Additive Manufacturing Optimization Research Articles

Additive manufacturing best practices and its impact on supply chain performance through structural equation modelling: A case study in Ethiopian footwear industry

The aim of this study is to analyze the effects of additive manufacturing (AM) best practices on supply chain (SC) performance. The study developed conceptual framework with four AM best practice as independent variables and five SC performance measuring factors as dependent variables. To test the developed model and hypothesis, data’s has been collected from 129 respondents from 29 footwear industries in Ethiopia. AMOS v.23 is used to test model fit with confirmatory factor analysis (CFA) (χ2/df, RMSEA, CFI, GFI, RMR, Tucker-Lewis index (TLI)). Reliability analysis was carried out to test the reliability and stability of the research questionnaires. Correlation analysis was performed to test the strength and association between dependent and independent variables. And to test the developed hypothesis structural equation modelling (SEM) or paths analysis was used. The findings of the CFA result indicated that data fit the model reasonably well. The finds of correlation analysis indicated that, AM best practices have positive, strong and significant relationship with cost, time and reliability related factors of SC performance. But it has very weak and insignificant relationship with customer related factors. In addition, the findings revelled that AM best practices have negative and insignificant relationship with supplier related factors. On the other hands, the findings of SEM confirmed that the identified four AM best practices positively and significantly improve SC performance. And this study concludes that manufacturing flexibility, material utilization, environmental control and product design optimization of AM plays a vital role in improving SC performance.

Feature selection and framework design toward data-driven predictive sustainability assessment and optimization for additive manufacturing

Additive manufacturing (AM) is considered an innovative technology to fabricate goods with green characteristics. In comparison to conventional manufacturing approaches, AM technologies have shown promising results in enhancing sustainability in production systems. Various research has been conducted to assess the environmental impacts of AM based on the well-known life cycle assessment (LCA) framework. However, this approach requires intensive domain knowledge to build the environmental impact model and interpret the findings. This knowledge barrier may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts. Such challenges can be particularly prevalent during the early product design and planning stages. As such, the research community demands an automated LCA tool to support AM toward elevated sustainability. To achieve this ambitious goal, this paper particularly investigates the fundamental question—“What are the key influential parameters that pose an impact on the environmental sustainability of AM?” Thus, this paper proposes a methodological framework for identifying the key influential parameters for AM. The framework was demonstrated by taking the fused filament fabrication process as a case study. Through instantiating various parts within the proposed framework and conducting LCA on over 200 AM instances, followed by correlation analysis, the key influential parameters were identified. Consequently, a data-driven predictive sustainability assessment and optimization framework was developed by integrating the identified influential features.

The Impact of Additive Manufacturing Constraints and Design Objectives on Structural Topology Optimization

To analyze the impact of different objective functions and additive manufacturing (AM) constraints on structural topology optimization, it is necessary to perform an in-depth comparative study. This analysis should consider specific structural design factors, such as compliance, volume, or stress minimization, and assess their effects on the topology optimization for AM. In addition, the inclusion of AM constraints can have a significant influence on various aspects, including optimal part geometry, part volume, support structure volume, and structural performance. Thus, it is essential to investigate and compare these factors to determine the optimal part design for AM. This study focuses on comparing topology optimization results obtained using compliance, stress, or multi-objective minimization, with and without AM constraints. A comparative analysis was conducted in the study, utilizing three structural design examples: cantilever beam, bridge-shaped structure, and L-shaped beam. The comparison results provide insights into the effects of build orientation, AM constraints such as overhang, and different design objectives on the structural topology optimization for AM.

Scheduling of build and post processes for decomposed parts in additive manufacturing

