Multi Objective Optimisation of Build Orientation for Rapid Prototyping with Fused Deposition Modeling (FDM)

The ability to select the optimal orientation of build up is one of the critical factors since it affects the part surface quality, accuracy, build time and part cost. Various factors to be considered in optimisation of build orientation for FDM are build material, support material, build up time, surface roughness and total cost. Experiments were carried out and results are analysed for varying build orientation for primitive geometries like cylinder. An appropriate weighting factor has been considered for various objective functions depending on the specific requirement of the part while carrying out multi-objective optimisation. These analyses will help process engineers to decide proper build orientation.Mechanical Engineerin

AM Feature and Knowledge Based Process Planning for Additive Manufacturing in Multiple Parts Production Context

Additive Manufacturing (AM) has played an important role in manufacturing, especially in customized production. It is an ideal 'Concurrent Manufacturing' which enables fabricating a group of same or even different multiple parts simultaneously within one build volume due to its unique layer by layer processing way. However, there is very few available methods or tools for users, e.g. the AM manufacturing service bureaus, to optimize the process and production plan in multiple parts production context. To deal with this problem, this paper introduces an AM feature and knowledge based systematic process planning strategy. The main contents and key issues of process planning for AM in multiple parts production context are analyzed. Then, a developing CAPP system based on a systematic process planning framework for AM in this multiple parts production context is presented. Finally, some test examples are applied to demonstrate the functions and effectiveness of some key modules of the developing system.Mechanical Engineerin

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

Choosing the Best Direction of Printing for Additive Manufacturing Process in Medical Applications Using a New Geometric Complexity Model Based on Part CAD Data

Additive manufacturing processes is now experiencing significant growth and is at the origin of intense research activity (optimization of topology, biomedical applications, etc.). One of the characteristics of this method is that the geometric complexity is free. The complexity of a CAD model is also a field of research. The basic idea is that the complexity of a component has implications in design and especially in manufacturing. Indeed, industrial competitiveness in the mechanical field generated the need to produce increasingly complex systems and parts (in terms of geometry, topology ...). Part deposition orientation is also very important factor of additive manufacturing as it effects build time, support structure, dimensional accuracy, surface finish and cost of the part. A number of layered manufacturing process specific parameters and constraints have to be considered while deciding the part deposition orientation. Determination of an optimal part deposition orientation is a difficult and time consuming task as one has to trade-off among various contradicting objectives like part surface finish and build time. This paper describes and compares various attempts made to determine part deposition orientation of orthoses using geometric complexity model and part CAD information. (c) Springer Nature Switzerland AG 2019

Improving additive manufacturing performance by build orientation optimization

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used in several areas by engineers and designers. Build orientation is a critical issue in AM since it is associated with the part accuracy, the number of supports required and the processing time to produce the object. This paper presents an optimization approach to solve the part build orientation problem taking into account some characteristics or measures that can affect the accuracy of the part, namely the volumetric error, the support area, the staircase effect, the build time, the surface roughness and the surface quality. A global optimization method, the Electromagnetism-like algorithm, is used to solve the part build orientation problem.The authors are grateful to the anonymous referees for their fruitfulcomments and suggestions. This work has been supported and developed under the FIBR3Dproject - Hybrid processes based on additive manufacturing of composites with long or shortfibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by theLisbon Regional Operational Programme 2020, under the PORTUGAL 2020 PartnershipAgreement, through the European Regional Development Fund (ERDF). This work hasbeen also supported by national funds through FCT - Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the Project Scope: UID/CEC/00319/201

Synergic use of two-dimensional materials to tailor interfaces in large area perovskite modules

