Högskolan i Skövde

The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels.

Predicting part machining deformation is vital for optimizing design and manufacturing processes, thereby enhancing thequality and performance of heavy machinery parts. Traditional numerical methods, such as the finite element method, arelimited by their computational inefficiency. Furthermore, recent data-driven approaches for predicting machining deforma-tion face challenges due to the complex features and variable geometries of parts throughout design iterations and machin-ing processes. To this end, this paper proposes a method, Voxel-FNO, which rapidly predicts machining deformation forparts with variable feature geometry. This method utilizes the Fourier neural operator to capture the underlying mechanisticrelationship between residual stress and machining deformation of parts. Both stress and geometry are sampled by voxelinto standard domain before being input into the neural network model. This approach ensures efficiency and applicability,even as part geometries change. The proposed method is verified in both simulation and real environment, demonstrating itsaccuracy, stability, and generalization capability for varying part geometries, compared to the accurate results from the finiteelement method. It shows prediction max errors of 0.003 mm, 0.002 mm, and 0.018 mm, and RMSE of 0.0003 mm, 0.0002mm, and 0.0013 mm for deformations in X, Y, and Z directions, respectively, compared with FEM results.

The recycling of end-of-life (EOL) products poses significant challenges due to inefficient and unsafe disassembly processes. To address this, we propose a novel self-perception human-robot collaboration (HRC) system that enhances disassembly efficiency and safety through multi-modal human intention recognition. Our core methodological innovation lies in the real-time fusion of three key perception modules: action recognition using a Spatial-Temporal Graph Convolutional Network (ST-GCN), disassembly tool detection based on an enhanced YOLO algorithm, and facial angle recognition for operator awareness inference. A dedicated dataset for retired power battery disassembly was constructed to support this research, encompassing human skeletal data for action recognition, labeled images for tool detection, and facial expression detection. The proposed system was validated on a physical HRC disassembly platform. Experimental results demonstrate a marked improvement, with our integrated intention recognition method achieving an accuracy of approximately 85 %, significantly outperforming traditional single-modality approaches. Furthermore, the HRC disassembly operation was completed in 238 s, which is 60 s (or 20 %) faster than purely manual disassembly. This work provides a robust and efficient HRC disassembly framework for intelligent disassembly scenario understanding, contributing to advancing circular manufacturing. 

The formulation and selection of parameters and sequences in crankshaft production present challenges that are both demanding and time-intensive. This study introduces an innovative approach to intelligent process planning in crankshaft machining lines using multi-turret machines. Emphasis is placed on automating process planning through multi-objective optimization of critical decisions such as process parameters, operation sequencing, and tool positioning on turret magazines. The principal objectives addressed include minimizing machining and non-machining time, reducing costs by optimizing tool life, and enhancing product quality through optimal surface roughness. By automating these decision points, the proposed framework reduces manual intervention and aligns with Industry 4.0 goals for adaptive, data-driven manufacturing. Additionally, we discuss the potential future incorporation of artificial intelligence agents to dynamically refine parameters and enable adaptive planning.

Laser welding of busbars to battery tabs in electric vehicles (EVs) is crucial due to the rapid advancements in electric mobility technology. Ensuring weld quality is paramount, as it depends on factors such as porosity generation, fluid flow in the molten pool during welding, applied laser power, and welding speed. However, conventional laser welding techniques, which primarily focus on adjusting laser parameters along the weld direction, struggle to effectively mitigate porosity formation. While the effect of laser angles along the weld direction has been extensively studied, the effects of off-axis laser angles, i.e., angled in the plane perpendicular to the weld direction, have not yet been explored. This study introduces an innovative approach to laser welding by varying the laser off-axis angle at different laser energy densities to optimize the process specifically for porosity reduction. By implementing a three-dimensional computational fluid dynamics (CFD) model of laser welding of aluminum AA1050, we provide a detailed analysis of the fluid flow and melt pool dimensions while employing different off-axis angles. Our model incorporates multiple reflections, upward vapor pressure, and recoil pressure to explain porosity formation at different laser off-axis angles. The results show that increasing the laser off-axis angle at optimized laser power and welding speed significantly reduces porosity. The numerical analysis indicates a maximum deviation from the experimental melt pool width of 11% at a laser off-axis angle of 4.92° and a minimum error of 2.6% at an off-axis angle of 2.74°. For melt pool depth, the maximum deviation is 7.2% at an off-axis angle of 4.92°, and the minimum difference is 0.5% at an off-axis angle of 7.42°. This study presents a novel methodology for improving laser welding processes by addressing the specific challenge of porosity formation.

