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Due to its unique benefits over standard conventional "subtractive" manufacturing, additive manufacturing is attracting growing interest in academic and industrial sectors. Here, special emphasis is given to wire arc additive manufacturing (WAAM), a directed energy deposition process that employs arc welding tools and wire to build metallic components by deposition of weld material. The WAAM process has several advantages, e.g., low cost, rapid deposition rate, and suitability for large complex metallic components. However, many WAAM challenges such as large welding deformation, undesirable porosity, and components with high residual stress remain to be overcome. Multidisciplinary cross-fusion research involving manufacturing, material science, automation control, and artificial intelligence/machine learning (ML) are deployed to overcome the above-mentioned problems. ML enables improved product quality control, process optimization, and modeling of complex multiphysics systems in the WAAM process. This research utilizes a data-driven literature review process, a defined and deliberate approach to localizing, evaluating, and analyzing published studies in the literature. The most relevant studies in the literature are analyzed using keyword co-occurrence and cluster analysis. Numerous aspects of WAAM, including design for WAAM, material analytics/characterization, defect detection/monitoring, as well as process modeling and optimization, have been examined to identify state-of-the-art research in ML for WAAM. Finally, the challenges and opportunities for using ML in the WAAM process are identified and summarized. [ABSTRACT FROM AUTHOR]

To improve the microstructure and mechanical properties of fundamental materials including aluminum, stainless steel, superalloys, and titanium alloys, traditional manufacturing techniques have for years been utilized in critical sectors including the aerospace and nuclear industries. However, additive manufacturing has become an efficient and effective means for fabricating these materials with superior mechanical attributes, making it easier to develop complex parts with relative ease compared to conventional processes. The waste generated in additive manufacturing processes are usually in the form of powders, while that of conventional processes come in the form of chips. The current study focuses on the features and uses of various typical recycling methods for traditional and additive manufacturing that are presently utilized to recycle material waste from both processes. Additionally, the main factors impacting the microstructural features and density of the chip-unified components are discussed. Moreover, it recommends a novel approach for recycling chips, while improving the process of development, bonding quality of the chips, microstructure, overall mechanical properties, and fostering sustainable and environmentally friendly engineering. [ABSTRACT FROM AUTHOR]

The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes increase, there is an increasing need for advanced techniques to ensure quality control, optimize performance, and reduce production costs. Multiple tests are required to optimize processing variables for specific equipment and processes, to achieve optimum processing conditions. The application of digital twins (DTs) has significantly enhanced the field of additive manufacturing. A digital twin, abbreviated as DT, refers to a computer-generated model that accurately depicts a real-world object, system, or process. A DT comprises the complete additive manufacturing process, from the initial conception phase to the final manufacturing phase. It enables the manufacturing process to be continuously monitored, studied, and optimized in real time. DT has emerged as an important tool in the additive manufacturing industry. They allow manufacturers to enhance the process, improve product quality, decrease costs, and accelerate innovation. However, the development of DT in AM is an iterative and continuous process. It requires collaboration between domain experts, data scientists, engineers, and manufacturing teams to guarantee an accurate representation of the process by the digital twin. This paper aims to provide a comprehensive analysis of the current state of DT for additive manufacturing, examining their applications, benefits, challenges, and future directions. [ABSTRACT FROM AUTHOR]

Plasma arc deposition as an additive manufacturing technology has unique advantages for producing parts with complex shapes through layer-by-layer deposition. It is critical to predict and control the temperature field during the production process due to the temperature distribution and gradients determining the properties and performance of the part. Numerical simulation approaches, such as the finite element method, which provides a large amount of data for machine learning modeling, thus reducing the overhead of experimental measurements, are widely used in machine learning. In this paper, we propose a neural network combined finite element method and process prediction workflow. A one-dimensional convolutional neural network model for predicting 2D temperature distribution is developed by training the collected data on the planar temperature field of titanium–aluminum twin-wire plasma arc additive manufacturing and the finite element method. The results show that the predicted temperature mean square error is only 0.5, with less than a 20 °C error in peak temperature and a relative error below 1%. The proposed transfer learning method achieves the same training loss and is 500 iterations faster than basic training, which improves the training speed by 25%. The current study confirms the accurate performance of the ML model and the effectiveness of the optimization method. [ABSTRACT FROM AUTHOR]

Featured Application: This work lies in the field of hydraulic drive systems maintenance and remanufacturing and within the framework of the circular economy concept. The specific application is the remanufacture of hydraulic flowmeter components using additive manufacturing and reverse engineering techniques. By successfully repairing and upgrading hydraulic components like flowmeters, this approach contributes to reducing waste, conserving resources, and extending the lifespan of critical components in hydraulic drive systems. This innovative approach can not only enable the cost-effective restoration of damaged components but also enhances their performance by leveraging advanced materials and manufacturing technologies. The potential applications extend to various industries relying on hydraulic systems, including manufacturing, construction, agriculture, and transportation. By adopting these methods, organizations can promote sustainability, minimize downtime, and improve the efficiency and reliability of hydraulic systems, aligning with the principles of the circular economy. In response to the imperative of sustainability and resource efficiency, this article introduces an innovative framework for the remanufacture of hydraulic drive system components, firmly rooted in the tenets of the circular economy. This method mixes cutting-edge additive manufacturing technologies, meticulous 3D scanning techniques, and the rigorous discipline of reverse engineering to rebuild active elements within hydraulic drive systems. Fundamental to this framework is a resolute commitment to the repurposing of non-defective components, fostering a closed-loop system that systematically curtails waste generation and significantly diminishes the environmental footprint associated with hydraulic drive system maintenance. This article presents a meticulous examination of the remanufacturing process for a flowmeter, elucidating the intricate integration of additive manufacturing technology and the precision of 3D scanning to restore a previously damaged rotor. Through this interdisciplinary approach, we aim to elucidate the manner in which contemporary engineering practices can be harnessed to catalyze circularity, enhance resource efficiency, and increase sustainability within industrial operations. By demonstrating the efficacy of additive manufacturing and reverse engineering in hydraulic system remanufacturing, this article contributes significantly to the discourse on sustainable engineering methodologies, underlining their pivotal role in shaping a more environmentally conscientious, circular economy. [ABSTRACT FROM AUTHOR]

