The selected laser melting production and subsequent post-processing of Ti-6Al-4V prosthetic acetabular

&nbsp;Processing and post processing of human prosthetic acetabular cup by using 3D printing. The results showed using 3D printers leads to fabrication customized implants with higher quality.<br /

Micromachining

To present their work in the field of micromachining, researchers from distant parts of the world have joined their efforts and contributed their ideas according to their interest and engagement. Their articles will give you the opportunity to understand the concepts of micromachining of advanced materials. Surface texturing using pico- and femto-second laser micromachining is presented, as well as the silicon-based micromachining process for flexible electronics. You can learn about the CMOS compatible wet bulk micromachining process for MEMS applications and the physical process and plasma parameters in a radio frequency hybrid plasma system for thin-film production with ion assistance. Last but not least, study on the specific coefficient in the micromachining process and multiscale simulation of influence of surface defects on nanoindentation using quasi-continuum method provides us with an insight in modelling and the simulation of micromachining processes. The editors hope that this book will allow both professionals and readers not involved in the immediate field to understand and enjoy the topic

Cold Micro Metal Forming

This open access book contains the research report of the Collaborative Research Center “Micro Cold Forming” (SFB 747) of the University of Bremen, Germany. The topical research focus lies on new methods and processes for a mastered mass production of micro parts which are smaller than 1mm (by forming in batch size higher than one million). The target audience primarily comprises research experts and practitioners in production engineering, but the book may also be of interest to graduate students alike

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance

Machinability of Ti6Al4V alloy produced by electron beam melting under different lubricating conditions

In the last decade, the growing diffusion of metal additive manufacturing technologies is revolutionising the manufacturing processes of the most advanced industrial fields. Nowadays, more and more companies operating in the aeronautic and in the biomedical field are employing the additive manufacturing technology of Electron Beam Melting (EBM) to produce prosthesis and aero engine parts made of the titanium alloy Ti6Al4V, traditionally produced by hot forging and machining. Thanks to this technology, it is possible to realise a complex shape component with tailored mechanical and geometrical properties, passing from the 3D CAD model directly to the near net shape geometry without any intermediate manufacturing steps, thus cutting the production costs. However, finishing machining operations are still necessary to remove the surface porosity that is a direct and inevitable consequence of additive manufacturing technologies, and to achieve higher surface quality and geometrical accuracy. Aiming to optimize the machining operation and to avoid detrimental surface damages left on the final product, the material machinability has to be taken into account. At the moment, many efforts coming from both the academic and industrial research have been spent to enhance the poor machinability of wrought Ti6Al4V alloy due to the increasing demand from the aeronautic field; however no published works and technical data are available regarding the machinability of EBM Ti6Al4V that presents different mechanical properties. Within the biomedical field, the surgical replacements made of Ti6Al4V are traditionally machined under flood coolants, made of synthetic or vegetable oil and water emulsions. As a consequence, costly sterilizing and cleaning operations are performed to remove the toxic and pollutant chemical residuals left on the finished products at the end of the manufacturing process. Thus, there is a need to revise the traditional lubricating strategies applied in machining operations of surgical implants, proposing an innovative solution that might satisfy technological, environmental and economic issues. In this PhD thesis, an innovative cryogenic cooling line has been developed and implemented to turn EBM Ti6Al4V alloy, as a promising alternative to standard cooling methods applied in machining surgical implants. The alloy machinability has been firstly investigated trough an experimental approach, evaluating the effects of three different cooling methods namely: dry, wet and cryogenic and of different cutting parameters, on the tool wear, on the surface integrity and on the chip morphology. Subsequently, a FE numerical model has been developed to simulate the turning operation of EBM Ti6Al4V alloy, capable to predict the effects of different process conditions. Due to the beneficial effects induced by the cryogenic cooling on the surface integrity of turned Ti6AL4V EBM test pieces, the feasibility of such technology for biomedical applications has been validated by means of wear tests: the wear resistance of cryogenically machined specimens clearly increased with a strong reduction of metallic particles loss. Finally, cryogenic turning has been employed to machine real acetabular cups, in comparison with standard cooling methods applied in machining surgical implants. The beneficial effects imparted by cryogenic cooling in terms of improved material machinability, improved wear resistance and satisfying achievable geometrical accuracy, foresee a potential applicability of this technology in the biomedical field for years to come

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Advances in Micro and Nano Manufacturing: Process Modeling and Applications

Micro- and nanomanufacturing technologies have been researched and developed in the industrial environment with the goal of supporting product miniaturization and the integration of new functionalities. The technological development of new materials and processing methods needs to be supported by predictive models which can simulate the interactions between materials, process states, and product properties. In comparison with the conventional manufacturing scale, micro- and nanoscale technologies require the study of different mechanical, thermal, and fluid dynamics, phenomena which need to be assessed and modeled.This Special Issue is dedicated to advances in the modeling of micro- and nanomanufacturing processes. The development of new models, validation of state-of-the-art modeling strategies, and approaches to material model calibration are presented. The goal is to provide state-of-the-art examples of the use of modeling and simulation in micro- and nanomanufacturing processes, promoting the diffusion and development of these technologies

