This study aims to investigate issues of quality and quality control (QC) in three-dimensional (3D) printing by reviewing past work and current practices. Possible future developments are also discussed.
After a discussion of the major quality dimensions of 3D-printed objects, the applications of some QC techniques at various stages of the product life cycle (including product design, process planning, incoming QC, in-process QC and outgoing QC) are introduced.
The application of QC techniques to 3D printing is not uncommon. Some techniques (e.g. cause-and-effect analysis) have been applied extensively; others, such as design of experiments, have not been used accurately and completely and therefore cannot optimize quality. Taguchi’s method and control charts can enhance the quality of 3D-printed objects; however, these techniques require repetitive experimentation, which may not fit the work flow of 3D printing.
Because quality issues may discourage customers from buying 3D-printed products, enhancing 3D printing quality is imperative. In addition, 3D printing can be used to manufacture diverse products with a reduced investment in machines, tools, assembly and materials. Production economics issues can be addressed by successfully implementing QC.
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The quality of a product or process has several dimensions: performance, reliability, durability, serviceability, esthetics, features, perceived quality and conformance to specifications or standards (Montgomery, 2008). According to the American Society for Quality (2016), quality control (QC) includes the observation techniques and activities used to fulfill quality requirements. The seven basic tools of QC are cause-and-effect diagrams, check sheets, control charts, histograms, Pareto charts, scatter diagrams and design of experiments (DOE) (Montgomery, 2008). QC activities can also be classified into incoming QC (IQC), in-process QC (IPQC) and outgoing QC (OQC) or quality assurance, depending on when these activities are performed (Orient Semiconductor Electronics, 2016).
3D printing involves building a 3D object from a 3D model layer by layer with resin or other materials (Berman, 2012). 3D printing has been used to fabricate prototypes, mockups, replacement parts, dental crowns, artificial limbs and even bridges (Berman, 2012). With these successes, 3D printing is considered a convenient tool for producing complex internal and external porous structures (Asadi-Eydivand et al., 2016). 3D printing is also cheaper and more efficient than other rapid prototyping technologies, such as selective laser sintering (SLS) and stereolithography (Silva et al., 2008). As an extension, 3D printing has been incorporated into a convenience store chain to form a ubiquitous manufacturing network (Lin and Chen, 2017).
From a manufacturing perspective, 3D printing is a special manufacturing process in which there is no time gap between the research and development (R&D) stage and the mass production stage. In conventional manufacturing processes, a time gap is usually required for amassing the factory capacity and acquiring the raw materials.
This study investigated issues of quality and QC in 3D printing by reviewing past work and current practices. In this paper, the current practices are mapped to the stages of a QC cycle (i.e. product design, process planning, IQC, IPQC and OQC), and quality and QC activities that were either ignored or not actively performed in 3D printing are listed (Figure 1). Finally, this paper discusses possible future developments.
1.2 Importance of the topic
A drug made through 3D printing was approved by the FDA in 2015 (Norman et al., 2016), which not only revolutionizes pharmaceutical manufacturing but also poses a challenge for 3D printing because of the high quality requirements for making drugs. Bose et al. (2013) considered product quality a major limitation in applying 3D printing to biomedical applications. Wittbrodt et al. (2013) asserted that the quality of 3D-printed objects is a critical factor in the viability of widespread applications of low-cost 3D printing.
The ultimate target of QC is to manufacture products economically by eliminating defects and waste. Mironov et al. (2011) asserted that an automated QC system is essential to the success of a 3D printing system for organ biofabrication. 3D printing has been recognized as an effective means for economical manufacturing through reducing the investment in machines, tools, assembly and materials for diversified products (Weller et al., 2015). The production economics issues of 3D printing can be addressed by successfully implementing QC. Because quality issues may discourage customers from buying products manufactured through 3D printing, creating pressure to enhance the quality of 3D printing through QC activities, QC is a major concern for 3D printing (Weller et al., 2015). A QC program has been launched to guarantee that lenses manufactured through 3D printing meet both industry and user requirements (Shang, 2016).
