Procedia manufacturing最新文献

The application of high temperature materials is the key to technological advancement in engineering, particularly in the aerospace and automotive industries, where materials are expected to satisfy stringent operating requirements. New heat-resistant, light-weight materials, such as intermetallic gamma titanium aluminides (γ-TiAl) based alloys are attracting attention, and showing a great potential to meet severe operational demands due to their superior properties such as low density, high melting temperature, high specific yield strength, high specific stiffness, and excellent creep resistance. Consequently, γ-TiAl alloys have a great potential for high temperature applications in the aerospace and automotive industries. On the other hand, they are also well known as hard-to-machine materials due to poor ductility at low to intermediate temperatures that result in low fracture toughness and a fast fatigue-crack growth rate. In addition, there is no evidence in the open literature of these materials being subjected to production machining. These disadvantages have hindered their widespread application in industry. In this work, a rhombic turning tool is investigated to explore the machinability of γ-TiAl and to develop a cost-effective environmentally benign machining process. A set of central composite design (CCD) experiments are carried out for optimization of the machining process. The cutting parameters varied are cutting speed, feed rate, and depth of cut. Responses measured included thrust force, feed force, cutting force, specific cutting energy, surface roughness, chip morphology, and surface integrity. From the analysis of experimental data, quadratic models are developed, and 2-D contour and 3-D surface plots are drawn. Results obtained are of significant importance in terms of machinability of γ-TiAl and its application in the manufacturing of diesel engine valves and other tribological engine components subjected to operating temperatures range of 400 ºC to 800 ºC.

Laser surface texturing (LST) has shown immense promise in modifying the physical as well as interactive properties of the surfaces. Understanding the surface microtopography generated during LST provides key knowledge on developing desirable surfaces for different technologies. The work focuses on determining the range of frequency components present in the microtopography when processed at different parameters, such as beam diameter (BD), scanning speed (SS), and beam overlap (BO). Power spectral density (PSD) is utilized to evaluate the intensity of each spatial frequency and fitting models are applied to quantify their contribution to the overall microtopography. The experiments were performed on titanium alloy Ti6Al4V using a continuous watt fiber laser at constant power. The surface microtopography differs when processed under different conditions. The microtopography contains low and high spatial frequency components distributed along both the scan and overlap direction. The PSD analysis reveals the increase in high spatial frequency features when BD and SS are increased. Conversely, the growth of spatial features is observed with an increase in BO reducing the dominance of high spatial frequencies. With the increase in BD, the energy density decreases which reduces the growth of spatial features inducing increased contribution of high spatial frequency content noticeable by upward curvature in the PSD. An increase in SS causes rapid laser motion to affect the microtopography along the scan direction. The increase in BO leads to the enhanced overlapping between successive passes causing remelting and growth of previously formed microtopography and increasing the contribution of low spatial frequency content in the microtopography. The fitting model parameters from ABC at low spatial frequency and Fractal at high frequency provide the quantitative reasoning for the observed trend. Power spectral analysis reveals significant information about the surface microtopography formed during LST and accurate quantification of the PSD may help numerical models to fine-tune the surface features according to the desired functionality.

Additive Manufacturing technologies enable the fabrication of structures with multiscale complexities without increasing their cost. To take advantage of the unique design freedom enabled by additive manufacturing processes, this paper reports a new design method that can generate a fully customized porous shoe sole. The proposed design method contains five major steps and can generate a fully customized shoe sole with customized features on both macro and mesoscale. Specifically, compared to a conventional flat shoe sole, the top surface of a customized sole can fully conform to the bottom surface of patient’s feet, which can significantly reduce the peak plantar pressure. In addition to that, the strut diameters of designed lattice structures can also be customized based on the proposed data-driven design optimization algorithm. By varying the struts’ diameters on the different regions of the designed lattice shoe sole, the peak plantar pressure can be further minimized. The case study provided in this paper shows the proposed method that can significantly improve the performance of customized shoe sole. This promising result indicates that additive manufacturing fabricated lattice shoe soles can be a potential solution to prevent or treat diabetic ulcers.

The automotive industry is developing designs and manufacturing processes for new generations of electric motors intended for use in hybrid and electric vehicles. This paper focuses on the in-process exit hole at the end of the friction stir welding (FSW) path where the tool is withdrawn along the Z-axis and this leaves an impression in the material at the point of extraction. Despite the interest in the FSW technology of solid-state welding to join copper end rings to copper spokes in the fabrication of copper rotors, one of the concerns about the circular tool path is the exit hole produced. Several processes were considered and some of them evaluated for mass production application. The exit hole mitigation was explored using the friction plug welding process for refilling the hole made in copper end rings with consumable tool plugs. The influences of the tool plug’s taper geometry and plunging process parameters for two different method variations were investigated. The microstructure of the plug boundary was difficult to control. The weld characteristics were sufficient for this application but large defects at the interface could result in the separation of the plug at higher speeds due to thermo-mechanical stresses in the end rings during motor operation. Finally, this work puts forth a major welding process for induction copper rotors.