Recently, production planning has been highlighted in the Additive Manufacturing (AM) literature. However, most AM studies have focused on a build process performed by an AM machine (also known as a 3D printer). Compared to the build process, AM studies for post-processing have lagged behind in production planning at the macro level. Post-processing needs highly sophisticated and complicated tasks requiring a lot of process time and cost. Therefore, some AM systems need to primarily cover post-processing as well as the build process, which leads to a more comprehensive approach to production planning. This paper addresses the AM systems based on flow-shop, comprehensively including the build and post processes. For post-processing, we consider trimming, coloring, and assembly processes. This paper introduces three scheduling policies: (1) all-for-one-job; (2) one-for-one-job; and (3) hybrid. The first policy is packing every part into the same job, which means that all parts are simultaneously fabricated during the same build process. The second policy is assigning each part to a particular job, which results in parts being fabricated one by one during the build process. The last policy is a compromise between the former two policies. We propose a mixed-integer programming (MIP) model to minimize the makespan for each policy. In computational experiments with test models, the scheduling optimization for AM is applied to various cases: multiple parts, scheduling policies, and multiple AM machines. One of the main comparisons provides that the MIP model of the hybrid policy is more preferred than the all-for-one-job policy, reducing the makespan by 27.68%. In conclusion, our paper quantitatively shows that packing is not a significant factor in minimizing the makespan when comprehensively considering build and post processes. It is a different perspective from that the traditional scheduling for AM has focused on a packing issue to improve the productivity in the build process. • A two-phased framework is proposed for minimizing the makespan in AM systems. • Mixed-integer programs are developed to optimize the scheduling of AM tasks. • Decomposing an original model into pieces is preferred in reducing the makespan. • Experimental results show that packing all the parts into a job is less productive.

Experimental and numerical investigation of 17–4PH stainless steel fabricated by laser powder bed fusion and hot isostatic pressing

Meticulous design and optimization of additive manufacturing (AM) are essential for obtaining high-quality metallic products, particularly using laser powder bed fusion (L-PBF). However, its potential in applications is limited because of the lack of understanding of AM. This makes the process parameter optimization time and cost-consuming. Here, the L-PBF process is employed to minimize defects and enhance the mechanical properties of 17–4PH stainless steel specimens, coupled with modeling. The optimal manufacturing parameters were determined by evaluating the relative densities of the as-built parts and thermal deformation. Either high or low energy densities resulted in high porosity and a higher energy density results in greater thermal deformation, attributed to the high mismatch in thermal expansion, while the surface roughness of as-built products is not as good as commercially available products. The hot isostatic pressing process improved the mechanical properties of the printed product by reducing the porosity and recrystallizing microstructures.

On preventing the dripping effect of overhang constraints in topology optimization for additive manufacturing

This article falls within the scope of topology optimization for Additive Manufacturing processes and proposes an alternative strategy to prevent the phenomenon known as the Dripping Effect. The Dripping Effect is when an overhang constraint is imposed on topology optimization processes for Additive Manufacturing and is defined as the formation of oscillatory contour trends within the prescribed threshold angle. Although these drop-like formations constitute local minimizers of the constraint function, they do not provide a printable feature, and, therefore, they neither eliminate the need to form temporary support structures. So far, there has been no general agreement on how to prevent the Dripping Effect, so this work aims to introduce a strategy that effectively prevents it, and that at the same time may be easy to extrapolate to other types of geometric overhang restrictions. This paper provides a study of the origin of the Dripping Effect and gives detailed instructions on how the proposed prevention strategy is applied. In addition, several benchmark examples where the Dripping Effect is prevented are shown.

Energy consumption distribution and optimization of additive manufacturing

Energy consumption distribution and optimization of additive manufacturing

Progress on different topology optimization approaches and optimization for additive manufacturing: a review

The progress of topology optimizations for the last decade has been reviewed based on the five main methods mentioned in Sigmund’s article in 2013: density, level-set, topological derivative, phase field, and evolutionary. The recent progress is mainly focusing on methods originated in density or level set method, with an inclination of combining them with the evolutionary method. The methods developed have a large number of applications, yet certain issues, e. g., guaranteeing the convergence of the results generated through evolutionary methods need further investigation. Besides, those studies specifically targeted for the optimization and designing for additive manufacturing are also discussed, as well as the ideas and guidelines for those designs. It is recognized that in order to cope with additive manufacturing, it is necessary to modify existing structures for the application of lattice structures and the reduction of support structures. Moreover, the introduction of the idea of designing for additive manufacturing (DfAM), as well as efforts to make the end-users understand and familiarize themselves with the dramatic differences in designs, are also desired changes that need more efforts to be applied on.