1.3 Problems with existing methods
According to Silva et al. (2008), quality and QC issues in 3D printing or other rapid prototyping systems have not been sufficiently addressed. In particular, quality and QC standards for 3D printing lack a clear definition (Berman, 2012).
2. Quality of three-dimensional-printed objects
The traditional quality dimensions or attributes are not equally emphasized in 3D printing, but three of them are critical to 3D-printed objects: esthetics, conformance to specifications and performance. These are discussed in the following sections.
A 3D-printed object usually has a rough surface finish, and the coloration may not be as clear as expected, causing it to fail to meet esthetic requirements. The resolution of a 3D printer, or the layer thickness it can achieve, is a limiting factor in this regard (Zavorotnitsienko, 2015). To solve this problem, Lanzetta and Sachs (2003) used a bimodal powder (i.e. a mix of two types of powder) such that the finer powder appeared on the upper surface of a 3D object, resulting in a finer surface finish. The choice of an appropriate orientation can also help to improve the surface finish of a 3D object (Campbell et al., 2002; Alfieri et al., 2017). In addition, various power densities can be tested, and the one contributing to the most esthetically pleasing surface finish can be chosen (German, 1992); this requires some DOE techniques. The type of printhead, such as the drop-on-demand type or the continuous jet type (Lanzetta and Sachs, 2003), and the logic behind the slicing program (Herrmann et al., 2014) also influence the surface finish of a 3D-printed object. Otherwise, postprocessing treatments are required to polish the surface (Shang, 2016). For example, Alfieri et al. (2017) proposed a laser processing approach for reducing surface roughness that incorporated scanning optics and beam wobbling and used it to postprocess metal parts fabricated through the selective laser melting of stainless steel. A recent review of pre-processing and post-processing techniques applied to enhance the surface finish of a 3D-printed object refers to Chohan and Singh (2017).
2.2 Yield and conformance to specifications
Conformance to specifications is a quality attribute emphasized in many 3D printing applications. If an object is 3D scanned and then duplicated by a 3D printer, the shape of the 3D-printed object should be as close as possible to the original. For example, in diagnosis and treatment planning, 3D biomedical models must be accurately reproduced to be usable (Silva et al., 2008). Because the 3D model of a product can easily be shared via the internet, in theory, the product can be manufactured very similarly anywhere if the same printer, material and printing conditions are used. However, some studies have still noted limited reproducibility of products created using 3D printing (Weller et al., 2015).
An extended concept of conformance to specifications is yield, which is the percentage of jobs that are successful after production. A perfect yield (100 per cent) is required for human organ biofabrication by using 3D printing (Mironov et al., 2011). According to Weller et al. (2015), because 3D printing can prevent mistakes by eliminating many manual operations, it increases yield. However, each product printed with a 3D printer may be considerably different, which is not conducive to the accumulation of knowledge about products. Consequently, the results of yield calculations can fluctuate widely, and 3D printing may not have the same learning process as a volume production case (Chen and Wang, 1999). Nevertheless, knowledge about the use of a 3D printer and the control of 3D printing processes can be accumulated. According to Grieser (2015), the learning curve for yield in 3D printing is steep, meaning that such knowledge or experience is easy to obtain.
The performance of a 3D-printed object depends on its purpose. If it is only a prototype, it may not have all of the required functions, but otherwise, its performance should be comparable to that of the same product fabricated using traditional manufacturing technologies. For example, a product traditionally created through molding but now created through 3D printing is expected to have features such as a high impact resistance and Young’s modulus (Hopkinson and Dickens, 2001). A mechanical part, such as a build tray, should have a high tensile strength and modulus, even if it is built through 3D printing (Barclift and Williams, 2012). Calì et al. (2012) decomposed a 3D object into joints, and after the assembly of these joints, the 3D object was posable and would not fall. Lenses constructed through 3D printing should achieve high transparency and surface smoothness (Shang, 2016).