This research is centered on optimizing the mechanical properties of additively manufactured (AM) lattice structures via strain optimization by controlling different design and process parameters such as stress, unit cell size, total height, width, and relative density. In this regard, numerous topologies, including sea urchin (open cell) structure, honeycomb, and Kelvin structures simple, round, and crossbar (2 × 2), were considered that were fabricated using different materials such as plastics (PLA, PA12), metal (316L stainless steel), and polymer (thiol-ene) via numerous AM technologies, including stereolithography (SLA), multijet fusion (MJF), fused deposition modeling (FDM), direct metal laser sintering (DMLS), and selective laser melting (SLM). The developed deep-learning-driven genetic metaheuristic algorithm was able to achieve a particular strain value for a considered topology of the lattice structure by controlling the considered input parameters. For instance, in order to achieve a strain value of 2.8 × 10−6 mm/mm for the sea urchin structure, the developed model suggests the optimal stress (11.9 MPa), unit cell size (11.4 mm), total height (42.5 mm), breadth (8.7 mm), width (17.29 mm), and relative density (6.67%). Similarly, these parameters were controlled to optimize the strain for other investigated lattice structures. This framework can be helpful in designing various AM lattice structures of desired mechanical qualities. [ABSTRACT FROM AUTHOR]

Purpose: Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application. Design/methodology/approach: This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain. Findings: A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework. Originality/value: This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application. [ABSTRACT FROM AUTHOR]

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), which is also called rapid prototyping/3D printing/layered manufacturing, can be considered as a rapid conversion between digital and physical models. One of the most used materials in AM is polylactic acid (PLA), which has advantageous material properties such as biocompatibility, biodegradability, and nontoxicity. For many medical applications, it is considered as a leading biomaterial. In dentistry, in addition to its uses in dental models (education, teaching, simulation needs), it can be used for therapeutic objectives and tissue engineering. The fused filament fabrication (FFF) technique, also called fused deposition modeling (FDM), is widely used as an AM technique to perform complex and functional geometries directly from CAD files. In this review, the objective was to present the different challenges and future perspectives of this additively manufactured material by using FFF in dentistry areas. Some suggestions for future directions to extend to more dental applications (support structures, lattice structures, etc.) and to consider more criteria (sustainability, uncertainty etc.) will be discussed. Advanced studies such as machine learning (ML) techniques will be suggested to reduce the failure cases when using the additively manufactured PLA by FFF in dentistry. [ABSTRACT FROM AUTHOR]

In the context of additive manufacturing, the adjustment of process data to individual geometric features offers the potential to further increase manufacturing speed and quality, while being widely underestimated in recent research. Unfortunately, the current non-uniformd at a handling in the CAD-CAM-Link results in adown stream data loss, that prevents the availability of geometric knowledge from being present at any time to apply the more advanced approaches of adaptive slicing and tool path generation. Automatic detection of various geometric entities would be beneficial for classifying partial surfaces and volumetric ranges to gain customized informational insights of geometric parameterization. In this work, an enhanced approach of geometric deep learning for the analysis of voxelized engineering parts will be presented to align the inference representations to modeling paradigms for complex design models like architected materials. Although the baseline voxel representation offers distinct advantages in detection accuracy, it comes with an adversely large memory footprint. The geometry discretization leads to high resolutions needed to capture various detail levels that prevent the analysis of fine-grained objects. To achieve efficient usage of three-dimensional (3D) deep learning techniques, we propose a 3D-convolutional neural network-based feature recognition approach using signed distance field data to limit the needed resolution. These implicit geometric data leverage the advantages of volumetric convolution while alleviating their disadvantages through the use of the continuous signed distance function. When analyzing computer-aided design data for geometric primitive features, a common application task in surface reconstruction of reverse engineering the proposed methodology, achieves a detection accuracy that is in line with the accuracy values achieved by comparable algorithms. This enables the recognition offine-grained surface instances. The unambiguous shape information extracted could be used in subsequent adaptive slicing algorithms to achieve individual geometry-based hatch generation. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM), a technique leveraging 3D modeling data to fabricate objects through layer-by-layer material deposition, has seen a surge in adoption across industries. This has, in turn, spurred rapid advancements in design, process, and manufacturing technologies integral to AM. Simultaneously, Machine Learning (ML), a subset of artificial intelligence centered on enabling self-improvement in computer programs, has carved its niche in this burgeoning field. This review provides an in-depth exploration of recent advancements in the application of ML within the AM framework. Specifically, the focus is placed on regression, classification, and clustering tasks integral to anomaly identification and parameter optimization in AM processes. A comparative analysis of the efficacy of various ML algorithms in executing these tasks forms the crux of this review. In light of these developments, the paper seeks to underscore the potential of ML as a viable tool in augmenting the capabilities of AM, thereby offering insights that could guide future research and development efforts in this interdisciplinary domain. [ABSTRACT FROM AUTHOR]

