Cirp Annals-Manufacturing Technology最新文献

This study addresses the critical need for intelligent process monitoring in unmanned manufacturing through real-time fault detection. The proposed hybrid approach, which is focused on overcoming the limitations of existing methods, utilizes machine learning (ML) for precise parameter identification in real-time to detect deviations. The ML system is developed using extensive data obtained from simulations based on enhanced force models also achieved through ML. Demonstrating over 96 % accuracy in real-time predictions, the method proves applicable for diverse unmanned manufacturing applications, including monitoring and process optimization, emphasizing its adaptability for industrial implementation using CNC controller signals.

The fraction of materials that does not reach the target product in discrete manufacturing is very significant. Directing secondary material streams towards other manufacturing processes, possibly after intermediate pre-processing, offers opportunities for more energy and resource efficient recycling routes. This paper contributes to the exploration of such symbiotic recycling strategies by identifying relevant process mechanisms and by systematically scanning the manufacturing domain for relevant process combinations. Emerging and proven symbiotic combinations within the discrete manufacturing domain are reviewed, with proper attention for the quantification of the thus avoided environmental impact. A series of representative case studies illustrates the feasibility of applying industrial symbiosis principles in a discrete manufacturing context, with uniform data sheets providing detailed information on relevant research achievements.

This study utilizes Additive Manufacturing (AM), as a key enabler, in creating Polyethylene (PE)-based composites from industrial waste. The benefits of this study are: firstly, promoting environmental sustainability by successfully fabricating composites by repurposing industrial waste and increasing the capacity of PE recycling through AM; and secondly, developing high-value PE-based composites with enhanced structural, mechanical and rheological properties. Detailed printability assessment of various blend ratios of waste PE, Polystyrene, and fiber reinforced resin are discussed and successful printing of PE-based composites with tangible improvements in material properties is demonstrated. The agility of the proposed approach is also highlighted.

The wire DED-LB/M AM process is fast, cost-effective and it creates high-quality, dense parts. However, its industrial adoption is limited since process stability needs complex monitoring devices and tools. This work proposes a twofold strategy to resolve this issue; A fast-running, physics-based simulation tool calculates the temperature field during the building of single walls, while, an affordable vision-based monitoring system captures the melt pool dimensions that are correlated to the temperature and reveal heat accumulation. Therefore, real-time process stability control is enabled. An infrared thermal camera is used to validate the model and calibrate the monitoring system.

Current methods for modeling hybrid additive manufacturing are computationally inefficient for use in optimization algorithms. An analytical tool is needed to understand how cycling thermal and mechanical loads via 3D printing and cold working reshapes cumulative residual stress within a build volume. A novel analytical model was developed that couples beam theory and superposition to rapidly predict cumulative residual stress. Modeling results were experimentally validated on AlSi10Mg after laser shock peening prescribed layers during powder bed fusion. Results demonstrated a vertically translating heat-affected zone, and the use of beam-based superposition accurately accounted for residual stress redistribution from cyclic printing and peening.

This paper describes the design and testing for a low-cost, table-mounted drilling torque and thrust force dynamometer. A flexure-based (constrained-motion) design is detailed, where the rotation for torque and translation for thrust force are measured using a dual magnet-Hall effect sensor configuration that provides a linear voltage output. Two sensors are implemented for each direction to reject undesired structural dynamics. Validation experiments for torque and thrust force are reported using a commercially available, spindle-mounted rotating dynamometer. Results are provided for blind hole drilling in aluminum and stainless steel samples using two drill diameters and various feed rates.

Planning production capacities in multi-product production networks is challenging due to the multitude of decision factors, inter-organisational interests, and a high degree of uncertainty. Particularly, the organisational separation of different products that share sites induces planning complexity. This paper proposes an interactive-two model concept integrating product-specific network planning and a site capacity planning perspective. Stochastic mixed integer linear programming determines order allocations, line investments, and personnel plans. The potential to swiftly adapt plans while obeying local constraints is demonstrated with a large automotive supplier. The approach should allow quicker and more adaptive planning, leading to more resilient organisations.

A self-configuring real-time tool condition monitoring (TCM) system for milling applications using vibration signals is introduced. A suite of signal processing and machine learning algorithms was developed to define a generalized correlation between distortion-resistant features of usable and worn tools. Using only a few seconds of learning data acquired at the early stage of tool life, the system synthesizes worn tool features in-process to define the decision-making boundaries, independent of the utilized cutting parameters, machines, and sensors. It provides high detection accuracy and reduces the lead time and cost needed for system development and calibration, introducing the plug-and-play concept to TCM.

Frog-legged robots are commonly used for silicon wafer handling in semiconductor manufacturing. However, their precision, speed and versatility are limited by vibration which varies with their position in the workspace. This paper proposes a methodology for modelling the pose-dependent vibration of a frog-legged robot as a function of its changing inertia, and its experimentally-identified joint stiffness and damping. The model is used to design a feedforward tracking controller for compensating the pose-dependent vibration of the robot. In experiments, the proposed method yields 65–73% reduction in RMS tracking error compared to a baseline controller designed for specific poses of the robot.

The transition from conventional standalone metrology to integrated metrology has been accelerating in advanced manufacturing over the past decade. This keynote paper defines the concept of integrated metrology, which extends beyond parts inspection and encompasses processes and manufacturing equipment to enhance efficiency and productivity. The paper presents the characteristics, benefits, constraints, and future possibilities of integrated metrology for parts, processes, and equipment. It also includes a classification of the physical quantities of measurands, the corresponding measuring instruments, data and communication methods, uncertainty, and traceability. The paper also discusses future challenges and emerging trends.