Various QC programs have been launched to guarantee that products manufactured using 3D printing meet industrial and user requirements (Shang, 2016). However, some researchers have asserted that a product fabricated using 3D printing is usually not comparable in functionality to its counterparts produced using traditional manufacturing technologies (Hopkinson and Dickens, 2001). By contrast, other researchers have noted that 3D printing provides opportunities for further improving the quality of a product manufactured using traditional technologies. For example, aircraft parts constructed through 3D printing are expected to lighten an aircraft by 50 per cent (Young, 2015) because of the improved precision in forming parts and the elimination of assembly operations.
According to Mironov et al. (2011), focusing on a single performance (or functionality) area of a 3D object can prevent failures in bioprinting human organs and the subsequent downtime. Nevertheless, more functionalities and more flexible functionalities are always being pursued.
3. Quality control in a three-dimensional printing process
QC is a challenging task for 3D printing, for the following reasons:
Although some applications of 3D printing for mass production exist, 3D printing is mostly used for prototyping during R&D, for which the volume of production is low and previous experience cannot be consulted.
QC issues vary with the materials used or products to be printed. For example, warping is a serious problem in products with elongated or rectangular shapes, but not for products with vertical structures. For this issue, heating the print table in advance may be helpful (Herrmann et al., 2014).
The development of 3D printing technologies is still underway, meaning that there are multiple alternatives without an absolute rule for choosing among them.
Nevertheless, according to Norman et al. (2016), an understanding of products and processes facilitates the development of a QC strategy for different 3D printing methods.
Grieser (2015) identifies ten actions that can improve the quality of a 3D-printed object:
setting up the 3D printer according to the equipment vendor’s instructions;
regularly updating the printer’s hardware and software;
periodically maintaining and calibrating the printer;
cleaning the printbed before every printing;
leveling the printbed before every printing;
adjusting the distance between the printhead and the printbed;
printing only objects with moderate size and complexity;
choosing a filament with sufficient adhesion;
terminating the printing process when the results of the first few layers are poor; and
This advice, if not properly followed, can cause defects in a 3D-printed object (Figure 2).
4. Applications of quality control techniques to three-dimensional printing
The following subsections discuss the applications of some QC techniques to 3D printing. These applications are classified according to the stages of the product life cycle (including product design, process planning, IQC, IPQC and OQC) to which QC techniques can apply.
4.1 Product design
The quality of a 3D-printed object depends on the quality of the initial 3D model of the object; therefore, the image acquisition step is essential to the quality of a 3D-printed object (Rengier et al., 2010). For this step to be successful, the spatial resolution of the imaging system, or voxels, must be sufficiently high (e.g. >400 μm for medical applications) (Rengier et al., 2010). However, no clear guidelines have been established for determining the required spatial resolution. Furthermore, some factors influencing the quality of a 3D-printed object must be determined at this stage, such as the slice thickness. Rengier et al. (2010) recommended a slice thickness of less than 1 mm for medical applications.
Considerable effort has been devoted to building .STL/.OBJ-compliant 3D models from existing 3D databases such as medical image databases (Rengier et al., 2010) and anthropometry databases (Straub et al., 2015). The effectiveness of the used algorithm strongly influences the quality of the 3D model. However, designing an effective algorithm is difficult. Furthermore, many existing 3D databases are heterogeneous, meaning that a dedicated algorithm must be designed for each database.
4.2 Quality control tools for process planning
Several QC tools, such as expert systems, classification and regression trees (CARTs) and decision trees, can be applied to facilitate the planning of the 3D printing process.
Compared with control charts, expert systems may be more effective and practical for process planning in 3D printing. As increasingly more products are printed, a user accumulates knowledge about how to optimize the printing conditions for various products. This knowledge can be subjectively expressed by the user or objectively mined using tools such as CARTs (Wu and Chen, 2015). The extracted knowledge can be stored in a knowledge base on which an expert system can be built or illustrated with CARTs or decision trees. Figure 3 depicts an example of such a decision tree. However, neither expert systems nor decision trees have been widely applied to 3D printing, and most relevant studies have only reported on the gained knowledge and experience.