The printing accuracy is one of the most important metrics to evaluate the additive manufacturing (AM) machine. In this paper, an error identification and compensation method for Cartesian 3D printer is presented based on a specially designed test artifact to improve printing accuracy. The relationship between the geometric errors of the printed object and the kinematic errors of the printer axes is established based on the theory of the multi-body system. A series of formulas are derived to separate the kinematic errors of each axis from the geometric errors. To extract the geometric errors required for the mathematical calculations, an artifact with the special features is proposed and printed. The geometric errors of the characteristic points on the artifact are measured by a coordinate measuring machine (CMM). From the measured geometric errors, kinematic errors of the printer can be identified, and can be further compensated by adjusting the CAD model of the object. Two compensated algorithms are established; one uses the fitted curves of the kinematic errors, and the other uses the average kinematic error values. Printing tests and case studies are performed to verify the effectiveness of the proposed method. The results show that the proposed method can improve printing accuracy of the Cartesian 3D printer. [ABSTRACT FROM AUTHOR]

The underlying complicated spatiotemporal thermo-mechanical processes in additive manufacturing (AM) technology pose challenges in predicting and optimizing the as-built part quality for production use. Physics-based simulations are being developed to provide reliable predictions such as part distortions, residual stresses/strains, microstructure contents, and grain morphology, which can guide the product design and manufacturing process for improved part quality. However, due to the complexity of the problem, e.g., multi-physics and multi-scale, physics-based models need the expertise to build an extensive simulation time preventing its use in real-time monitoring and control. This study proposes an inductive data-driven framework to simulate the part distortion field for additively manufactured components. The part distortion field data were collected from different AM build strategies using finite element (FE) simulations. The FE model consists of multi-layer part distortion fields as outputs constituted by AM process parameters as inputs. The surrogate model consists of two stages: (1) self-organizing map to project the high-dimensional spatial field of the part distortion into a likelihood estimator, (2) hybrid self-organizing methods to predict the extracted features and reconstruct the part distortion field. The data-driven model evaluates different build scenarios' effects on the distortion field for additively manufactured parts. The results correlated well with FE simulations and established a prediction-based compensation strategy to reduce part distortions. It should be noted that while the framework is currently applied to FE simulation data, the integrated data-driven methods can also be deployed on experimentally measured 3D cloud data or other high throughput field measurements. [ABSTRACT FROM AUTHOR]

Piezoelectric materials have the merits of fast response, speed, and high precision. These materials have been widely investigated for functional device applications such as sensors, actuators, microelectromechanical systems (MEMS), nanoelectromechanical systems (NEMS), 3D‐printing etc. This paper offers a review on recent advances in piezoelectric materials and their limitations. In addition, different piezoelectric materials such as lead‐based, lead‐free piezoelectric materials etc. are discussed, and their applications in the catalysis, energy harvesting, sensors, biomedical engineering, and additive manufacturing are addressed. [ABSTRACT FROM AUTHOR]

Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research and development. The DfMA's conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concepts and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures for Dfab, DfAM, and AM assembly processes were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. As Dfab and DFAM are innovated, practical DFMA techniques begin to develop to a great extent. Large research gaps in the DfMA for Dfab and DfAM can be filled in terms of integrating them with product structural performance, management, studied cases, building information modeling (BIM), and machine learning to increase operational efficiency and sustainable practices. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are widely used for design, manufacturing, prognostics, and decision support for operations. One critical challenge in optimizing DTs usually involves multi-fidelity (MF) models and data, such as multi-resolution computational simulation and experimental testing. The MF strategies provide advantages of high accuracy with low computational and experimental cost in DTs. A novel MF optimization framework is proposed in this paper to demonstrate and validate its potential application in DTs. First, MF data aggregation using convolutional neural networks (MDACNN) is introduced to integrate low-fidelity (LF) and high-fidelity (HF) models and data. It can fully utilize the LF data to learn the relationship across multiple fidelities. With MDACNN, predictions can be made with high accuracy compared to HF models. Next, MDACNN is integrated into the neural optimization machine (NOM), an optimization framework based on NNs. NOM is explicitly designed for optimizing NN objective functions based on the stochastic gradient descent method. The integrated model is named MF neural optimization machine (MFNOM). A numerical example is presented to illustrate the procedure of implementing MFNOM. Two engineering applications are presented for both MF simulation models and experimental data. The first problem focuses on the multi-resolution finite element simulation for structures and materials. Coarse and fine meshes are applied for simulation. The properties of multi-phase heterogeneous materials are optimized to minimize the stress in the simulation domain. The second problem investigates the internal defects in additively manufacturing. Low/high resolution and full/partial field scanning data are utilized to build DTs. MFNOM is used to design pore size and orientation to reduce the risk caused by irregular pores. [ABSTRACT FROM AUTHOR]