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system. 

For battery pack assemblies, it is crucial that the laser welded cell-to-busbar joints demonstrate both high mechanical strength and minimal electrical resistance. The present study investigates the effect of different laser welding parameters, on the mechanical strength, electrical resistance, porosity formation and joint microstructure, for dissimilar material cell-to-busbar joints. Laser welding experiments are performed, on thin nickel-plated copper and steel plates. The plates are joined in an overlap configuration, using laser beam wobbling and power modulation. Both circular and sinusoidal laser beam wobbling are used as selected strategies to increase the interface width of the joints, where also a comparison is made between the two methods. The joint quality is evaluated using joint geometry analysis, shear strength tests, computed tomography scanning and electrical resistance measurements. The results show that circular laser beam wobbling gives a larger joint shear strength compared with sinusoidal laser beam wobbling. In addition, it is observed that both the total pore volume and material mixing are significantly increased with increasing laser power and wobbling frequency for circular laser beam wobbling. However, for the sinusoidal laser beam wobbling the wobbling frequency does not show a significant impact on the total pore volume.

In response to increasing environmental concerns, many governments have implemented carbon tax policies to incentivize green technology investments. However, the impact of such policies on supply chain coordination, particularly when retailers are risk-averse, remains underexplored. This study investigates a two-tier supply chain where manufacturers invest in green technology and retailers engage in marketing activities within the framework of a carbon tax policy. Motivated by the need to understand how carbon taxes affect strategic decisions and profitability, we analyze the decisions of risk-averse retailers using a mean-variance approach. Our findings indicate that carbon tax policy significantly influences green initiatives and may present challenges to manufacturers and overall supply chain profitability. To address these challenges, we propose both non-contractual and contractual coordination strategies aimed at enhancing the performance of decentralized channels for risk-averse retailers. The comparison of these strategies reveals that the optimal coordination approach is contingent upon the marketing effect and the level of retailer risk aversion. Specifically, a green investment cost-sharing strategy is optimal for maximizing supply chain profit when both retailer risk aversion and the marketing effect are high. Conversely, a marketing effort cost-sharing strategy is more effective in minimizing environmental impact when retailer risk aversion is medium or high and the marketing effect is substantial. This study makes significant contributions by elucidating the interplay between risk aversion, marketing effects, and green technology investments. It provides valuable managerial insights for decision-makers seeking to foster sustainable development in supply chains through the selection of appropriate coordination strategies.

Residual stress (RS) within titanium alloy structural components is the primary factor contributing to machining deformation. It comprises initial residual stress （IRS） and machined surface residual stress (MSRS), resulting from the interplay between IRS and high-level machining-induced residual stress MIRS). Machining deformation of components poses a significant challenge in the aerospace industry，and accurately assessing RS is crucial for precise prediction and control. However, current RS prediction methods struggle to account for various uncertainties in the component manufacturing process，leading to limited prediction accuracy. Furthermore, existing measurement methods can only gauge local RS in samples，which proves inefficient and unreliable for measuring RS fields in large components. Addressing these challenges, this paper introduces a method for simultaneously estimating IRS and MSRS within titanium alloy aircraft components using a Bayesian framework. This approach treats IRS and MSRS as unobservable fields modeled by Gaussian processes. It leverages observable deformation force data to estimate IRS and MSRS while incorporating prior correlations between MSRS fields. In this context，the prior correlation between MSRS fields is represented as a latent Gaussian process with a shared covariance function. The proposed method offers an effective means of estimating the RS field using deformation force data from a probabilistic perspective. It serves as a dependable foundation for optimizing subsequent deformation control strategies. 

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology.