Adaptive slicing based on efficient profile analysis

Adaptive slicing is an important computational task required in the layer-based manufacturing process. Its purpose is to find an optimal trade-off between the fabrication time (number of layers) and the surface quality (geometric deviation error). Most of the traditional adaptive slicing algorithms are computationally expensive or only based on local evaluation of errors. To tackle these problems, we introduce a method to efficiently generate slicing plans by a new metric profile that can characterize the distribution of deviation errors along the building direction. By generalizing the conventional error metrics, the proposed metric profile is a density function of deviation errors, which measures the global deviation errors rather than the in-plane local geometry errors used in most prior methods. Slicing can be efficiently evaluated based on metric profiles in contrast to the expensive computation on models in boundary-representation. An efficient algorithm based on dynamic programming is proposed to find the best slicing plan. Our adaptive slicing method can also be applied to models with weighted features and can serve as the inner loop to search the best building direction. The performance of our approach is demonstrated by experimental tests on different examples

Characterization of surface and bulk features of SLM parts

An experiment-analytical procedure based on the building of an object in severe atmosphere resistant steel by SLM is proposed. The complex shape was investigated with the sectioning and laboratory observation of the physical object. The study evidenced the need to get a variable layer thickness to follow double curvature complex shapes. In particular the key variable in the process is the melt bath dimension by which the metal powder assumes by solidification the required global geometry. It was observed that the bath detected mainly in terms of the area of section tends to decrease when approaching to the surface of the physical model where the complex geometry needs to be described. Relationships describing the bath area behaviour and correlations between surface roughness and internal bath dimensions were found and proposed in detail. The surface roughness is highly correlated with the bath area in the zones of the section approaching the surface

Non-Uniform Planar Slicing for Robot-Based Additive Manufacturing

Planar slicing algorithms with constant layer thickness are widely implemented for geometry processing in Additive Manufacturing (AM). Since the build direction is fixed, a staircase effect is produced, decreasing the final surface finish. Also, support structures are required for overhanging portions. To overcome such limits, AM is combined with manipulators and working tables with multiple degrees of freedom. This is called Robot-Based Additive Manufacturing (RBAM) and it aims to increase the manufacturing flexibility of traditional printers, enabling the deposition of material in multiple directions. In particular, the deposition direction is changed at each layer requiring non-uniform thickness slicing. The total number of layers, as well as the volume of the support structures and the manufacturing time are reduced, while the surface finish and mechanical performance of the final product are increased. This paper presents an algorithm for non-uniform planar slicing developed in Rhinoceros and Grasshopper. It processes the input geometry and uses parameters to capture manufacturing limits. It mostly targets curved geometries to remove the need for support structures, also increasing the part quality

Investigation into adaptive slicing methodologies for additive manufacturing

Adaptive slicing is a methodology used to optimise the trade-off between build-time reduction and geometric accuracy improvement in additive manufacturing (AM). It works by varying decreasing layer thickness in sections of high curvature. However, current adaptive slicing methodologies all face the difficulty of adjusting layer thickness precisely according to the variations of the model’s geometry, thereby limiting the geometric accuracy improvement. This thesis tackles this difficulty by indicating the geometric variations of the model by evaluating the ratio of the volume of each sliced layer’s geometric deviation to the volume of its corresponding region in the digital model. This indication is accomplished because all the topological information of the corresponding region is considered in assessing the geometric deviation (volume) between each sliced layer and its corresponding region. Through having this precise indication to modify each layer thickness, this thesis aims to develop an adaptive slicing that can mitigate geometric inaccuracies (e.g. staircase effect and dimensional deviation) while balancing the build time. This slicing is evaluated using six different test models, compared with three current slicing methodologies (voxelisation-based, cusp height-based, and uniform slicing), and validated through computation and manufacturing. These validations all demonstrate that volume deviation-based slicing optimises the trade-off between build-time reduction and geometric accuracy improvement better than the other existing slicing methodologies. For example, it can reduce the build time by nearly half compared to other existing slicing methodologies assuming a similar degree of printed parts’ geometric accuracy. The improved trade-off optimised by volume deviation-based slicing can directly benefit the AM applications in the aerospace and medical industries. This is because current research has shown geometric inaccuracies are the primary cause of reducing energy efficiency (e.g. turbine blade and wind tunnel testing models) and having failed implants (e.g. hip and cranial implants, dental prostheses). In addition to improving the geometric accuracy of AM-constructed parts, volume deviation-based slicing may also be incorporated with non-planar layer slicing. Non-planar layer slicing is designed to mitigate the mechanical anisotropy of printed parts by using curved-sliced layers. By integrating volume deviation-based slicing with non-planar layer slicing, the thickness of each curved-sliced layer can be adjusted according to the model’s geometric variations and, therefore, has a possibility of reducing the geometric inaccuracies and mechanical anisotropy simultaneously.Open Acces

Geometric Manufacturability Analysis for Additive Manufacturing

During the development of a new product, it is difficult for designers to predict how their design decisions will impact manufacturability and manufacturing cost of the individual parts in their product. Additive manufacturing is increasingly becoming a viable option to produce high fidelity prototypes and even small-scale production part runs. However, as an emerging technology, there are few resources available to help designers make design decisions regarding quality and manufacturability for additive manufacturing. Most information developed to help designers ensure manufacturability is in the form of general guidelines that designers must interpret and then use their best judgment to scrutinize their design. Designers can only guess, based on previous experience, if the process can produce part features that meet their specified geometric tolerances. However, by using algorithms to analyze part geometry, it is possible to predict additive manufacturing outcomes. This thesis describes the development of two software tools to analyze part geometry in near real-time: one that predicts manufacturability, and another that predicts achievable quality. These tools are used to explore how automated part geometry analysis influences the effectiveness of design for additive manufacturing feedback. The research hypothesis of this thesis is that part geometry analysis improves the practicality, accuracy, and usefulness of design for additive manufacturing feedback. To test this hypothesis, three research thrusts were conducted: evaluating the performance of the newly developed tools relative to existing tools, experimental verification of the predictions of the tools, and a user study evaluating usage of the manufacturability tool during a design task. Comparison with existing tools indicated that both tools described in this thesis have similar computation time as existing solutions, while providing greater potential to allow designers to analyze manufacturing trade-offs, with a more comprehensive approach to modeling sources of errors in the manufacturing process. A range of parts were printed using fused deposition modeling and then inspected. The experimental results showed that the predictions of both tools were relatively accurate, and highlighted several additional process parameters that can be included in the modeling approach to improve accuracy. Lastly, a user study demonstrated that use of the software tool reduced the number of manufacturability problems in participants' designs while requiring a similar amount of time to use, compared with using a list of design heuristics. The findings of the thesis support the practicality, accuracy, and usefulness of geometry analysis software tools to support design for additive manufacturing