Today, additive manufacturing (AM) is employed in various sectors, including aerospace, automobiles, healthcare, architecture, toys, arts and design, construction, etc. The AM used a set of technologies like 3D printing, 3D scanning, and designing, scanning, and printing software. 3D printing technologies are used to build 3D objects from computer-aided design models by layering material on top of material until a genuine part is formed. It is anticipated that AM will significantly impact manufacturing and, ultimately, on all of our lives. However, a sustained research effort into AM metrology is required to make the manufacturing revolution a reality. Tolerance and quality control methods must be in place for AM processes, starting with offline metrology and progressing to closed-loop control utilising inline metrology. This paper discusses the various significant capabilities of AM with their metrological aspects. Furthermore, several crucial industrial prospects of AM for measurement systems are discussed briefly. Finally, the paper discusses the significant features of inspection and quality control. A reliable methodology for the metrological assessment of AM parts would be highly advantageous in expanding AM's utility. As a result of the extensive literature evaluation, the current study will assist students, engineers, designers, manufacturers, and metrologists in identifying research gaps and potential for the field's progress. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM, also known as 3D printing) is an advanced manufacturing technique that has enabled progress in the design and fabrication of customised or patient-specific (meta-)biomaterials and biomedical devices (e.g., implants, prosthetics, and orthotics) with complex internal microstructures and tuneable properties. In the past few decades, several design guidelines have been proposed for creating porous lattice structures, particularly for biomedical applications. Meanwhile, the capabilities of AM to fabricate a wide range of biomaterials, including metals and their alloys, polymers, and ceramics, have been exploited, offering unprecedented benefits to medical professionals and patients alike. In this review article, we provide an overview of the design principles that have been developed and used for the AM of biomaterials as well as those dealing with three major categories of biomaterials, i.e., metals (and their alloys), polymers, and ceramics. The design strategies can be categorised as: library-based design, topology optimisation, bio-inspired design, and meta-biomaterials. Recent developments related to the biomedical applications and fabrication methods of AM aimed at enhancing the quality of final 3D-printed biomaterials and improving their physical, mechanical, and biological characteristics are also highlighted. Finally, examples of 3D-printed biomaterials with tuned properties and functionalities are presented. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) is an additive manufacturing process used in manufacturing free form geometries, repair applications, coating and surface modification, and fabrication of functionally graded materials. It is a process in which focused thermal energy is used to fuse materials by melting. Thermal effects can cause distortions and defects on the parts during the DED process, therefore they should be evaluated and taken into account during the manufacturing of products. Melting pool control and DED bead geometries should be defined properly as well. In this work, an Artificial Neural Network model has been applied considering the DED process parameters in order to predict the geometrical patterns and create a local reinforced product as a hybrid manufacturing technology. Although lots of studies are available on topology optimization for manufacturing methods such as casting, extrusion, and powder bed fusion, topology optimization for the DED process is not widely taken into consideration to predict the design geometrical patterns. DOE RSM and ANN approaches were applied in this study to predict convenient dimensions, topology based geometrical patterns of local stiffeners and heat source power optimizing the energy, total mass, and peak force results of the hybrid part. A single bead track deposition is simulated in terms of validation of the numerical heat source model, and cross-sections of the beads are analysed. A cross-member structure is manufactured using the DED device and the structure is correlated under the three point bending physical conditions on test bench. It has been investigated that locally reinforced cross beam has much more energy absorption and peak force values than plain model. The results showed that the proposed NN-GA is a promising approach to generate the topology based geometrical patterns and process parameters which can be used to create a local reinforced product as hybrid manufacturing technologies. [ABSTRACT FROM AUTHOR]

Tolerance allocation methods significantly contribute to the qualification of Additive Manufacturing (AM) for (small-)series production ensuring high performance and efficiency. However, their usage prerequisites the availability of quantitative, reliable information on the impact of the assigned tolerances on the resulting manufacturing costs. The given article proposes a novel methodology for the systematic development of tolerance-cost curves for a cost-efficient tolerancing of 3D-printed parts. The proposed structured workflow aims at serving as a general guideline for both researchers and practitioners, while the exemplarily chosen perspective from Fused Layer Modeling (FLM) illustrates its adaption to a specific AM technology. The indirect, non-apparent interrelations between tolerances and resulting costs are modelled with the aid of an activity-based cost model, whereas the individual costs elements are mapped as function of the values for the machine-specific process parameters for AM, e.g., layer height or printing speed, which are required to achieve the assigned design tolerances. The total procedure covers all relevant steps, viz. the identification and quantification of the single cost items, the design of benchmark artifacts, adapted to given manufacturing and measuring techniques, the empirical determination of data on cost and geometrical accuracy by design of experiments and tolerance-cost curves. Its exemplary application to an academic use case shows its general applicability and benefits, but also its current limitations. [ABSTRACT FROM AUTHOR]

Nowadays, the role of hybridization within the wider manufacturing ecosystem gains significant momentum with multiple commercial solutions already available on the market. Despite the very promising benefits of combining and selectively exploiting the advantages of additive and subtractive technologies on the same machine, hybrid additive manufacturing is far from reaching its full potential. One of the central limitations of existing hybrid process chains is the lack of a harmonized, structured and automated workflows to support an adaptive manufacturing strategy. This work is motivated by the need to bridge this gap and to capture the logic behind an adaptive hybrid process chain with the aim to support the achievement of enhanced product quality and improved operational efficiency in hybrid additive manufacturing. The paper discusses the implementation of a hybrid CAx platform and the underlying methodology aiming at the dynamic reduction of variabilities associated with the laser metal deposition process. The hybrid workflow identifies the most adapted sequence and planning of additive and subtractive operations while considering part inspection as an in-envelope functionality to quantify the geometrical and dimensional part deviations and to trigger the regenerative mechanism. The methodology is demonstrated on a hybrid machine by deploying laser ablation for the in situ removal of build deviations and an adapted deposition operation as part of a regenerative strategy leading to higher part confidence. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present a survey concerning the accuracy of thermoplastic polymeric parts fabricated by additive manufacturing (AM). Based on the scientific literature, the aim is to provide an updated map of trends and gaps in this relevant research field. Several technologies and investigation methods are examined, thus giving an overview and analysis of the growing body of research. Design/methodology/approach: Permutations of keywords, which concern materials, technologies and the accuracy of thermoplastic polymeric parts fabricated by AM, are used for a systematic search in peer-review databases. The selected articles are screened and ranked to identify those that are more relevant. A bibliometric analysis is performed based on investigated materials and applied technologies of published papers. Finally, each paper is categorised and discussed by considering the implemented research methods. Findings: The interest in the accuracy of additively manufactured thermoplastics is increasing. The principal sources of inaccuracies are those shrinkages occurring during part solidification. The analysis of the research methods shows a predominance of empirical approaches. Due to the experimental context, those achievements have consequently limited applicability. Analytical and numerical models, which generally require huge computational costs when applied to complex products, are also numerous and are investigated in detail. Several articles deal with artificial intelligence tools and are gaining more and more attention. Originality/value: The cross-technology survey on the accuracy issue highlights the common critical aspects of thermoplastics transformed by AM. An updated map of the recent research literature is achieved. The analysis shows the advantages and limitations of different research methods in this field, providing an overview of research trends and gaps. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) is one of the widely used metal additive manufacturing techniques. While SLM is able to produce high-quality products, the parameter selection process can be very complicated, especially for magnetic materials in that the iron loss and permeability properties must also be considered, which renders the parameter selecting process more complicated. This research explores the parameter selection process of magnetic material for SLM, which integrates machine and evolutionary algorithms to accurately predict magnetic characteristics, such as iron loss and permeability, and generates suggestions for the process parameters according to practical demands. [ABSTRACT FROM AUTHOR]