Segmentation of the 3D model is an important step in the process planning stage (Jacobs et al., 2008). Therefore, the effectiveness of the segmentation algorithm critically influences the quality of the printed component. Common segmentation algorithms include simple region growing, surface/volume rendering, maximal/minimal intensity projection and multiplanar reformation (Rengier et al., 2010). However, low-resolution and nonenhanced 3D models require more enhanced segmentation algorithms (Rengier et al., 2010).
4.3 Design of experiments and Taguchi’s method
DOE, a basic tool in quality engineering, can be applied to optimize the settings of a 3D printer and other factors in a 3D printing process, especially when there are interactions among the factors. However, DOE is rarely accurately and completely applied; instead, the settings of a 3D printer are often determined subjectively, for example, according to the limited experience of the user (Herrmann et al., 2014). A possible reason is the high number of factors to consider. For example, to fabricate scaffolds with 3D printing, the values of at least six major factors (powder packing density, powder flowability, layer thickness, binder drop volume, binder saturation and powder wettability) must be set to optimize the quality of a printed scaffold, but to discover the appropriate values requires repetitive and time-consuming experimentation (Bose et al., 2013). To overcome this problem, Taguchi’s orthogonal arrays can limit the replications of experiments to cover a wide range for each factor (Yang and El-Haik, 2008). For example, for three factors (e.g. powder packing density, powder flowability and layer thickness), each with two levels, in theory, eight (23) replications are required to consider all possible combinations to optimize the performance; however, the L4 orthogonal array (Table I) can be used to achieve the same goal with only four replications.
When each factor has three levels, central composite design can be used to build a quadratic model for the response variable, thereby eliminating the need to conduct a complete three-level factorial experiment (Mohammad et al., 2017).
Some examples of applying DOE and Taguchi’s method to 3D printing are reviewed as follows. To analyze the effects of three factors on the mechanical properties of a 3D-printed photopolymer part, Barclift and Williams (2012) performed a three-factor, two-level full factorial DOE that included eight experiment runs. Hsiao (2015) designed an experiment of nine runs according to the L9 (34) orthogonal array to investigate the effects of four control factors (including nozzle height, printing speed and ultraviolet light exposure time), each with three levels, on two qualities (variation in the droplet diameter and the maximum coverage) of a product fabricated using a photocurable printing system. The results along the two quality dimensions were aggregated using the gray relational analysis method (Kuo et al., 2008). The values of the control factors giving the best gray relational grade were chosen.
4.4 Incoming quality control
An important goal of IQC is to ascertain the suitability of an incoming material for the other materials or subassemblies. However, in 3D printing, various products are commonly created from a single type of material. In addition, the whole manufacturing process of a product can be completed by a 3D printer, eliminating subassembly. An incoming material is not checked but simply replaced if it is unfit for use. These properties eliminate the need to conduct extensive IQC in 3D printing, although the choice of material remains critical to the quality of a 3D-printed object (Lanzetta and Sachs, 2003). IQC is generally a simple task in 3D printing because the inputs to a 3D printer are raw materials. However, Hopkinson and Dickens (2001) noted a contamination problem caused by manually scooping the unused materials in a 3D printing process back into the feed tray.
4.5 In-process quality control
IPQC is a QC activity that is critical but may be overlooked (Manufacturing Behavioral Science, 2010). IPQC is based on the premise that some signals detected during the manufacturing process can be related to defects in finished goods. As Grieser (2015) mentioned, it is important to terminate a 3D printing process early if the first few layers are not satisfactory. This IPQC task can be fulfilled if a 3D printing process is closely and continuously monitored either manually or automatically. However, IPQC is a challenging task for 3D printing because of the considerable variations between the products manufactured by a 3D printer. For this reason, visual inspection is a common IPQC practice for 3D printing. Nevertheless, sensors can be installed to facilitate automatic and quick measurement and inspection of the printing results (Mendibil et al., 2016).