Machining strategies for distortion control during high speed machining

Airframe structural components that are machined from aluminium forgings or plate stock represent a significant contribution to the cost of both military and commercial aircraft. These components tend to distort due to heat treating induced bulk stresses and machining. Correcting these distortions increase costs and manufacturing lead times, especially for a high-volume, high-quality production company. In addition to this, variation in the residual stress profile from component to component is common due to variation in the condition of supply state. There is therefore a need to understand and model the effects of heat-treating and machining strategies on distortion and to predict, minimize, and control these distortions. This thesis addresses the modeling, data acquisition, and validation of residual stress and distortion models using different aluminium test cases. The project is divided into different technical studies to build the modelling capability: In the first study, aluminium 7050 material data and heat transfer coefficients were experimentally acquired. This data was to be used as an input to demonstrate the capability of Finite Element (FE) modelling as the main tool to predict and design robust strategies in the presence of residual stress variation due to processing or geometric differences. In the second study, the simulation study was performed to improve the machining distortion by using finite element (FE) modelling on varying residual stress profiles of aluminium coupons. Other studies included the influence of tool paths, the pocketing sequence, billet orientation and part location on machining distortion. Finally, utilizing the knowledge acquired, a machining process strategy for distortion control was proposed

Rapid Fabrication Techniques for Anatomically-Shaped Calcium Polyphosphate Substrates for Implants to Repair Osteochondral Focal Defects

The purpose of the present study is to develop techniques for manufacturing anatomically-shaped substrates of implants made from calcium polyphosphate (CPP) ceramic. These substrates have tissue-engineered cartilage growing on their top surfaces and can be used as implants for osteochondral focal defect repair. While many research groups have been fabricating such substrates using standard material shapes, e.g., rectangles and circular discs, it is considered beneficial to develop methods that can be integrated in the substrate fabrication process to produce an implant that is specific to a patient’s own anatomy (as obtained from computer tomography data) to avoid uneven and/or elevated stress distribution that can affect the survival of cartilage. The custom-made, porous CPP substrates were fabricated with three-dimensional printing (3DP) and computer numerically controlled (CNC) machining for the first time to the best of the author’s knowledge. The 3DP technique was employed in two routines: indirect- and direct-3DP. In the former, 3DP was used to fabricate molds for pre-shaping of the CPP substrates from two different powder size ranges (<75 μm and 106-150 μm). In the latter, CPP substrates were produced directly from the retrofitted 3DP apparatus in a layer-by-layer fashion from 45-75 μm CPP powder with a polymeric binder. The prototyped samples were then sintered to obtain the required porosity and mechanical properties. These substrates were characterized in terms of their dimensional shrinkage and density. Also, SEM images were used to assess the particle distribution and neck and bond formations. The substrates produced using the indirect-3DP method yielded densities (<75 μm: 66.28 ± 11.62% and 106-150 μm: 65.87 ± 6.12%), which were comparable to the substrates used currently and with some success in animal studies. Geometric adjustment factors were devised to compensate for the slight expansion inherent in the 3DP mold fabricating process. These equations were used to bring the plaster molds into true dimension. The direct-3DP method has proven to be the ultimate choice due to its ability to produce complex anatomically-shaped substrates without the use of a chemical solvent. In addition, it allows for precise control of both pore size and internal architectures of the substrates. Thus, the direct-3DP was considered to be superior than the indirect-3DP as a fabrication method. In the alternative CNC machining approach to fabrication, the ability to machine the CPP ceramic was feasible and by careful selection of the machining conditions, anatomically-shaped CPP substrates were produced. To develop strategies for optimizing the machining process, a mechanistic model was developed based on curve fitting the average cutting forces to determine the cutting coefficients for CPP. These cutting coefficients were functions of workpiece material, axial depth of cut, chip width, and cutter geometry. To explore the utility of this modelling approach, cutting forces were predicted for a helical ball-end mill and compared with experimental results. The cutting force simulation exhibits good agreement in predicting the fundamental force magnitude and general shape of the actual forces. However, there were some discrepancies between the predicted and measured forces. These differences were attributed to internal microstructure defects, density gradients, and the use of a shear plane model in force prediction that was not entirely appropriate for brittle materials such as CPP. The present study successfully developed 3DP and CNC fabrication methods for manufacturing anatomically-shaped CPP substrates. Future studies were recommended to explore further optimization of these fabrication methods and to demonstrate the utility of accurate substrates shapes to the clinical application of focal defect repair implants