The advent of additive manufacturing (AM) has brought about radically new ways of designing and manufacturing of end-use parts and components, by exploiting freedom of design. Due to the unique manufacturing process of AM, both design and process parameters can strongly influence the part properties, thereby enlarging the possible design space. Thus, finding the optimal combination of embodiment design and process parameters can be challenging. A structured and systematic approach is required to effectively search the enlarged design space, to truly exploit the advantages of AM. Due to lowered costs in computing and data collection in the recent years, data-driven strategies have become a viable tool in characterization of process, and researches have starting to exploit data-driven strategies in the design domain. In this paper, a state-of-the-art data-driven design strategy for fused filament fabrication (FFF) is presented. The need for data-driven strategies is explored and discussed from design and process domain, demonstrating the value of such a strategy in designing an FFF part. A comprehensive review of the literature is performed and the research gaps and opportunities are analysed and discussed. The paper concludes with a proposed data-driven framework that addresses the identified research gaps. The proposed framework encompasses knowledge management and concurrent optimization of embodiment design and process parameters to derive optimal FFF part design. Contribution of this paper is twofold: A review of the state-of-the-art is presented, and a framework to achieve optimal FFF part design is proposed. [ABSTRACT FROM AUTHOR]

Featured Application: In this work, a comprehensive review on how data-driven modelling and machine learning for error compensation in additive manufacturing is conferred. Additive Manufacturing (AM) of three-dimensional objects is now being progressively realised with its ad-hoc approach with minimal material wastage (lean manufacturing) being one of its benefit by default. It could also be considered as an evolutional paradigm in the manufacturing industry with its long list of application as of late. Artificial Intelligence is currently finding its usefulness in predictive modelling to provide intelligent, efficient, customisable, high-quality and sustainable-oriented production process. This paper presents a comprehensive survey on commonly used predictive models based on heuristic algorithms and discusses their applications toward making AM "smart". This paper summarises AM's current trend, future opportunity, gaps, and requirements together with recommendations for technology and research for inter-industry collaboration, educational training and technology transfer in the AI perspective in-line with the Industry 4.0 developmental process. Moreover, machine learning algorithms are presented for detecting product defects in the cyber-physical system of additive manufacturing. Based on reviews on various applications, printability with multi-indicators, reduction of design complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of as prospective opportunities for further research. [ABSTRACT FROM AUTHOR]

Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding. In order to make AM suitable for the medical, aerospace, and automotive industries, geometry variations should be controlled and managed with a tight tolerance range. In the previously published article, the authors used statistical analysis to develop linear models for the prediction of dimensional features of laser-sintered specimens. Two identical builds with the same material, process, and build parameters were produced, resulting in 434 samples for mechanical testing (ISO 527-2 1BA). The developed linear models had low accuracy, and therefore needed an application of more advanced data analysis techniques. In this work, machine learning techniques are applied for the same data, and results are compared with the previously reported linear models. The linear regression model is the best for width. Multilayer perceptron and gradient boost regressor models have outperformed other for thickness and length. The recommendations on how the developed models can be used in the future are proposed. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0, additive manufacturing will be widely used to produce customized components. However, it is rather time-consuming and expensive to produce components with sound structure and good mechanical properties using additive manufacturing by a trial-and-error approach. To obtain optimal process conditions, numerous experiments are needed to optimize the process variables within given machines and processes. Digital twins (DT) are defined as a digital representation of a production system or service or just an active unique product characterized by certain properties or conditions. They are the potential solution to assist in overcoming many issues in additive manufacturing, in order to improve part quality and shorten the time to qualify products. The DT system could be very helpful to understand, analyze and improve the product, service system or production. However, the development of genuine DT is still impeded due to lots of factors, such as the lack of a thorough understanding of the DT concept, framework, and development methods. Moreover, the linkage between existing brownfield systems and their data are under development. This paper aims to summarize the current status and issues in DT for additive manufacturing, in order to provide more references for subsequent research on DT systems. [ABSTRACT FROM AUTHOR]

Melt pool dynamics reflect the formulation of microstructural defects in parts during Laser-Based Additive Manufacturing (LBAM). The thermal images of the melt pool collected during the LBAM process provide unique opportunities for modeling and monitoring its evolution. The recognized anomalies are evidence of part defects that are to be eliminated for higher product quality. A unique concern in analyzing thermal images is spatial-temporal correlations – the heat transfer within the melt pool causes spatial correlations among pixels in an image, and the evolution of the melt pool causes temporal correlations across images. The objective of this study is to develop a LBAM modeling-monitoring framework that incorporates spatial-temporal effects in characterizing and monitoring melt pool behavior. Spatial-Temporal Conditional Autoregressive (STCAR) models are explored. STCAR-AR is identified as the best candidate among the numerous STCAR variants. A novel two-level control chart is constructed on top of the STCAR-AR model to monitor the melt pool dynamics. A hierarchical structure underlies the two-level control chart in the sense that global anomalies recognized in Level II can be traced in Level I for further inspection. A comparison with other recently developed in-situ monitoring approaches shows that the proposed framework achieves the best detection power and false positive rate. [ABSTRACT FROM AUTHOR]

