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After experimental testing, it was recognized that a component’s strength relationship with respect to the volume material usage is inconsistent and that failures occurred in regions of voids. The purpose of this study is to present an optimal toolpath for a material extrusion process to minimize voids and discontinuities using standard parameters and settings available for any given machine.

To carry out this study, a literature review was performed to understand the influence of the build parameters. Then, an analysis of valid parameter settings to be targeted was performed for a commercial system. Fortus 400 machine build parameters are used for the case studies presented here. Optimal relationships are established based on the geometry and are to be applied on a layer-by-layer or sub-region basis and available machine build options. The component geometry is analyzed and decomposed into build regions. Matlab® is used to determine a standard (available) toolpath parameters with optimal variables (bead height, bead width, raster angle and the airgap) for each layer/build region.

It was found that the unwanted voids are decreased by up to 8 per cent with the new model. The final component will contain multiple bead widths and overlap conditions, but all are feasible as the available machine solutions are used to seed the model.

Unwanted voids can create failure points. Introducing an optimization solution for a maximized material fill strategy using existing build options will reduce the presence of voids and will eliminate “chimneys” or a void present in every layer of the component. This solution can be implemented using existing machine-toolpath solutions.

This study demonstrates that existing build settings and toolpath strategies can be used to improve the interior fill by performing targeted optimization strategies for the build parameters.

The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy. Complementing the modelling challenges is the development of a framework and methodologies to minimize data collection while maximizing the goodness of fit for the predictive models. This is essential for developing a foundation for metallic additive manufacturing process planning solutions.

Using the coaxial powder flow laser cladding method, 420 steel cladding powder is deposited on low carbon structural steel plates. A design of experiments (DOE) approach is taken using the response surface methodology (RSM) to establish the experimental configuration. The five process parameters such as laser power, travel speed, etc. are varied to explore their impact on the bead geometry. A total of three replicate experiments are performed and the collected data are assessed using a variety of methods to determine the process trends and the best modelling approaches.

There exist unpredictable, non-linear relationships between the process parameters and the bead geometry. The best fit for a predictive model is achieved with the artificial neural network (ANN) approach. Using the RSM, the experimental set is reduced by an order of magnitude; however, a model with R2 = 0.96 is generated with ANN. The predictive model goodness of fit for a single bead is similar to that for the overlapping bead geometry using ANN.

Developing a bead shape to process parameters model is challenging due to the non-linear coupling between the process parameters and the bead geometry and the number of parameters to be considered. The experimental design and modelling approaches presented in this work illustrate how designed experiments can minimize the data collection and produce a robust predictive model. The output of this work will provide a solid foundation for process planning operations.

The purpose of this paper is to characterize mechanical properties (tensile, compressive and flexural) for the three-dimensional printing (3DP) process, using various common recommended infiltrate materials and post-processing conditions.

A literature review is conducted to assess the information available related to the mechanical properties, as well as the experimental methodologies which have been used when investigating the 3D printing process characteristics. Test samples are designed, and a methodology to measure infiltrate depths is presented. A full factorial experiment is conducted to collect the tensile, compressive and bending forces for a set of infiltrates and build orientations. The impact of the infiltrate type and depth with respect to the observed strength characteristics is evaluated.

For most brittle materials, the ultimate compression strength is much larger than the ultimate tensile strength, which is shown in this work. Unique stress–strain curves are generated from the infiltrate and build orientation conditions; however, the compressive strength trends are more consistent in behavior compared to the tensile and flexural results. This comprehensive study shows that infiltrates can significantly improve the mechanical characteristics, but performance degradation can also occur, which occurred with the Epsom salts infiltrates.

More experimental research needs to be performed to develop predictive models for design and fabrication optimization. The material-infiltrate performance characteristics vary per build orientation; hence, experimental testing should be performed on intermediate angles, and a double angle experiment set should also be conducted. By conducting multiple test scenarios, it is now understood that this base material-infiltrate combination does not react similar to other materials, and any performance characteristics cannot be easily predicted from just one study.

These results provide a foundation for a process design and post-processing configuration database, and downstream design and optimization models. This research illustrates that there is no “best” solution when considering material costs, processing options, safety issues and strength considerations. This research also shows that specific testing is required for new machine–material–infiltrate combinations to calibrate a performance model.

There is limited published data with respect to the strength characteristics that can be achieved using the 3DP process. No published data with respect to stress–strain curves are available. This research presents tensile, compressive and flexural strength and strain behaviors for a wide variety of infiltrates, and post-processing conditions. A simple, unique process is presented to measure infiltrate depths. The observed behaviors are non-linear and unpredictable.