Developments in high degree-of-freedom(DOF) manufacturing processes such as 5-axis machining and additive manufacturing have greatly moderated the design constraint and brought unprecedented manufacturing capability for parts in complex geometry. The advancement in manufacturing processes, at the same time, leads to significant challenges for process planning due to the increasing decision complexity. A method is needed to enable full automated process planning for high DOF manufacturing processes in the foreseeable future. This work focuses on exploring an artificial neural network(ANN) based approach for machining process planning, specifically the toolpath planning for milling operations. The objective of this research is to construct a framework for automated machining process planning that leverages the advancement in ANN methodologies in an attempt to generate an optimized toolpath without any human logic input. In this proposed framework, the voxel model is used as part design and stock geometry representations. An evolving ANN method NeuralEvolution of Augmenting Topologies(NEAT) is applied as the solution algorithm. A prototype implementation of the proposed framework is created and experimented with reasonably simplified machining scenarios and basic part geometries. Initial experiments demonstrate optimistic results supporting the feasibility of creating such an ANN through an evolutionary method to accomplish specific manufacturing requirements on different geometries. The work also revealed that the geometric input is a critical factor for successfully training an ANN model. Further work is needed to encode the part design geometric information as input. Additionally, an improved evolutionary ANN algorithm needs to be created to accelerate the model training.

Industry 4.0 is prominent in the current research agenda, however, measuring the Industry 4.0 readiness and maturity in manufacturing is still challenging. There is no common definition for Industry 4.0 concept, and there are several Industry 4.0 readiness evaluation methods and maturity models in the literature. These models assess the Industry 4.0 maturity using different dimensions, frameworks, or maturity items. The most common dimensions from the literature are strategy, organization, technology, IT, smart factory, smart products, data utilization, and the human factor [1]. This paper provides Company Compass (CCMS) 2.0, a renewed Industry 4.0 conceptual framework and maturity assessment solution aiming to support Industry 4.0 progression. The suggested framework follows a holistic approach in Industry 4.0 maturity assessment by integrating the following dimensions: physical and virtual world, human, strategy and culture, products and services, value chain, and the broader environment. The main contributions of the suggested Industry 4.0 maturity assessment solution are its holistic view, its capability to highlight deficiencies, gaps of enterprises’ Industry 4.0 readiness level, and providing guidelines for setting improvement goals and actions.

The application of the elements of the Fourth Industrial Revolution (4IR) through the development of a Flexible Manufacturing System (FMS) for manufacturing operations in the railcar industry will promote flexibility, intelligent coordination of the manufacturing operations, efficient handling and quality control. Some production systems in the railcar industry are non-responsive to changes in real time thereby leading to reduction in productivity, hence, the need for a FMS that will cater for the dynamics of manufacturing operation. This work proposes a FMS, which encompasses the assembly line, lean production, logistics and quality assurance. The system comprises of the Radio Frequency Identification Technology (RFID) for components identification and process control, arrays of sensors and cameras, automated material storage and supply, standard interfaces such as the interface for the internet of things (IoT), the robotic welding system as well as a robust intelligent control system. A framework for the implementation of the FMS was developed while the simulation of the designed system was performed using the Anylogic 8.2.3. software. Based on the results obtained, there was an inverse relationship between the operating cycle time of the conveyor and the conveyor speed per cycle when the conveyor’s performance was simulated at a speed of 3 m/s and 7 m/s. The results showed that the system can suitably perform the sequence of assembly and quality assurance operations during the manufacturing of railcar subassemblies with minimal interruptions and human intervention. This will promote production of component parts with high structural and dimensional integrity with significant reduction in the manufacturing cycle time and cost.

This study investigates the use of a statistical anomaly detection method to analyse in-situ process monitoring data obtained during the Laser-Powder Bed Fusion of Ti-6Al-4V parts. The printing study was carried out on a Renishaw 500M Laser-Powder Bed Fusion system. A photodiode-based system called InfiniAM was used to monitor the melt-pool emissions along with the operational behaviour of the laser during the build process. The analysis of the in-process data was carried out using an unsupervised machine learning approach called the Search and TRace AnomalY algorithm. The ability to detect defects during the manufacturing of metal alloy parts was demonstrated.

Tool wear prediction is of significance in advanced manufacturing industries, as it aims to ensure the quality of parts, improve machining efficiency and reduce machining costs. Existing tool wear monitoring and prediction methods mainly adopt neural network model with fixed architecture, which rely on the researchers’ experience and cannot guarantee accuracy under different cutting conditions. This paper proposes a tool wear prediction method based on network architecture search. which can learn a suitable network structure under different cutting conditions. Experiments shows sufficient improvement in the accuracy of predicting tool wear compared with existing methods.