| 1. |
- Fan, Wei, et al.
(författare)
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A Data-Driven Machining Error Analysis Method for Finish Machining of Assembly Interfaces of Large-Scale Components
- 2021
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Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:4
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Tidskriftsartikel (refereegranskat)abstract
- To guarantee the final assembly quality of the large-scale components, the assembly interfaces of large components need to be finish-machined on site. Such assembly interfaces are often in low-stiffness structure and made of difficult-to-cut materials, which makes it hard to fulfill machining tolerance. To solve this issue, a data-driven adaptive machining error analysis and compensation method is proposed based on on-machine measurement. Within this context, an initial definite plane is fitted via an improved robust iterating least-squares plane-fitting method based on the spatial statistical analysis result of machining errors of the key measurement points. Then, the parameters of the definite plane are solved by a simulated annealing-particle swarm optimization (SA-PSO) algorithm to determine the optimal definite plane; it effectively decomposes the machining error into systematic error and process error. To reduce these errors, compensation methods, tool-path adjustment method, and an optimized group of cutting parameters are proposed. The proposed method is validated by a set of cutting tests of an assembly interface of a large-scale aircraft vertical tail. The results indicate that the machining errors are successfully separated, and each type of error has been reduced by the proposed method. A 0.017 mm machining accuracy of the wall-thickness of the assembly interface has been achieved, well fulfilling the requirement of 0.05 mm tolerance.
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| 2. |
- Hu, Cheng, et al.
(författare)
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On the steady-state workpiece flow mechanism and force prediction considering piled-up effect and dead metal zone formation
- 2021
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Ingår i: Journal of Manufacturing Science and Engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:4
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Tidskriftsartikel (refereegranskat)abstract
- The manufacturing of miniaturized components is indispensable in modern industries, where the uncut chip thickness (UCT) inevitably falls into a comparable magnitude with the tool edge radius. Under such circumstances, the ploughing phenomenon between workpiece and tool becomes predominant, followed by the notable formation of dead metal zone (DMZ) and piled-up chip. Although extensive models have been developed, the critical material flow status in such microscale is still confusing and controversial. In this study, a novel material separation model is proposed for the demonstration of workpiece flow mechanism around the tool edge radius. First, four critical positions of workpiece material separation are determined, including three points characterizing the DMZ pattern and one inside considered as stagnation point. The normal and shear stresses as well as friction factors along the entire contact region are clarified based on slip-line theory. It is found that the friction coefficient varies symmetrically about the stagnation point inside DMZ and remains constant for the rest. Then, an analytical force prediction model is developed with Johnson-Cook constitutive model, involving calibrated functions of chip-tool contact length and cutting temperature. The assumed tribology condition and morphologies of material separation including DMZ are clearly observed and verified through various finite element (FE) simulations. Finally, comparisons of cutting forces from cutting experiments and predicted results are adopted for the validation of the predictive model.
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| 3. |
- Koji, Kimita, et al.
(författare)
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A Method for Remanufacturing Process Planning and Control Using Loosely Coupled Systems
- 2021
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Ingår i: Journal of manufacturing science and engineering. - : ASME. - 1087-1357 .- 1528-8935. ; 143:10, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- Remanufacturing is a crucial component for our societies to move toward a circular economy. Compared with new manufacturing, the distinctive nature of remanufacturing is found to have high variability, high uncertainty and, thereby, complexity. Therefore, remanufacturers need to be able to adapt to the complexity and to flexibly adjust their processes. Especially, the ability to remanufacturing process planning and control is important. However, few practical methods supporting that are available so far. Therefore, this paper aims to propose a method for designing teams and processes in remanufacturing based on the concept of loosely coupled systems. In the proposed method, design structure matrix (DSM) is applied to identify loosely coupled sub-systems that enable to localize impacts of changes within themselves. These sub-systems are also regarded as cross-functional teams that reduce coordination efforts among line departments and, therefore, increase the adaptability against uncertainties. As a preliminary validation, the proposed method was applied to a real case of remanufacturing, and then found to be effective for creating teams and processes for remanufacturing process planning and control depending on given uncertainties.
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| 4. |
- Liu, Sichao, et al.