In the field of halide perovskite solar cells (PSCs), interface engineering has been conceptualized and exploited as a powerful mean to improve solar cell performance, stability, and scalability. In this regard, here we propose the use of a multi two-dimensional (2D) materials as intra and inter layers in a mesoscopic PSCs. By combining graphene into both compact and mesoporous TiO2, Ti3C2Tx MXenes into the perovskite absorbing layer and functionalized-MoS2 at the interface between perovskite and the hole transporting layer, we boost the efficiency of PSCs (i.e., +10%) compared to the 2D materials-free PSCs. The optimized 2D materials-based structure has been successfully extended from lab-scale cell dimensions to large area module on 121 cm2 substrates (11 x11 cm2) till to 210 cm2 substrates (14.5 x14.5 cm2) with active area efficiency of 17.2% and 14.7%, respectively. The remarkable results are supported by a systematic statistical analysis, testifying the effectiveness of 2D materials interface engineering also on large area devices, extending the 2D materials-perovskite photovoltaic technology to the industrial exploitation

Trapped-modes, slow light and collective resonances in metamaterials

A new class of metamaterials exhibiting coherent, collective response has been introduced. It is shown that the sharp resonant behaviour of coherent metamaterials can only be observed in arrays of metamaterial elements and is absent from the response of a single isolated unit cell. As a result, such arrays are extremely sensitive to positional disorder and resonances degrade rapidly with increasing randomization. These observed strong inter-element interactions render coherent metamaterials ideal candidates for gain-assisted functionalities as demonstrated by the suggestion and numerical study of a novel amplifying/lasing device, termed the 'lasing spaser'. An antipode class of incoherent metamaterials is also presented, where the resonant response of a single unit and of an infinite array are very similar resulting in weak dependence on disorder. The first metamaterial analogue of electromagnetically induced transparency is demonstrated experimentally and theoretically in essentially planar structures. The phenomenon arises from destructive interference of fields radiated by strongly coupled metamaterial elements that support anti-symmetric weakly-radiating current configurations, termed trapped-modes. This behaviour is accompanied by sharp resonances and steep normal dispersion which leads to long pulse delays. It is shown that cascading of metamaterial slabs increases the bandwidth of the pulse delay effect, while extension to all-angles and all-polarizations is demonstrated by appealing to incoherent metamaterials. The first experimental study of metamaterials with toroidal symmetry is reported. Resonant circular dichroism is observed in a metamaterial consisting of toroidal wire windings. Further numerical investigation attributes the gyrotropic behaviour to current standing waves corresponding to the eigenmodes of the unit cell winding. Multipole expansion of the resonant current configurations indicates a dominant electric dipole-magnetic dipole contribution to gyrotropy followed by electric dipole-electric quadrupole order effects, while a non-negligible toroidal response comparable to electric quadrupole in scattering efficiency also emerges. Finally, collective effects are studied in quasicrystal hole arrays and it is demonstrated that non-resonant scatterers can lead to strong lattice resonances and extraordinary transmission even in the case of quasi-periodicity. Microwave and optical quasicrystal patterns exhibit similar response exceeding predictions based on absence of inter-element interactions and even reaching a nearly invisible state in the microwave part of the spectrum

Composite structural materials

The purpose of the RPI composites program is to develop advanced technology in the areas of physical properties, structural concepts and analysis, manufacturing, reliability and life prediction. Concommitant goals are to educate engineers to design and use composite materials as normal or conventional materials. A multifaceted program was instituted to achieve these objectives

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and collision avoidance, our method employs the score-based diffusion model to learn a series of gradient fields. These gradient fields encode the correlations between constraint satisfaction and the spatial relationships of polygons, learned from teacher examples. During the testing phase, packing solutions are generated using a coarse-to-fine refinement mechanism guided by the learned gradient fields. To enhance packing feasibility and optimality, we introduce two key architectural designs: multi-scale feature extraction and coarse-to-fine relation extraction. We conduct experiments on two typical industrial packing domains, considering translations only. Empirically, our approach demonstrates spatial utilization rates comparable to, or even surpassing, those achieved by the teacher algorithm responsible for training data generation. Additionally, it exhibits some level of generalization to shape variations. We are hopeful that this method could pave the way for new possibilities in solving the packing problem