A comparative study between traditional topology optimization and lattice optimization for additive manufacturing

A comparative study between traditional topology optimization and lattice optimization for additive manufacturing

A framework for big data driven process analysis and optimization for additive manufacturing

PurposeThis paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.Design/methodology/approachFour components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM.FindingsThe presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase.Research limitations/implicationsThis study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.Practical implicationsThe proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.Originality/valueThis paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.

Planning and Scheduling in Additive Manufacturing

Recent advances in additive manufacturing (AM) and 3D printing technologies have led to significant growth in the use of additive manufacturing in industry, which allows for the physical realization of previously difficult to manufacture designs. However, in certain cases AM can also involve higher production costs and unique in-process physical complications, motivating the need to solve new optimization challenges. Optimization for additive manufacturing is relevant for and involves multiple fields including mechanical engineering, materials science, operations research, and production engineering, and interdisciplinary interactions must be accounted for in the optimization framework.
 In this paper we investigate a problem in which a set of parts with unique configurations and deadlines must be printed by a set of machines while minimizing time and satisfying deadlines, bringing together bin packing, nesting (two-dimensional bin packing), job shop scheduling, and constraints satisfaction. We first describe the real-world industrial motivation for solving the problem. Subsequently, we encapsulate the problem within constraints and graph theory, create a formal model of the problem, discuss nesting as a subproblem, and describe the search algorithm. Finally, we present the datasets, the experimental approach, and the preliminary results.

Influence of particle shape in the additive manufacturing process for ceramics

Additive manufacturing (AM) of ceramics through a vat photopolymerization (VP) process is a promising technique due to the high intrinsic resolution and the evanescence of stresses introduced during the layerwise additive manufacturing process. Compared to the regular vat photopolymerization process, the addition of ceramic powder increases physical complexity. Hence, large scale adoption and optimization of AM for ceramics requires a thorough understanding of the underlying physics. This work focuses on the light scattering introduced through a complex interplay between optical properties of different constituents and its effect on the photopolymerization. The effect of the shape of the ceramic particle is investigated for isolated and multiple particle cases using a parametric analysis with regular convex polygons. Although the analysis of two-dimensional isolated particle shapes indicates a preference for one shape over the other, this difference vanishes in a multiple-scattering situation. A three-dimensional analysis furthermore shows that a two-dimensional approximation of the three-dimensional scattering problem is relevant and can be used to further the process understanding of ceramic VP.

Multiphysical modeling of the photopolymerization process for additive manufacturing of ceramics

Abstract Additive manufacturing (AM) of ceramics through vat photopolymerization is a promising technique in which a ceramic filled photopolymer is selectively solidified in a layer-wise manner towards the final part geometry. Large scale adoption and optimization of AM for ceramics requires an in depth understanding of the process, which is pursued through a theoretical-numerical approach in this work. A modeling framework is proposed that integrates the coupled effect of four relevant physical mechanisms: (i) light propagation through the heterogeneous matter; (ii) conversion of the photopolymer; (iii) thermal effects and (iv) evolution of mechanical properties upon solidification. Interestingly, the inclusion of ceramic particles (compared to the regular vat photopolymerization process) has a marked influence for each individual physical mechanism. Even though the individual key ingredients are established, the coupled and integrated framework provides innovative insights, demonstrating how difficult it is to achieve homogeneous polymerization for ceramic-filled resins.

AM optimization framework for part and process attributes through geometric analysis

In additive manufacturing (AM) processes, part and process attributes are often optimized with build orientation/tool-path direction. Both of them may alter the layer topology and tool-path pattern which implicitly affect the part and process attributes. However, optimizing either build orientation or tool-path direction independently undermines the hierarchical relationship in the AM process plan and may produce a sub-optimal solution. In this paper, an integrated framework is proposed to quantify their combined effect on the part and process attributes by analyzing the generated geometry. The proposed methodology is designed on the basis of the layer geometries to ensure manufacturability and minimize fabrication complexity in AM processes. The objective function is formulated with weighted attributes and the weights are optimized by Hypothetical Equivalents and Inequivalents Method (HEIM). Both build orientation and tool-path/deposition direction are concurrently optimized using a Genetic Algorithm (GA). The proposed algorithm is implemented on two free form shaped objects and the process converges within a reasonable number of iterations. The outcome is also compared with an exhaustive search method.