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Quantifying and controlling the quality characteristics of parts produced by additive manufacturing (AM) processes has attracted significant interest in the research community. However, to increase the sustainability of AM processes, such quality characteristics need to be assessed together with life cycle performance of AM processes such as energy and material consumption and manufacturing cost. Although a few studies have been performed for several quality characteristics, i.e. surface roughness and tensile strength, the relationship between dimensional performance and manufacturing cost is still not well known for AM processes.

In this paper, a comprehensive study of the dimensional performance and manufacturing cost of fused deposition modeling AM process is performed. Design of experiment technique is used, and the correlation of different cost components and the dimensional accuracy of parts are statistically studied.

The optimum process parameters for simultaneously optimizing the dimensional performance and manufacturing cost are identified. The analysis shows that as opposed to traditional manufacturing processes, obtaining a better dimensional performance is not necessarily associated with higher cost in the AM processes.

Almost no study and analysis for the combined dimensional performance and manufacturing cost has been performed for AM processes in the literature. It is known that within the context of traditional manufacturing processes, a natural trade-off governs the pursuit of higher dimensional performance and the manufacturing cost. However, as the AM process has a different nature compared with traditional manufacturing processes, the relationship between manufacturing cost and dimensional performance of parts has to be studied. Understanding this relationship will also help to establish a cost-optimal and sustainable tolerance allocation strategy in assemblies with AM components.

This paper aims to investigate the effect of post-processing techniques on dimensional accuracy of laser sintering (LS) of Nylon and Alumide^® and fused deposition modelling (FDM) of acrylonitrile butadiene styrene (ABS) materials.

Additive manufacturing (AM) of test pieces using LS of Nylon and Alumide^® powders, as well as the FDM of ABS materials, were first conducted. Next, post-processing of the test pieces involved tumbling, shot peening, hand finishing, spray painting, CNC machining and chemical treatment. Touch probe scanning of the test pieces was undertaken to assess the dimensional deviation, followed by statistical analysis using Chi-square and Z-tests.

The deviation ranges of the original built parts with those being subjected to tumbling, shot peening, hand finishing, spray painting, CNC machining or chemical treatment were found to be different. Despite the rounding of sharp corners and the removal of small protrusions, the dimensional accuracy of relatively wide surfaces of Nylon or Alumide^® test pieces were not significantly affected by the tumbling or shot peening processes. The immersion of ABS test pieces into an acetone bath produced excellent dimensional accuracy.

Only Nylon PA2200 and Alumide^® processed through LS and ABS P400 processed through FDM were investigated. Future work could also examine other materials and using parts produced with other AM processes.

The service bureaus that produce prototypes and end-use functional parts through AM will be able to apply the findings of this investigation.

This research has outlined the differences of post-processing techniques such as tumbling, shot peening, hand finishing, spray painting, CNC machining and chemical treatment. The paper discusses the advantages and disadvantages of each of those methods and suggests that the immersion of ABS test pieces into an acetone bath produced excellent dimensional accuracy.

The assembly quality of complex products is pivotal to their lifecycle performance. Assembly precision analysis (APA) is an effective method used to check the feasibility and quality of assembly. However, there is still a need for a systematic approach to be developed for APA of kinematic mechanisms. To achieve more accurate analysis of kinematic assembly, this paper aims to propose a precision analysis method based on equivalence of the deviation source.

A unified deviation vector representation model is adopted by considering dimension deviation, geometric deviation, joint clearance and assembly deformation. Then, vector loops and vector equations are constructed, according to joint type and deviation propagation path. A combined method, using deviation accumulation and sensitivity modeling, is applied to solve the kinematic APA of complex products.

When using the presented method, geometric form deviation, joint clearance and assembly deformation are considered selectively during tolerance modeling. In particular, the proposed virtual link model and its orientation angle are developed to determine joint deviation. Finally, vector loops and vector equations are modeled to express deviation accumulation.

The proposed method provides a new means for the APA of complex products, considering joint clearance and assembly deformation while improving the accuracy of APA, as much as possible.

Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure.

The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process.

The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well.

The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.

This paper aims to investigate the dimensional accuracy consisting of thickness, grip section width, full length, circularity, cylindricity and surface finish of printed polyurethane dog-bone samples based on American Society for Testing and Materials D638 type V standard, which were optimally printed by fused deposition modelling (FDM).

The experimental approach focuses on determining main effects of printing parameters, including nozzle temperature, infill percentage, print speed and layer height on dimensional error and surface finish of the printed samples, followed by the confirmation tests to warrant the reproducibility of experimental results.

This study shows that layer height has the most significant impact on dimensional accuracy and surface finish of printed samples compared to other printing parameters, whereas infill density has no significant effect on all sample dimensions.

This paper presents a comprehensive study relating to various dimensional accuracies in terms of full length, grip section width, thickness, circularity, cylindricity and surface finish of dog-bone samples printed by FDM to improve the printability and processibility via additive manufacturing.

The purpose of this paper is to investigate the influence of low-cost chemical vapour treatment process on geometric accuracy and surface roughness of different curved and freeform surfaces of fused deposition modelling (FDM) specimens build at different part building orientations.

Parts with different primitive and curved surfaces are designed and modelled to build at three different part orientations along X orientation (vertical position resting on side face), Y orientation (horizontal position resting on base) and Z orientation (upright position). Later, the parts are post-processed by cold vapours of acetone. Geometric accuracy and surface roughness are measured both before and after the chemical treatment to investigate the change in geometric accuracy, surface roughness of FDM parts.