Purpose: Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM. Design/methodology/approach: In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework. Findings: The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details. Practical implications: A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm. Originality/value: This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment. [ABSTRACT FROM AUTHOR]

Blockchain combined with 3D printing offers businesses untapped opportunities. Blockchain can help businesses overcome intellectual property and data security barriers, allowing them to take advantage of emerging 3D printing business models. Specifically, blockchain can facilitate local manufacturing and may lay the groundwork for new business models such as secure design marketplaces and shared factories. Businesses could also improve their value proposition by offering additional services around a printed part, improving value delivery, and offering less costly and more customized products that involve fewer risks. Blockchain could transform the way firms create, deliver, and capture value in 3D printing ecosystems. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) is viewed as a disruptive technology with the potential to replace many manufacturing processes. With its rapid proliferation in recent years, it is crucial to understand how to measure this technology's social impacts. There are many studies available in the existing literature regarding the economic and environmental impacts of AM technology. However, research regarding the social impacts of this technology is still scarce. To this end, this research has developed a framework based on Social Life Cycle Assessment (SLCA) methodology to measure AM products' social impacts throughout their life cycle stages. The proposed framework includes an underlying mathematical model that was developed to allow for the aggregation of indicator scores. Additionally, cut-off scores were determined to differentiate between positive and negative social impacts. This framework can be applied to case studies, which provides a final score that quantifies the social impacts of an AM product throughout all its life cycle stages. It also provides scores for each stakeholder category and life cycle stage, which facilitates the identification of hotspots that require attention from organizations. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is an emerging process that has been extremely improved in terms of technology and application in recent years. In this technology review, new industrial improvements in laser powder bed fusion (LPBF) of metals are discussed. LPBF has the lowest build rate among all AM processes that produce metals such as electron beam powder bed fusion, direct energy deposition, binder jetting and sheet lamination. The findings of the current research show that the most innovations and future directions of LPBF printers are toward increasing the speed of the process by using interchangeable feedstock chamber, closed-loop control powder handling, automated powder sieving, multi-layer concurrent printing, 2-axis coating and multi powder hoppers. To increase the speed of the process, the new improvements for transferring time and using fast lasers are presented. Another innovation in the building of LPBF printers is enhancing part quality by improving lasers with the shorter beam diameter, multi-lasers, uniform inert gas flow, accurate positioning systems, using high vacuum systems and using sensors and automation. [ABSTRACT FROM AUTHOR]

Purpose: Metal-based additive manufacturing is a relatively new technology used to fabricate metal objects within an entirely digital workflow. However, only a small number of different metals are proven for this process. This is partly due to the need to find a new set of parameters which can be used to successfully build an object for every new alloy investigated. There are dozens of variables which contribute to a successful set of parameters and process parameter optimisation is currently a manual process which relies on human judgement. Design/methodology/approach: Here, the authors demonstrate the application of machine learning as an alternative method to determine this set of process parameters, the subject of this test is the processing of pure copper in a laser powder bed fusion printer. Data in the form of optical images were collected over the course of traditional parameter optimisation. These images were segmented and fed into a convolutional autoencoder and then clustered to find the clusters which best represented a high-quality result. The clusters were manually scored according to their quality and the results applied to the original set of parameters. Findings: It was found that the machine-learned clustering and subsequent scoring reflected many of the observations which were found in the traditional parameter optimisation process. Originality/value: This exercise, as well as demonstrating the effectiveness of the ML approach, indicates an opportunity to fully automate the approach to process optimisation by applying labels to the data, hence, an approach that could also potentially be suited for on-the-fly process optimisation. [ABSTRACT FROM AUTHOR]

Despite the advantages that additive manufacturing (AM) processes present and the progressive number of activity sectors that they emerge in, their successful adoption is currently hampered by defects in surface finish as well as dimensional and geometrical precision. This results in reduction of their suitability for net shape manufacturing, thereby, requiring the evaluation of their performances. Considering this notion, this paper deals with in-plane deviation modeling of material shrinkage occurring in fused deposition modeling (FDM) process. To achieve this aim, two models were developed, each for circular and squared shapes. The objective was to first understand the deviation behavior, then compare it with actual data extracted from a test part to find out the rates with which the shrinkage would be compensated, and finally based on the obtained results, the CAD file would be modified. The experimental findings showed the effectiveness of the adopted methodology and the ability of developed models to compensate for the deviation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to compare the overall performance of the injection moulding process by using metallic inserts produced by both conventional technologies and selective laser melting (SLM). Design/methodology/approach: A systematic methodology is proposed for prior evaluation of the effectiveness of conformal cooling channels to reduce cycle time and/or to reduce the scrap rate. Findings: The mould was reengineered considering the SLM process and manufactured. Injection trials were carried out to validate expectations provided by injection simulations, which resulted on good quality parts and a significant decrease on cooling time, and, consequently, on the overall cycle time. The minimisation of scrap provided energy savings and time-to-market reduction. Research limitations/implications: The initial costs for AM tools still pose some doubts on decision-makers. The challenge of this study is to implement the methodology on a small-scale production and still ensure that benefits are achieved. Practical implications: The case study selected for this research work is based on a parking sensor housing, which is a plastic part assembled on the vehicle's front and rear bumpers, therefore, with aesthetics concerns. The part produced with the conventional mould exhibits surface defects that, to be minimised (not eliminated), require a longer packing time to diminish the sink marks. Social implications: The economic impact of the use of SLM is relevant despite the low batch size for the case study presented. Energy savings are achieved due to scrap reduction and shorter cycle time. Originality/value: The systematic methodology proposed for prior evaluation of the advantages of conformal cooling is possible to be applied both on small scale and high production series. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to report the state of art review on sustainable additive manufacturing (AM). Design/methodology/approach: AM processes are becoming environmentally conscious and sustainable. In total, 63 papers are being collected from peer-reviewed journal sources. The review is presented on triple bottom line sustainable aspects, namely, environment, economy and society. Findings: In the environmental dimension, papers on energy consumption, design optimization and life cycle assessment aspects are being collected and presented. The key inferences and insights are being derived from the mentioned perspectives and a framework for sustainable AM is proposed. Research limitations/implications: The review is limited to journal articles from the study on sustainable AM. Book chapters are being excluded. Practical implications: The study also discussed various perspectives of sustainable AM and provided insights on sustainable AM to practitioners. Originality/value: Systematic review on sustainable additive manufacturing is the original contribution of the authors. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a numerical approach inspired by Geometrical Product Specifications (GPS) standards for the assessment of geometrical defects appearing during Additive Manufacturing (AM) by Laser Beam Melting (LBM). Design/methodology/approach: The study is based on finite element (FE) simulations of thermal distortions, then an assessment of flatness defects (warping induced by the high-residual stresses appearing during the manufacturing) from the deformed surfaces provided by simulation, and finally the correction of the calculated flatness defects from preliminary comparison between simulated and experimental data. Findings: For an elementary geometrical feature (a wall), it was possible to identify the variation in the flatness defect as a function of the dimensions. For a complex geometry exhibiting a significant flatness defect, it was possible to improve the geometric quality using the numerical tool. Research limitations/implications: To the best of the author's knowledge, this work is the first attempt using a numerical approach inspired by GPS standards to identify variations in thermal distortions caused by LBM, which is an initial step toward optimization. This paper is mainly focused on flatness defect assessment, even though the approach is potentially applicable for all types of geometrical defects (shape, orientation or position defects). Practical implications: The study opens prospects for the optimization of complex parts elaborated using LBM, based on the minimization of the geometric defects caused by thermal distortions. Social implications: The prospects in terms of shape optimization will extend the potential to benefit from the new possibilities offered by LBM additive manufacturing. Originality/value: Unlike the usual approach, the proposed methodology does not require any artifacts or comparisons with the computer-aided-design (CAD) model for geometrical distortion assessment. The present approach opens up the possibility of performing metrology from FE simulation results, which is particularly promising in the AM field. [ABSTRACT FROM AUTHOR]

Purpose 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.Design/methodology/approach 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.Findings 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.Originality/value 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. [ABSTRACT FROM AUTHOR]

Nowadays, the implementation of digital thread for Additive Manufacturing (AM) and product data management system strongly depends on specified file formats and used interoperability standards in order to integrate geometric dimensioning and tolerancing (GD&T) information directly in the 3D CAD model. The consideration of geometric deviations and variations is a key issue for design and tolerancing, inspection and management of manufacturing part information in AM through product and manufacturing information (PMI). PMI has been used to describe GD&T and non-geometric data, such as surface texture, surface finishing requirements, material specifications, process data, and other annotations, based on ISO standards. Standards play an important role in enabling the interoperability and efficiency of AM systems through the development of AM standard formats. This paper reviews current challenges of geometric and tolerancing model and formats for AM processes, which largely impede the advancement of AM technologies. Two criteria to enhance AM geometric and tolerancing model and standards are presented in this paper: GD&T management and compliance with PMI. STL, AMF and STEP formats are discussed on their characteristics of product definition and manufacturing specification for AM. The review presented here highlights that STEP standard can be a good basis for future research work to integrate and standardise material information, geometric and tolerancing model, and process planning for AM. Finally, new specifications of STEP-AM format are proposed in this work. [ABSTRACT FROM AUTHOR]

Composites of biodegradable phosphate glass fiber and polylactic acid (PGF/PLA) show potential for bone tissue engineering scaffolds, due to their ability to release Ca, P, and Mg during degradation, thus promoting the bone repair. Nevertheless, glass degradation tends to acidify the surrounding aqueous environment, which may adversely affect the viability and bone-forming activities of osteoblasts. In this work, MgO was investigated as a neutralizing agent. Porous network-phase gyroid scaffolds were additive-manufactured using four different materials: PLA, MgO/PLA, PGF/PLA, and (MgO + PGF)/PLA. The addition of PGF enhanced compressive properties of scaffolds, and the resultant scaffolds were comparably strong and stiff with human trabecular bone. While the degradation of PGF/PLA composite induced considerable acidity in degradation media and intensified the degradation of PGF in return, the degradation media of (MgO + PGF)/PLA maintained a neutral pH close to a physiological environment. The experiment results indicated the possible mechanism of MgO as the neutralizing agent: the local acidity was buffered as the MgO reacted with the acidic degradation products thereby inhibiting the degradation of PGF from being intensified in an acidic environment. The (MgO + PGF)/PLA composite scaffold appears to be a candidate for bone tissue engineering. [ABSTRACT FROM AUTHOR]