4.6 Control charts
Control charts, a crucial tool in statistical QC, can be classified as control charts for variables and control charts for attributes (Montgomery, 2008). The first category contains control charts for individual measurements that are common in 3D printing. However, such control charts are designed for identical products, which seldom happen in 3D printing. The second category contains control charts for attributes that have great potential to be applied to 3D printing. For example, the number of defects on a 3D-printed object is a critical problem. Under the assumption that the number of defects on a unit of surface follows a Poisson distribution, control charts for nonconformities (defects) can be constructed as follows to minimize the number of defects (Montgomery, 2008):
Human mistakes and neglect are the two main causes of low reliability, and 3D printing has been considered effective for enhancing reliability because it eliminates human intervention (Wittbrodt et al., 2013).
Compared with traditional manufacturing machines, a 3D printer is less reliable and breaks down easily. To address this problem, 3D printer vendors continually improve the hardware and software. For example, the printhead is a critical component; the mean time between failures of a printhead is therefore a meaningful index of the reliability of a 3D printer (Benchoff, 2015). To enhance thermal management, sensor points have been added to the printhead (Tarantola, 2016). However, the reliability of a 3D printer is seldom monitored in practice (Benchoff, 2015).
The printing material is another cause of the low reliability of a 3D printer. For example, some materials can clog the nozzle by drying within it. To address this problem, Lanzetta and Sachs (2003) used ethylene glycol-based colloidal silica instead of aqueous colloidal silica. However, the most critical criterion for choosing the printing material is the resulting performance of the printed objects rather than the reliability of the printer. Reliability may therefore not be the top priority in 3D printing.
4.8 Cause-and-effect diagram
One of the seven basic tools of QC is the cause-and-effect diagram. Barclift and Williams (2012) used a cause-and-effect diagram to categorize the factors influencing the mechanical properties of a photopolymer part fabricated using 3D printing. The factors were classified into six categories: man, machine, method, material, measurement and environment. The classification results helped group the efforts to improve the quality of the 3D-printed object, thereby eliminating redundant or contradictory actions and enhancing efficiency and effectiveness. Figure 1 provides another example.
4.9 Outgoing quality control
OQC is the QC stage wherein the product is validated against the customer’s requirements. Finished goods are usually sampled before inspection. However, each product is usually printed only once, rendering sampling impossible; that is, all products must be inspected. The features that are typically checked include product appearance, performance, service life and packaging, with some features (e.g. appearance and performance) having higher emphasis in 3D printing. In addition, when a 3D-printed object is used for prototyping, there is no external customer but only an internal customer (i.e. the R&D or product engineer), and the requirements for the 3D-printed object differ from those for a commercial product. Moreover, whether a 3D-printed object can meet the requirements for a commercial product is often questioned.
5. Standards and requirements
5.1 Quality and quality control standards for three-dimensional printing
Both .STL and .OBJ are standard file formats for 3D printing, and adhering to them ensures ubiquitous printability for 3D objects (Lin and Chen, 2017). Some standards and guidelines, such as the 3D Printing and Additive Manufacturing Equipment Guideline by UL (UL, 2015), can be followed while developing 3D printing equipment. Following such standards is believed to yield safe and high-quality equipment.
Some standards and guidelines for the quality of a 3D-printed object and the execution of QC activities within the 3D printing process have recently been proposed by various international organizations. For example, the American Society for Testing and Materials International (ASTM International) proposed some guidelines for fabricating safe and high-quality components by using powder-bed fusion methods involving laser and electron-beam sources (Orr, 2014), such as electron beam melting, SLS, selective laser melting and direct metal laser sintering. For example, the ASTM F3091 standard specifies the quality requirements for the mechanical, tolerance, surface finishing and postprocessing properties of polyamide components fabricated through SLS. The ASTM F3049 standard is a version of the ASTM F3091 standard that is tailored for the automotive, aerospace and medical industries. The WK46188 standard instructs how to determine the values of the process parameters for powder-bed fusion. However, further efforts for standardization in 3D printing are necessary and underway.