The results indicate that surface roughness is reduced immensely after cold vapour treatment with minimum variation in geometric accuracy of parts. Parts build vertically over its side face (X orientation) provides the overall better surface finish and geometric accuracy.

The present study provides an approach of post-built treatment for FDM parts and observes a significant improvement in surface finish of the components. The present approach of post-built treatment can be adopted to enhance the surface quality as well as to achieve desired geometric accuracy for different primitive, freeform/curved surfaces of FDM samples suitable for functional components as well as prototypes.

The purpose of this paper is to optimize the process parameters to obtain the best dimensional accuracy, surface finish and hardness of the castings produced by using fused deposition modeling (FDM)-based patterns in investment casting (IC).

In this paper, hip implants have been prepared by using plastic patterns in IC process. Taguchi design of experiments has been used to study the effect of six different input process parameters on the dimensional deviation, surface roughness and hardness of the implants. Analysis of variance has been used to find the effect of each input factor on the output. Multi-objective optimization has been done to find the combined best values of output.

The results proved that the FDM patterns can be used successfully in IC. A wax coating on the FDM patterns improves the surface finish and dimensional accuracy. The improved dimensional accuracy, surface finish and hardness have been achieved simultaneously through multi-objective optimization.

A thin layer of wax is used on the plastic patterns. The effect of thickness of the layer has not been considered. Further research is needed to study the effect of the thickness of the wax layer.

The results obtained by the study would be helpful in making decisions regarding machining and/or coating on the parts produced by this process.

In this paper, multi-objective optimization of dimensional accuracy, surface roughness and hardness of hybrid investment cast components has been performed.

The purpose of this study is to mitigate the dimensional inaccuracy due to vertical curling/bending deformation of three-dimensional (3D) printed parts produced by selective laser sintering (SLS) using PA12 based on dimensional compensation of the computer-aided design (CAD) model.

To carry out this study, specially designed features are initially produced as references, and the dimensional deviations from the vertical bending deformation of the SLS process are analyzed. Next, the deformation patterns are formulated using a polynomial regression model in the global Cartesian coordinates of the building platform. Then, the compensation algorithm is implemented and the original 3D CAD file is preprocessed with an inverse transformation of the features to compensate the deformation errors.

It was found that the 3D printed parts from the SLS process have the dimensional inaccuracy due to the vertical bending pattern of the quadratic form. By implementing the compensation algorithm, it was statistically shown to effectively reduce bending deformations of various sample parts, including the automotive components, in SLS.

The position of samples in a batch has a direct impact on not only bending deformation but also on horizontal shape geometry error. However, the application of this algorithm is focused on the vertical bending deformation, which is estimated as a major part of dimensional inaccuracy.

This paper provides a practical case study with a real vehicle part. The algorithm was shown to provide a more realistic solution to the dimensional deformation of printed products, which is not manageable by simply using the constant scale factors provided by SLS 3D printer manufacturers.

This paper suggests that the vertical bending deformation from SLS’s 3D printed complex parts can be improved through the proposed compensation algorithm. The compensation algorithm was constructed by using the predictive regression model created from the bending deformation patterns of reference samples. The proposed compensation algorithm can be further used and applied for other complex samples without making additional reference parts.

Dimensional accuracy analysis of wax model created by room temperature vulcanization (RTV) silicone rubber molding to be used in investment casting is presented. The purpose of this paper is to discuss the effective parameters of dimensional accuracy in RTV tooling technique.

After producing a pattern by stereolithography (SLA) and then creating the RTV silicone rubber mold by the SLA pattern, dimensional accuracy of wax models produced by RTV tool are analyzed. Design of experiments (DOE) using the Taguchi approach is used for analysis and determination of optimum condition.

Experiments show that the dimensional accuracy in RTV technique is as accurate as in traditional molding. Hence, RTV tooling technique can be used in investment casting. Using Taguchi approach based on DOE, it was realized that the optimum condition to achieve acceptable accuracy is 35°C for mold temperature, 85°C for wax temperature, and −0.5 barG for vacuum pressure.

RTV silicone rubber mold is a useful alternative of metallic mold to produce wax patterns for investment casting. It has benefits such as reduction in production lead‐time and cost, compared with traditional metallic mold.

A case study for research of dimensional accuracy of wax patterns created by RTV silicone rubber mold had not been attempted as such. In addition to reduction in production lead‐time and cost, the dimensional accuracy of wax patterns using RTV tooling technique are as accurate as in traditional technique.

One major problem preventing further application and benefits from additive manufacturing (AM) nowadays is that AM build parts always end up with poor geometrical quality. To help improving geometrical quality for AM, this study aims to propose geometrical deviation identification and prediction method for AM, which could be used for identifying the factors, forms and values of geometrical deviation of AM parts.

This paper applied the skin model-based modal decomposition approach to describe the geometrical deviations of AM and decompose them into different defect modes. On that basis, the approach to propose and extend defect modes was developed. Identification and prediction of the geometrical deviations were then carried out with this method. Finally, a case study with cylinders manufactured by fused deposition modeling was introduced. Two coordinate measuring machine (CMM) machines with different measure methods were used to verify the effectiveness of the methods and modes proposed.

The case study results with two different CMM machines are very close, which shows that the method and modes proposed by this paper are very effective. Also, the results indicate that the main geometrical defects are caused by the shrinkage and machine inaccuracy-induced errors which have not been studied enough.

This work could be used for identifying and predicting the forms and values of AM geometrical deviation, which could help realize the improvement of AM part geometrical quality in design phase more purposefully.