Procedia manufacturing最新文献

This paper examines a novel way of training Lean Manufacturing Systems and Tools utilising an Industry 4.0 methodology during the SARS-COVID2 Pandemic of 2020. Currently, it is challenging for the Integrated Production Systems Team, responsible for carrying out training on the Lean principles, to undertake the training safely and without the risk of possible disease transmission. This is due to the usual close quarters training carried out in the Engine Manufacturing Centre. Schools, Colleges and Universities have adapted and utilised technology and moved to an Industry 4.0 digitalised approach to learning and development. This is therefore an opportunity for manufacturing to follow suit and create digitised solutions to training and development opportunities, to ensure that the employees within the manufacturing facility have adequate knowledge on the Lean principles.

This paper describes the propagation of uncertainty in the parameters for a 6061-T6 aluminum Johnson-Cook flow stress model to, first, the uncertainty in the corresponding mechanistic cutting force model obtained by orthogonal cutting finite element simulation and, second, the milling force predicted by time domain simulation using the force model. The approach includes five key elements: 1) a literature review to identify the means and standard deviations for the 6061-T6 aluminum Johnson-Cook model parameters; 2) structured light scanning to measure an endmill’s cutting edge macro-geometry along the tool axis; 3) structured light scanning to identify the cutting edge cross-sectional rake and relief profiles for the same endmill; 4) orthogonal cutting finite element analysis to determine the mechanistic force model coefficients that relate the force components to chip area and width using the tool’s rake and relief profiles and random samples from the Johnson-Cook parameter distributions; and 5) time domain simulation with inputs that include the measured cutting edge macro-geometry, uncertain finite element-based force model, and measured structural dynamics. Distributions for milling force predictions are determined by Monte Carlo simulation and compared to in-process measurements for an indexable endmill-collet holder to demonstrate the approach. It is observed that 95% confidence intervals on the predicted forces bound the measured time-dependent force profile peaks in over half of the cases tested. It is also seen that the Johnson-Cook flow stress model-based force predictions performed as well as predictions based on a calibrated mechanistic force model.

The tool condition used in machining process is generally determined by the wear status, which is an important factor of resulting in the machining efficiency of the manufacturing processes. This paper presents a method to estimate the tool condition of grinding wheel using an image sensor with machine learning techniques. Statistical features, mean, standard deviation, and entropy were selected from the simplified wear model. The selected features were extracted from the wheel surface images of an electroplated cubic Boron Nitride (cBN) wheel on a commercial CNC machine, and compared to the tool condition represented by the number of grinding passes. Finally, the statistical features were fused by machine learning techniques to estimate the wear condition of the grinding wheel. Results show a sufficient correspondence between the estimated and true tool condition with 0.9590 of the coefficient of determination (R²) based on support vector regression model.

As one of the Additive Manufacturing (AM) technologies, Directed Energy Deposition (DED) inherited common concerns from the AM concept, such as the layer discretization process, which brings about the staircase effect, and the complex thermal reheating cycles that occur due to the scanning strategies. In this regard, diversification in slicing strategies and the application of post-processing techniques arises as a feasible way to reduce defects and the usual poor surface finishing of DED parts. This study combines the non-planar slicing strategy as toolpath for laser polishing (LP) in DED parts, evaluating the influence of such post-processing for surface finishing, geometry and hardness of the Inconel 625 truncated pyramid built by DED. This geometry was built by using planar slicing and zigzag scanning strategy in BeAM M250 DED equipment. The non-planar coordinates of the LP were applied at an offset in the external profile of the pyramid with support of an open-source software Slic3R. The workpieces were built with laser power of 500 W delivered at the 800 μm spot diameter with scanning rate at 2000 mm/min and powder flow of 7.5 g/min. Laser polishing was performed considering three levels of energy density (9.38, 11.25 and 14.06 J/mm²) and the number of iterations (once, 3 and 5 times). Both, deposition and LP, were performed with local Argon gas shielding. Among the results, LP has shown to be a feasible post-processing technique to L-DED parts, which can improve surface finishing and shape, maintaining hardness. The energy spent LP causes the remelting of satellite particles and shown to be not enough to rearrange the microstructure. The non-planar slicing applied, as toolpath to the LP, comprises a new possibility allowing DED parts to have better results towards surface finishing with a clean and quick method of post-processing. This has shown to be beneficial to the efficiency of the post-processing stage by leading to a greater assertiveness of the process, less post-processing time and no waste of material.

Surface topography and surface finish are two significant factors for evaluating the quality and dimensional accuracy of additive manufacturing (AM) parts. In general, compared with traditional subtraction and forming manufacturing techniques, the nature of the rough surface and the geometric complexity make the surface of AM parts "another surface," and traditional methods such as coordinate machine measurement may not be applicable. Most research on surface extraction focuses on regular surfaces, such as flat, cylindrical, or spherical surfaces, but less has been done with irregular surfaces. This paper presented an approach for extracting irregular surfaces based on micro-computed tomography (μ-CT) data of AM parts. The extracted data sets were then compared with data sets obtained by a structured light system (SLS). Areal surface texture parameters, cloud comparison methods, and statistical methods were applied to evaluate the difference between the surface data obtained by the two systems. The results showed that the two surface data correlated well and confirmed the capability of the proposed method for surface extraction from irregular surfaces. This work will contribute to the further multi-sensor data fusion for metrology and provide valuable information for near-surface defects prediction with real-time in-situ metrology method in additive manufacturing.

Effective and safe human-robot collaboration (HRC) in assembly requires a reasonable proximity between humans and robots. This paper considers an assembly scenario that humans and robots collaborate in a shared workspace, where safety is the most important issue. A dynamic safety field is defined for HRC to ensure safe collaboration and interaction between a human operator and a robot to achieve assembly tasks. The safety field can be adjusted and calibrated based on predicted assembly tasks. Utilizing the dynamically calibrated safety field, the robot movement will be obtained. The dynamic calibration of safety field for robot movement presents an alternative easier way for robot motion planning based on safety sense. Case studies are performed to demonstrate the effectiveness of the proposed dynamic safety field motion planning method.