(författare)
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Function block-based multimodal control for symbiotic human-robot collaborative assembly
- 2021
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Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:9, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- In human–robot collaborative assembly, robots are often required to dynamically changetheir preplanned tasks to collaborate with human operators in close proximity. One essential requirement of such an environment is enhanced flexibility and adaptability, as well asreduced effort on the conventional (re)programming of robots, especially for complexassembly tasks. However, the robots used today are controlled by rigid native codes thatcannot support efficient human–robot collaboration. To solve such challenges, thisarticle presents a novel function block-enabled multimodal control approach for symbiotichuman–robot collaborative assembly. Within the context, event-driven function blocks asreusable functional modules embedded with smart algorithms are used for the encapsulation of assembly feature-based tasks/processes and control commands that are transferredto the controller of robots for execution. Then, multimodal control commands in the form ofsensorless haptics, gestures, and voices serve as the inputs of the function blocks to triggertask execution and human-centered robot control within a safe human–robot collaborativeenvironment. Finally, the performed processes of the method are experimentally validatedby a case study in an assembly work cell on assisting the operator during the collaborativeassembly. This unique combination facilitates programming-free robot control and theimplementation of the multimodal symbiotic human–robot collaborative assembly withthe enhanced adaptability and flexibility.
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| 5. |
- Liu, Sichao, et al.
(författare)
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Multimodal Data-Driven Robot Control for Human-Robot Collaborative Assembly
- 2022
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Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 144:5
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Tidskriftsartikel (refereegranskat)abstract
- In human-robot collaborative assembly, leveraging multimodal commands for intuitive robot control remains a challenge from command translation to efficient collaborative operations. This article investigates multimodal data-driven robot control for human-robot collaborative assembly. Leveraging function blocks, a programming-free human-robot interface is designed to fuse multimodal human commands that accurately trigger defined robot control modalities. Deep learning is explored to develop a command classification system for low-latency and high-accuracy robot control, in which a spatial-temporal graph convolutional network is developed for a reliable and accurate translation of brainwave command phrases into robot commands. Then, multimodal data-driven high-level robot control during assembly is facilitated by the use of event-driven function blocks. The high-level commands serve as triggering events to algorithms execution of fine robot manipulation and assembly feature-based collaborative assembly. Finally, a partial car engine assembly deployed to a robot team is chosen as a case study to demonstrate the effectiveness of the developed system.
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| 6. |
- Ping, Yaoyao, et al.
(författare)
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Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing Under Different Task Arrival Modes
- 2023
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Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 145:8
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Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a service-oriented networked manufacturing model that aims to provide manufacturing resources as services in an on-demand manner. Scheduling is one of the key techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is a critical problem in cloud manufacturing. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to address the issue, which, however, either are incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) as the combination of deep learning (DL) and reinforcement learning (RL) provides an effective technique in this regard. In view of this, we employ a typical DRL algorithm-Deep Q-network (DQN)-and propose a DQN-based approach for multitask scheduling in cloud manufacturing. Three different task arrival modes-arriving at the same time, arriving in random batches, and arriving one by one sequentially-are considered. Four baseline methods including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time (min-time) scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is effective and performs best among all approaches in addressing the multitask scheduling problem in cloud manufacturing.
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| 7. |
- Ramesh Sagar, Vaishak, 1988, et al.
(författare)
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A Robust Design Perspective on Factors Influencing Geometric Quality in Metal Additive Manufacturing
- 2021
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Ingår i: Journal of Manufacturing Science and Engineering, Transactions of the ASME. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:7
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Tidskriftsartikel (refereegranskat)abstract
- Additive manufacturing (AM) for metals is a widely researched, continuously enhanced manufacturing process and is implemented across various industries. However, the AM process exhibits variation that affects the geometric quality of the end product. The effect of process variation on geometric quality is rarely considered during design stages. This paper discusses the various sources that contribute to geometric variation and the prospect of applying robust design method to produce geometry assured AM products. A framework for geometric robustness analysis of AM products is presented as an outcome. This framework would facilitate development of methods and tools to produce geometry assured AM products. The prospects of variation simulation to support geometric robustness analysis and the challenges associated with it are discussed.
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| 8. |
- Ramesh Sagar, Vaishak, 1988, et al.