This paper aims to propose a new framework on prioritizing and deployment of design for additive manufacturing (DfAM) strategies to an industrial component using Fuzzy TOPSIS multiple criteria decision-making (MCDM) techniques. The proposed framework is then applied to an automotive component, and the results are discussed and compared with existing design.

Eight DfAM design alternatives associated with eight design criteria have been identified for framing new DfAM strategies. The prioritization order of the design alternatives is identified by Fuzzy TOPSIS MCDM technique through its closeness coefficient. Based on Fuzzy TOPSIS MCDM output, each of the design alternatives is applied sequentially to an automobile component as a case study. Redesign is carried out at each stage of DfAM implementation without affecting the functionality.

On successful implementation of proposed framework to an automotive component, the mass is reduced by 43.84%, from 0.429 kg to 0.241 kg. The redesign is validated by finite element analysis, where von Mises stress is less than the yield stress of the material.

The proposed DfAM framework and strategies will be useful to designers, R&D engineers, industrial practitioners, experts and consultants for implementing DfAM strategies on any industrial component without impacting its functionality.

To the best of the authors’ knowledge, the idea of prioritization and implementation of DfAM strategies to an automotive component is the original contribution.

This paper aims to propose a vacuum-assisted post-processing method for use in binder jetted technology. The method is based on six key technological parameters and uses standard, commercially available consumables to achieve improvement in tensile strength, as well as the microstructure and porosity of the infiltrated matrix.

Six key technological parameters were systematically varied as factors on three levels, using design of experiment, i.e. definitive screening design. Surface response methodology was used to optimize the process and yield optimal tensile strength for the given range of input factors. Thus obtained, the optimized factor settings were used in a set of confirmation runs, where the result of optimization was experimentally confirmed. To confirm improvement in microstructure of the infiltrated matrix, SEM analysis was performed, while the reduction of porosity was analyzed using mercury porosimetry.

The obtained results indicate that, compared to its conventional counterpart, the proposed, optimized infiltration method yields improvement in tensile strength which is significant from both the statistical and engineering point of view, while reducing porosity by 3.5 times, using only standard consumables. Scanning electron microscopy examination of fractured specimens’ micrographs also revealed significant morphological differences between the conventional and proposed method of post-processing. This primarily reflects in higher surface area under hardened epoxy infiltrate, which contributes to increased load capacity of specimen cross-section.

At the present stage of development, the most important limitation of the proposed method is the overall size of models which can be accommodated in standard vacuum impregnation units. Although, in this study, the infiltration method did not prove statistically significant, further investigation is required with models of complex geometry, various sizes and mass arrangements, where infiltration would be more challenging and could possibly result in different findings.

The most important practical implication of this study is the experimentally verified result of optimization, which showed that tensile strength and matrix microstructure can be significantly improved, using just standard consumables.

Improved strength contributes to reduction of material consumption, which, in a longer run, can be beneficial for environment protection and sustainable development.

Based on literature review, there have been no previous investigations which studied the tensile strength of infiltrated specimens through design of experiment, which involved specimen preheating temperature, level and duration of vacuum treatment of infiltrate mixture and infiltrated specimens and infiltration method.

The purpose of this paper is to develop a quantitative model to assess probability of errors and errors correction costs in parts feeding systems for assembly lines.

Event trees are adopted to model errors in the picking-handling-delivery-utilization of materials containers from the warehouse to assembly stations. Error probabilities and quality costs functions are developed to compare alternative feeding policies including kitting, line stocking and just-in-time delivery. A numerical case study is included.

This paper confirms with quantitative evidence the economic relevance of logistic errors (LEs) in parts feeding processes, a problem neglected in the existing literature. It also points out the most frequent or relevant error types and identifies specific corrective measures.

While the model is general purpose, conclusions are specific to each applicative case and are not generalizable, and some modifications may be required to adapt it to specific industrial cases. When no experimental data are available, human error analysis should be used to estimate event probabilities based on underlying modes and causes of human error.

Production managers are given a quantitative decision tool to assess errors probability and errors correction costs in assembly lines parts feeding systems. This allows better comparing of alternative parts feeding policies and identifying corrective measures.

This is the first paper to develop quantitative models for estimating LEs and related quality cost, allowing a comparison between alternative parts feeding policies.

The effects of infiltrant-related factors during post-processing on mechanical performance are fully considered for three-dimensional printing (3DP) technology. The factors contain infiltrant type, infiltrating means, infiltrating frequency and time interval of infiltrating.

A series of printing experiments are conducted and the parts are processed with different conditions by considering the above mentioned four parameters. Then the mechanical performances of the parts are tested from both macroscopic and microscopic papers. In the macroscopic view, the compressive strength of each printed part is measured by the materials testing machine – Instron 3367. In the microscopic view, scanning electron microscope and energy dispersion spectrum are used to obtain microstructure images and element content results. The pore size distributions of the parts are measured further to illustrate that if the particles are bound tightly by infiltrant. Then, partial least square (PLS) is used to conduct the analysis of the influencing factors, which can solve the small-sample problem well. The regression analysis and the influencing degree of each factor are explored further.

