Additive manufacturing: scientific and technological challenges, market uptake and opportunities

Additive manufacturing technology overcomes the limitations imposed by traditional manufacturing techniques, such as fixtures, tools, and molds, thereby enabling a high degree of design freedom for parts and attracting significant attention. Combined with subtractive manufacturing technology, additive and subtractive hybrid manufacturing (ASHM) has the potential to enhance surface quality and machining accuracy. This paper proposes a method for simulating the additive and subtractive manufacturing process, enabling accurate deformation prediction during processing. The relationship between stress distribution and thermal stress deformation of thin-walled 316L stainless steel parts prepared by Laser Metal Deposition (LMD) was investigated using linear scanning with a laser displacement sensor and finite element simulation. The changes in stress and deformation of these thin-walled parts after milling were also examined. Firstly, 316L stainless steel box-shaped thin-walled parts were fabricated using additive manufacturing, and the profile information was measured using a Micro Laser Displacement Sensor. Then, finite element software was employed to simulate the stress and deformation of the box-shaped thin-walled part during the additive manufacturing process. The experiments mentioned were conducted to validate the finite element model. Finally, based on the simulation of the box-shaped part, a simulation prediction was made for the box-shaped thin-walled parts produced by two-stage additive and subtractive manufacturing. The results show that the deformation tendency of outward twisting and expanding occurs in the additive process to the box-shaped thin-walled part, and the deformation increases gradually with the increase of the height. Meanwhile, the milling process is significant for improving the surface quality and dimensional accuracy of the additive parts. The research process and results of the thesis have laid the foundation for further research on the influence of subtractive process parameters on the surface quality of 316L stainless steel additive parts and subsequent additive and subtractive hybrid manufacturing of complex parts.

The Manufacturing Automation course in the Mechanical Engineering program at the University of Connecticut (UConn) was one of the most popular courses in the ME curriculum. The students’ benefits from the course were already described in the companion paper [1]. In this paper the advantages of prototyping and part production through Subtractive Manufacturing (SM) and Additive Manufacturing (AM) are described. The paper discusses parts fabrication done as subtractive and additive manufacturing operations. This was done in the scope of the UConn Engineering i.e. in the ME and MEM programs where Manufacturing Automation and Senior Design courses are taught. Such operations were possible thanks to the equipment available at UConn School of Engineering and thanks to the cooperation with the creator of the Mastercam software - CNC Software Inc and aircraft engines and equipment manufacturer - Pratt & Whitney of East Hartford. The integration of design and manufacturing in the course was done through putting together the operations of conceptual design, geometric design and modeling of the parts designed during the course. The models of parts done by AM were created using 3D printing in ME Laboratory out of acrylonitrile butadiene styrene and different kinds of plastic and in PW/UConn laboratory using laser and electron beam AM machines. To demonstrate further integration of design and machining automation, the students were introduced to complicated problems of surfaces crossing, connections of surfaces and edges of cross sections of the tops and valleys. Thanks to the support and cooperation of the CNC Software, Inc., it was possible to show the students how to cut complicated surfaces on different computer numerically controlled (CNC) machines that ranged from three to nine degrees of freedom specifically designed for accurate and repeatable metal working. In addition, the additive manufacturing (AM) capabilities were introduced in the course thanks to the support of Pratt & Whitney/UConn Additive Manufacturing Laboratory located on the UConn campus. The AM machines are Arcam and laser machines that use electron and laser beams to meld titanium powder. The fabricated parts of high strengths are useful as rapid prototypes or in some cases as substitution parts in an existing mechanical systems. Thanks to the UConn Engineering program and support of the corporations: CNC Software, Inc. and P&W, students were introduced to the spectrum of modern Rapid Prototyping and part sintering operations going through subtractive and additive manufacturing. The process details of the theory, practice of operations, and recommendation for use of the technologies discussed above, as well as possibilities of further applications, are described in this paper. After learning the fundamentals of these processes, students are prepared to design and analyze parts as well as the process required for different machining capabilities. Methods to introduce students to the concepts of using laser and electron beams AM machine as well the prototype machining are described in the paper. Conclusions recommending the teaching methods of product SM and AM machining concepts and lessons learned are also pointed out.

Statement of problemDetermining the effect of additive and subtractive manufacturing on marginal and internal fit in restorations with more than one unit is important, which are less discussed in the literature.PurposeThis in vitro study aims to evaluate the effect of subtractive and additive manufacturing on the marginal and internal fit of provisional fixed dental prostheses (FDPs) using Micro-CT.Materials and methodsModel teeth were prepared with 2 mm occlusal, 1.5 mm axial reduction, and chamfer finish line. After digital impressions with laboratory scanner, provisional crown and bridge restorations were designed with Exo-CAD software. All restorations were produced using 2 different production methods (n = 10). Marginal and internal fit of restorations were evaluated micrometrically and volumetrically with Micro-CT. Shapiro Wilk normality test and Levene test for variance homogeneity were used for data distributions. The Independent Sample T Test was used for normally distributed data, and the Mann Whitney-U Test was used for those not normally distributed to compare the independent groups. The two-way ANOVA test was used to examine the difference between normally distributed independent groups with the interaction effect, and the Scheirer Ray Hare test was used for non-normally distributed groups. Post-hoc Corrected Bonferroni tests were performed to reveal the group or groups that made the difference (α = 0.05).ResultsThe fabrication method affected marginal and internal fit for both resin crowns and bridges (α = 0.05). The additive method showed a better fit for crowns than the subtractive method. For bridges, it was found that a better fit was generally achieved with the subtractive method.ConclusionsIn both crown and bridge restorations, the marginal fit was higher than the internal fit in both production techniques. The additive technique provided a better fit for crown restoration, and the subtractive technique provided a better fit for bridge restoration. The production method significantly affected marginal fit.Clinical significanceWhile almost any type of restoration can be produced with subtractive manufacturing, additive manufacturing is mostly seen in single-unit treatments. Predicting that additive manufacturing will also achieve this goal is not difficult. Hence, examining additive manufacturing in study protocols where the units are increased is important.Graphical

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

3D Printing and Industrial Ecology

Comparison of fracture strength of implant-supported immediate restorations with different polymeric structures fabricated by additive and subtractive manufacturing.

Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.

Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.

Toward Rapid Manufacturability Analysis Tools for Engineering Design Education

Dimensional accuracy and internal fit of anterior 3-unit fixed dental prostheses fabricated by additive and subtractive manufacturing using polymer-based and ceramic restorative materials.

Objective To assess the marginal and internal fit of crowns manufactured by additive and subtractive manufacturing technique. Materials and Methods Twenty extracted teeth prepared for complete coverage crowns were scanned with an intra-oral scanner (Omnicam, DentsplySirona). For the subtractive manufacturing (SM) group, ten crowns were manufactured in a hybrid resin block (Vita Enamic, Vita Zahnfabrik). For the additive manufacturing (AM) group, the crowns were manufactured in a hybrid resin material (NextDent C&B, 3D systems). The design parameters were identical for the two groups. The marginal and internal fit (determined at the axial wall, the cusp tip and occlusally) was assessed before cementation with the replica technique and after cementation under stereomicroscope after sectioning of the crowned teeth. Results For the SM group, the marginal fit was 91 µm (±28 µm) before cementation and 85 µm (±18 µm) after cementation. In the AM group, the marginal fit was 75 µm (±29 µm) before cementation and 71 µm (±18 µm) after cementation. The differences were not statistically significant. As regards the internal fit, the fit at the axial wall was statistically significantly better in the SM group than in the AM group (p=.009 before cementation and .03 after cementation). Occlusally the fit in the AM group was significantly better than in the SM group after cementation (p<.001). Conclusion Within the limitations of the current study, the marginal fit of additively manufactured crowns is comparable to crowns manufactured with chair-side subtractive technique and within the clinically acceptable range. As regards the internal fit no one technique was consistently superior.

PurposeThis study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.Design/methodology/approachThis study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.FindingsA comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.Originality/valueBased on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

This study was aimed at preparing zirconia samples via additive manufacturing (AM) and subtractive manufacturing (SM) and testing the following aspects: (1) the manufacturing accuracy of the zirconia samples and (2) the bond strength of porcelain to zirconia to evaluate the applicability of the zirconia fabricated by AM in dental clinics. We used three milling machines for SM (AR, K5, and UP) and a 3D printer for AM (AO). The manufacturing accuracy of the zirconia specimen in the internal and marginal areas was evaluated by superimposing techniques to calculate the root mean square (RMS) values. The bond strengths of porcelain to zirconia prepared via SM and AM were measured using a universal testing machine. The internal and marginal RMS values of the zirconia prepared by AM (AO) were within the range of those of the zirconia prepared by SM (AR, K5, and UP). Moreover, the bond strength value of the zirconia prepared by AM (35.12 ± 4.09 MPa) was significantly higher than that of the zirconia prepared by SM (30.26 ± 5.20 MPa). Therefore, AM technology has significant potential for applications in dentistry.

