FEM where the turning-induced RSs are introduced as an initial condition using mapping techniques. The predicted CV-dependent RS profiles are incorporated into the mapping techniques using the shape function approach. It is observed that the stress amplitudes at the points at which failure occurs are lower when the mapped turning-induced RS profiles are considered in the d...
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Nov 24, 2022
Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to in... more Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to induced cutting forces resulting in machining error (dimensional inaccuracy of the machined surface). The development of smart fixtures is seen as an enabler for reduction of machining error. To reduce lead times, the smart fixtures need to be designed in an efficient way by using more virtual simulations and less physical iterations. This paper presents the development of a novel methodology for machining error prediction in milling of a fixture-workpiece system. The methodology integrates a cutting force model, a finite element based fixture-workpiece system and a multi-step error predictive approach. The methodology was first validated on a flexible thin-wall Ti6Al4V slender part where less than 6% difference was achieved between predicted and measured machining error. The difference between predicted and measured cutting forces was approximately 6%. After the gained confidence, the methodology was applied to the flexible thin-wall Ti6Al4V slender part encompassed by a fixture with three actuators acting as supports. The predicted machining error was reduced from the range of 0.2–0.33 mm (no actuators) to the range of 0.12–0.14 mm (with three actuators). This demonstrated the capability of the developed methodology to aid the design of future smart fixtures with the potential to reduce lead times during their development.
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to in... more Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to induced cutting forces resulting in machining error (dimensional inaccuracy of the machined surface). The development of smart fixtures is seen as an enabler for reduction of machining error. To reduce lead times, the smart fixtures need to be designed in an efficient way by using more virtual simulations and less physical iterations. This paper presents the development of a novel methodology for machining error prediction in milling of a fixture-workpiece system. The methodology integrates a cutting force model, a finite element based fixture-workpiece system and a multi-step error predictive approach. The methodology was first validated on a flexible thin-wall Ti6Al4V slender part where less than 6% difference was achieved between predicted and measured machining error. The difference between predicted and measured cutting forces was approximately 6%. After the gained confidence, the met...
This article shows how changing 3D printing parameters and using bio‐inspired lattice chambers ca... more This article shows how changing 3D printing parameters and using bio‐inspired lattice chambers can engineer soft pneumatic actuators (SPAs) with different behaviors in terms of controlling tip deflection and tip force using the same input air pressure. Fused deposition modeling (FDM) is employed to 3D print soft pneumatic actuators using varioShore thermoplastic polyurethane (TPU) materials with a foaming agent. The effects of material flow and nozzle temperature parameters on the material properties and stiffness are investigated. Auxetic, columns, face‐centered cubic, honeycomb, isotruss, oct vertex centroid, and square honeycomb lattices are designed to study actuators’ behaviors under the same input pressure. Finite‐element simulations based on the nonlinear hyper‐elastic constitutive model are carried out to precisely predict the behavior, deformation, and tip force of the actuators. A closed‐loop pneumatic system and sensors are developed to control the actuators. Results show...
A novel defect-based fatigue model for the prediction of S–N (stress versus number of cycles) dat... more A novel defect-based fatigue model for the prediction of S–N (stress versus number of cycles) data points and curves is proposed in this paper. The model is capable of predicting the material fatigue performance based on defect size and location from the surface. A defect factor was introduced and obtained based on notch theory, which considers the notch sensitivity of the material as well as the stress concentration obtained using the finite element method. A newly developed equation was applied to represent the relationship between the defect factor, defect size and defect location from the surface. AlSi10Mg samples were manufactured using laser powder bed fusion, and then machined. The samples were tested under rotational bending cyclic loading until failure. The failed samples were analysed using scanning electron microscopy and it was found that cracks initiated from defects located at the surface. The measured defect size and location were used to predict the number of cycles ...
The International Journal of Advanced Manufacturing Technology
Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufactu... more Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been eval...
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
This paper presents numerical analyses of a welding simulation of a bogie frame side beam. The si... more This paper presents numerical analyses of a welding simulation of a bogie frame side beam. The simulation is based on an analytical thermal model coupled with a non-linear structural finite element model using shell elements enabling the welding simulation of large structures. The predicted clamping forces, distortions, and residual stresses for different clamping conditions and plate thicknesses are analysed in terms of manufacture. A new fatigue model based on the endurance limit approach is proposed using residual stresses to predict the S-N curves. The predicted S-N curves with the proposed model showed close correlation with the S-N curves for class F and class F2 welds of the BS7608 standard, demonstrating its validity and potential use in design.
The International Journal of Advanced Manufacturing Technology, 2021
This paper presents the design of a multi-agent framework that aids engineers in the adaptation o... more This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify...
This paper presents case studies of additive manufacturing process chains including laser powder ... more 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 model...
International Journal of Automation Technology, 2011
The paper investigates the effects of turning-induced and mapped Residual Stresses (RSs) for a Ti... more The paper investigates the effects of turning-induced and mapped Residual Stresses (RSs) for a Ti6Al4V disc subjected to centrifugal loading. The turninginduced RSs are predicted in orthogonal cutting using the Finite Element Method (FEM). The FE predicted RSs are validated after performing face turning followed by hole drilling and x-ray diffraction measurements. Numerical analyses are carried out at different Cutting Velocities (CVs) to obtain the RS profiles. The results show that the compressive RSs increase by increasing the CV and the depth of cut. A disc subjected to a centrifugal load is modelled using the FEM where the turning-induced RSs are introduced as an initial condition using mapping techniques. The predicted CV-dependent RS profiles are incorporated into the mapping techniques using the shape function approach. It is observed that the stress amplitudes at the points at which failure occurs are lower when the mapped turning-induced RS profiles are considered in the d...
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Papers by Shukri Afazov