The experimental results show that commercial infiltrant has an outstanding performance than other super glues. The infiltrating action will own higher compressive strength than the brushing action. The higher infiltrating frequency and inconsistent infiltrating time interval will contribute to better mechanical performance. The PLS analysis shows that the most important factor is the infiltrating method. When compare the fitted value with the actual value, it is clear that when the compressive strength is higher, the fitting error will be smaller.

The research will have extensive applicability and practical significance for powder-based additive manufacturing.

The impact of the infiltrating-related post-processing on the performance of 3DP technology is easy to be ignored, which is fully taken into consideration in this paper. Both macroscopic and microscopic methods are conducted to explore, which can better explain the mechanical performance of the parts. Furthermore, as a small-sample method, PLS is used for influencing factors analysis. The variable importance in the projection index can explain the influencing degree of each parameter.

The purpose of this paper is to relate additive manufacturing (AM) and machining (CNC) synergistically in a modular approach in the design and manufacturing domains, to generate value for end‐users and manufacturers (a teleological system).

The research methodology decomposes a part into modules, by employing a teleological systems theory approach paired with principles of modular design. Modules are manufactured with either additive manufacturing (fused deposition modeling, FDM) or machining (CNC). Process selection is determined by a decision‐making framework that quantifies strength and weakness comparisons of FDM and CNC machining processes, accomplished using the analytic hierarchy process (AHP).

The developed methodology and decision‐making framework is successfully applied to the design and manufacturing of a large, complex V6 engine section sand casting pattern. This case study highlights the merits of the research.

The research assumes that the processes being considered are capable of meeting the product functional requirements. The proposed methodology can be extended to evaluate additional processes.

Value is assessed in this research relative to: time and cost opportunities, managing knowledge limitations of a process by leveraging hybrid options, and aligning design and manufacturing to create a product that accomplishes the goals of the end‐user (teleological effectiveness).

Utilizing the AHP process and a teleological perspective are new, and proven effective, approaches in relating additive and subtractive processes in a hybrid approach with end‐user perspectives. The research demonstrates a systematic methodology to quantify additive and subtractive process selection.

Complexity is the main challenge for present and future manufacturers. Assembly complexity heavily affects a product’s final quality in the fully automated assembly system. This paper aims to propose a new method to assess the complexity of modern automated assembly system at the assembly design stage with respect to the characteristics of both manufacturing system and each single component to be mounted. Aiming at validating the predictive model, a regression model is additionally presented to estimate the statistic relationship between the real assembly defect rate and predicted complexity of the fully automated assembly system.

The research herein extends the S. N. Samy and H. A. ElMaraghy’s model and seeks to redefine the predictive model using fuzzy evaluation against a fully automated assembly process at the assembly design stages. As the evaluation based on the deterministic scale with accurate crisp number can hardly reflect the uncertainty of the judgement, fuzzy linguistic variables are used to measure the interaction among influence factors. A dependency matrix is proposed to estimate the assembly complexity with respect to the interactions between mechanic design, electric design and process factors and main functions of assembly system. Furthermore, a complexity attributes matrix of single part is presented, to map the relationship between all individual parts to be mounted and three major factors mentioned in the dependency matrix.

The new proposed model presents a formal quantification to predict assembly complexity. It clarifies that how the attributes of assembly system and product components complicate the assembly process and in turn influence the manufacturing performance. A center bolt valve in the camshaft of continue variable valve timing is used to demonstrate the application of the developed methodology in this study.

This paper presents a developed method, which can be used to improve the design solution of assembly concept and optimize the process flow with the least complexity.

Fused deposition modelling (FDM) is the most economical additive manufacturing technique. The purpose of this paper is to describe a detailed review of this technique. Total 211 research papers published during the past 26 years, that is, from the year 1994 to 2019 are critically reviewed. Based on the literature review, research gaps are identified and the scope for future work is discussed.

Literature review in the domain of FDM is categorized into five sections – (i) process parameter optimization, (ii) environmental factors affecting the quality of printed parts, (iii) post-production finishing techniques to improve quality of parts, (iv) numerical simulation of process and (iv) recent advances in FDM. Summary of major research work in FDM is presented in tabular form.

Based on literature review, research gaps are identified and scope of future work in FDM along with roadmap is discussed.

In the present paper, literature related to chemical, electric and magnetic properties of FDM parts made up of various filament feedstock materials is not reviewed.

This is a comprehensive literature review in the domain of FDM focused on identifying the direction for future work to enhance the acceptability of FDM printed parts in industries.