Additive manufacturing has the potential to make a longstanding impact on the manufacturing world and is a core element of the Fourth Industrial Revolution. Additive manufacturing signifies a new disruptive path on how we will produce parts and products. Several studies suggest this technology could foster sustainability into manufacturing systems based on its potential of optimizing material consumption, creating new shapes, customizing designs and shortening production times that, all combined, will greatly transform some of the existing business models. Although it requires reaching a certain level of design maturity to completely insert this technology in an industrial setting, additive manufacturing has the potential to favorably impact the manufacturing sector by reducing costs in production, logistics, inventories, and in the development and industrialization of a new product. The transformation of the industry and the acceleration of the adopting rate of new technologies is driving organizational strategy. Thus, through the lenses of Industry 4.0 and its technological concepts, this paper aims to contribute to the knowledge about the impacts of additive manufacturing technology on sustainable business models. This aim is accomplished through a proposed framework, as well as the models and scales that can be used to determine these impacts. The effects are assessed by taking into account the social, environmental and economic impacts of additive manufacturing on business models and for all these three dimensions a balanced scorecard structure is proposed. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is the process of joining materials layer by layer to fabricate products based on 3D models. Due to the layer-by-layer nature of AM, parts with complex geometries, integrated assemblies, customized geometry or multifunctional designs can now be manufactured more easily than traditional subtractive manufacturing. Path planning in AM is an important step in the process of manufacturing products. The final fabricated qualities, properties, etc., will be different when using different path strategies, even using the same AM machine and process parameters. Currently, increasing research studies have been published on path planning strategies with different aims. Due to the rapid development of path planning in AM and various newly proposed strategies, there is a lack of comprehensive reviews on this topic. Therefore, this paper gives a comprehensive understanding of the current status and challenges of AM path planning. This paper reviews and discusses path planning strategies in three categories: improving printed qualities, saving materials/time and achieving objective printed properties. The main findings of this review include: new path planning strategies can be developed by combining some of the strategies in literature with better performance; a path planning platform can be developed to help select the most suitable path planning strategy with required properties; research on path planning considering energy consumption can be carried out in the future; a benchmark model for testing the performance of path planning strategies can be designed; the trade-off among different fabricated properties can be considered as a factor in future path planning design processes; and lastly, machine learning can be a powerful tool to further improve path planning strategies in the future. [ABSTRACT FROM AUTHOR]

Today, the current trends of manufacturing are towards the adaptation and implementation of smart manufacturing, which is a new initiative to turn the traditional factories into profitable innovation facilities. However, the concept and technologies are still in a state of infancy, since many manufacturers around the world are not fully knowledgeable about the benefits of smart manufacturing compared to their current practices. This article reviews several aspects of smart manufacturing and introduces its advantages in terms of energy-saving and production efficiency. This article also points out that some areas need further research so that smart manufacturing can be shaped better. [ABSTRACT FROM AUTHOR]

The fabrication of porous materials for tissue engineering applications in a straightforward manner is still a current challenge. Herein, by combining the advantages of two conventional methodologies with additive manufacturing, well-defined objects with internal and external porosity were produced. First of all, multi-material fused deposition modeling (FDM) allowed us to prepare structures combining poly (ε-caprolactone) (PCL) and poly (lactic acid) (PLA), thus enabling to finely tune the final mechanical properties of the printed part with modulus and strain at break varying from values observed for pure PCL (modulus 200 MPa, strain at break 1700%) and PLA (modulus 1.2 GPa and strain at break 5–7%). More interestingly, supercritical CO2 (SCCO2) as well as the breath figures mechanism (BFs) were additionally employed to produce internal (pore diameters 80–300 µm) and external pores (with sizes ranging between 2 and 12 μm) exclusively in those areas where PCL is present. This strategy will offer unique possibilities to fabricate intricate structures combining the advantages of additive manufacturing (AM) in terms of flexibility and versatility and those provided by the SCCO2 and BFs to finely tune the formation of porous structures. [ABSTRACT FROM AUTHOR]

The design of additive manufacturing processes, especially for batch production in industrial practice, is of high importance for the propagation of new additive manufacturing technology. Manual redesign procedures of the additive manufactured parts based on discrete measurement data or numerical meshes are error prone and hardly automatable. To achieve the required final accuracy of the parts, often, various iterations are necessary. To address these issues, a data-driven geometrical compensation approach is proposed that adapts concepts from forming technology. The measurement information of a first calibration cycle of manufactured parts is the basis of the approach. Through non-rigid transformations of the part geometry, a new shape for the subsequent additive manufacturing process was derived in a systematic way. Based on a purely geometrical approach, the systematic portion of part deviations can be compensated. The proposed concept is presented first and was applied to a sample fin-shaped part. The deviation data of three manufacturing cycles was utilised for validation and verification. [ABSTRACT FROM AUTHOR]

In order to compete with traditional manufacturing processes, Additive Manufacturing (AM) should be capable of producing medium to large batches at industrial-degree quality and competitive cost-per-unit. This paper proposes a systematic framework approach to the problem of fulfilling dimensional and geometric requirements for medium batch sizes of AM parts, which has been structured as a three-step optimization methodology. Firstly, specific work characteristics are analyzed so that information is arranged according to an Operation Space (factors that could have an influence upon quality) and a Verification Space (formed by quality indicators and requirements). Standard process configuration leads to characterization of the standard achievable quality. Secondly, controllable factors are analyzed to determine their relative influence upon quality indicators and the optimal process configuration. Thirdly, optimization of part dimensional and/or geometric definition at the design level is performed in order to improve part quality and meet quality requirements. To evaluate the usefulness of the proposed framework under quasi-industrial condition, a case study is presented here which is focused on the dimensional and geometric optimization of surgical-steel tibia resection guides manufactured by Laser-Power Bed Fusion (L-PBF). The results show that the proposed approach allows for part quality improvement to a degree that matches the initial requirements. [ABSTRACT FROM AUTHOR]