(författare)
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A Simulation Study on the Effect of Layer Thickness Variation in Selective Laser Melting
- 2023
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Ingår i: Journal of Manufacturing Science and Engineering, Transactions of the ASME. - : ASME International. - 1087-1357 .- 1528-8935. ; 145:2
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Tidskriftsartikel (refereegranskat)abstract
- Selective laser melting (SLM) has gained prominence in the manufacturing industry for its ability to produce lightweight components. As the raw material used is in powder form, the stochastic nature of the powder distribution influences the powder layer thickness and affects the final build quality. In this paper, a multi-layer multi-track simulation study is conducted to investigate the effect of stochastic powder distribution on the layer thickness and plastic strain in a printed geometry. A faster simulation approach is employed to simulate multiple layers. First, the powder distribution and the melt layer thickness of the first layer are obtained from discrete element method (DEM) and computational fluid dynamics (CFD) simulations respectively. Next, the melt layer thickness of the first layer is used as an input to the finite element (FE) based structural mechanics solver to predict the deformation and layer thickness of subsequent layers. Two nominal layer thicknesses 67.4 μm and 20 μm were considered. Two particle size distribution (PSD) configurations and two scanning strategies were tested. The results showed that variation in PSD and scanning strategy leads to variation in layer thickness which in turn leads to variation in the plastic strain that is known to drive the deformation. However, the nominal layer thickness of 20 μm was found to be less influenced by the PSD configuration. The proposed simulation approach and the insights achieved can be used as inputs in the part-scale simulations for geometric robustness evaluation in the early design stages of SLM products.
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| 9. |
- Ramesh Sagar, Vaishak, 1988, et al.
(författare)
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A SIMULATION STUDY ON THE EFFECT OF PARTICLE SIZE DISTRIBUTION ON THE PRINTED GEOMETRY IN SELECTIVE LASER MELTING
- 2022
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Ingår i: Journal of Manufacturing Science and Engineering, Transactions of the ASME. - : ASME International. - 1087-1357 .- 1528-8935. ; 144:5
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Tidskriftsartikel (refereegranskat)abstract
- Selective laser melting (SLM) process is a powder bed fusion additive manufacturing process that finds applications in aerospace and medical industries for its ability to produce complex geometry parts. As the raw material used is in powder form, particle size distribution (PSD) is a significant characteristic that influences the build quality in turn affecting the functionality and aesthetics aspects of the product. This paper investigates the effect of PSD on the printed geometry for 316L stainless steel powder, where three coupled in-house simulation tools based on Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), and Structural Mechanics are employed. DEM is used for simulating the powder bed distribution based on the different powder PSD. The CFD is used as a virtual testbed to determine thermal parameters such as heat capacity and thermal conductivity of the powder bed viewed as a continuum. The values found as a stochastic function of the powder distribution is used to analyse the effect on the melted zone and deformation using Structural Mechanics. Results showed that mean particle size and PSD had a significant effect on the packing density, melt pool layer thickness, and the final layer thickness after deformation. Specifically, a narrow particle size distribution with smaller mean particle size and standard deviation produced solidified final layer thickness closest to nominal layer thickness. The proposed simulation approach and the results will catalyze in development of geometry assurance strategies to minimize the effect of particle size distribution on the geometric quality of the printed part.
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| 10. |
- Sadeghi Tabar, Roham, 1989, et al.
(författare)
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Efficient spot welding sequence simulation in compliant variation simulation
- 2021
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Ingår i: Journal of Manufacturing Science and Engineering, Transactions of the ASME. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:7
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Tidskriftsartikel (refereegranskat)abstract
- Geometrical variation is one of the sources of quality issues in a product. Spot welding is an operation that impacts the final geometrical variation of a sheet metal assembly considerably. Evaluating the outcome of the assembly, considering the existing geometrical variation between the components, can be achieved using the Method of Influence Coefficients (MIC), based on the Finite Element Method (FEM). The sequence with which the spot welding operation is performed influences the final geometrical deformations of the assembly. Finding the optimal sequence that results in the minimum geometrical deformation is a combinatorial problem that is experimentally and computationally expensive. Traditionally, spot welding sequence optimization strategies have been to simulate the geometrical variation of the spot-welded assembly after the assembly has been positioned in an inspection fixture. In this approach, the calculation of deformation after springback is one of the most time-consuming steps. In this paper, a method is proposed where the springback calculation in the inspection fixture is bypassed during the sequence evaluation. The results show a significant correlation between the proposed method of weld relative displacements evaluation in the assembly fixture and the assembly deformation in the inspection fixture. Evaluating the relative weld displacement makes each assembly simulation less time-consuming, and thereby, sequence optimization time can be reduced by up to 30%, compared to the traditional approach.
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