Introduction: Ceramic based prosthetic materials for dental restorations have been playing an important role in the dentistry industry, due to their mechanic and aesthetic properties, chemical stability and biocompatibility. Additive manufacturing (AM) is an emerging alternative to the conventional subtractive techniques (SM), to obtain dental pieces, since it is able to produce customized complex 3D pieces at low cost, decreasing the material’ waste and the production time. In particular, robocasting (RC), which is an extrusion filament-based 3D printing process, seems promising for this purpose. However, concerns regarding the material’s intrinsic final properties may arise. The aim of this study is to evaluate the potential of the robocasting technique to produce reliable zirconia dental structures, comparing several properties of the samples produced. Materials and methods: Yttria stabilized-Zirconia (5 wt% Y2O3) pieces were produced by subtractive manufacturing (SM) and robocasting (AM). Samples were sintered at 1500 °C and polished. Density and porosity were determined by the Archimedes method and by optical microscopy, respectively. The topography/morphology was analysed by atomic force microscopy. Wettability measurements with water were performed by the sessile drop method. Vickers hardness was also determined with an applied load of 98 N. In average, 10 measurements were done for each technique. Results: The obtained results are summarized in Table 1. AFM images are shown in Figure 1. Figure 1. AFM topographical images of SM and AM samples Discussion and conclusions: Contrarily to SM samples that present a density very similar to the theoretical value, AM samples present a lower value (95.5%). This explains the higher porosity observed for AM samples. Although the surface roughness of the samples is similar, different values were found for wettability. Nevertheless, both are hydrophilic. Finally, AM samples hardness is significantly lower than that found for SM samples. In conclusion, there are still some challenges to overcome to produce AM samples for dental applications, in particular relatively to material’s densification. The samples’ porosity may compromise the materials’ mechanical properties, like toughness.

Additive manufacturing (AM) has gained notoriety for offering advantages over traditional manufacturing methods, such as increased design complexity and flexibility. However, it has not found widespread use beyond rapid prototyping. One hindrance to the acceptance of AM processes in industry is the time and cost of fabrication per component. While metal AM by itself can be inexpensive, extra manufacturing steps in the form of subtractive manufacturing (SM) may need to be performed to reach final part tolerances, leading to hybrid additive-subtractive manufacturing (HASM) of a part, which increases time and cost. A potential area to reduce cost is through increasing the efficiency of the HASM process by conducting additive and subtractive manufacturing simultaneously. Usually, HASM is performed in a process where AM is completed in one machine or cell and transferred to another machine or cell for SM in a sequential assembly line process. This efficiency decreases part cost, but high aspect ratio parts or parts with internal geometry that require interleaved additive deposition and machining cannot be produced. One unexplored solution to simultaneous HASM that allows for interleaved operations is to operate the deposition head and machining spindle concurrently within the same machine envelope, known as concurrent HASM (CHASM). In this type of process, both AM and SM occur simultaneously on a batch of small parts or a single large part, maintaining a high efficiency without sacrificing the full range of complex geometries that AM allows for. A potential approach to the single-machine method could be to combine a robot and mill within the same envelope. A challenge to this approach, however, is control of both systems. Most machine controllers have limited external communication or, if a robot has been integrated, only offer movement of either the robot or mill at any given time. As a result, systems must pause either the AM or SM process to switch between them rather than working simultaneously. The present work investigates the positional accuracy of such a CHASM system comprised of a robotic arm and a 3-axis mill. Open-loop tests with limited communication between machines are performed on the system to verify positional error during concurrent robot-mill movements. Under certain conditions, it is demonstrated that position error can stay within 2 mm for the duration of a single layer; however, these tests show that, generally, the open-loop positioning performance of the system is inadequate for CHASM without part-specific hand-tuning of parameters. Based on these results, a set of requirements for successful robot-CNC CHASM is proposed for future integrations.