5.2 Quality and quality control requirements in various industries
Quality and QC requirements for 3D printing applications vary across industries. For example, appearance (e.g. surface finishing and colors) is a critical characteristic for 3D-printed objects used in the education industry to teach students new concepts (Rengier et al., 2010). The requirement also applies to the medical industry, where 3D-printed tissues, organs and scaffolds must be vivid to facilitate the training of doctors, the diagnosis of anomalies and the planning of treatments. By contrast, in mechanical industries, the primary concern is whether a component can be precisely assembled along with other parts and whether the assembly functions as designed (Barclift and Williams, 2012), and the appearance of the component is not a crucial concern. Similarly, in the pharmaceutical industry, where customized drugs are synthesized through 3D printing (Acosta-Vélez et al., 2017), the appearance of the drug is largely irrelevant; the successful printing of the customized drug and it efficacy are of prime importance.
By contrast, in all industries that apply 3D printing, conformance to the product specifications and designed performance of the 3D-printed object must be ensured.
6. Future research and development perspectives
Table II summarizes the investigation results and lists both problems that have been solved and those that remain to be addressed.
On the basis of these findings, we recommend that the following R&D directions be explored in the near future:
Product design: Guidelines for the minimal spatial resolution of a 3D scanning system should be established, and these guidelines should account for the purpose of 3D printing. In addition, effective algorithms must be designed for converting the numerous heterogeneous 3D databases. Furthermore, low-resolution 3D models must be enhanced to facilitate 3D printing.
Process planning: There are already several online hubs for gathering and sharing 3D models (e.g. myminifactory.com). However, knowledge bases or expert systems are still required for guiding the setup of 3D printers and the preparation of the printing conditions. Such knowledge bases or expert systems can be integrated with the software of a 3D printer. In addition, algorithms for segmenting low-resolution and nonenhanced 3D models must be proposed. Furthermore, the Taguchi method and objective DOE can be applied to enhance the correctness and efficiency of experimentation to improve the quality of 3D-printed objects by optimizing the production setting.
IQC: As researchers and practitioners desire more types of usable materials and more methods to use these materials, Taguchi method and objective DOE can be applied to optimize the method of using materials (e.g. choosing an optimal combination of materials).
IPQC: Guidelines must be established to determine whether a 3D printing process should be terminated early. In addition, control charts dedicated to 3D printing and the corresponding SQC guidelines should be devised.
OQC: The features to be checked in OQC are clearly purpose-oriented; therefore, relevant clear guidelines must be formulated.
From the previous review and discussion, the following can be concluded about the current research on and practice of QC in 3D printing:
3D printer vendors continue to upgrade hardware and software to improve the quality of 3D-printed objects. However, the preparation of the printing environment and the setup, monitoring and maintenance of a 3D printer may be more critical, all of which are the responsibility of the user.
The application of QC techniques to 3D printing is not uncommon. Cause-and-effect analysis and DOE, in particular, are extensively applied. However, the DOEs in many studies were subjective and incomplete and therefore could not guarantee the optimization of the quality of the 3D-printed objects.
QC techniques such as Taguchi’s method and control charts can be applied to further enhance the quality of 3D-printed objects. However, these QC techniques require repetitive experimentation, whereas an object may be 3D-printed only once or a few times. This limits the applicability of these two techniques.
3D printing has both positive and negative effects on product quality. A product fabricated using 3D printing is usually cruder than counterparts fabricated using conventional manufacturing technologies. However, 3D printing can fabricate parts with complicated shapes exactly according to the specifications, which can improve the quality of a product composed of 3D-printed parts.
Regarding future R&D, QC will play a more important role in 3D printing as an increasing number of products can be created or even mass-produced through 3D printing. In addition, to implement IPQC, a 3D printer can be equipped with sensors to detect any abnormal conditions that require early termination of the printing process.
L4 orthogonal array
|Replication no.||Powder packing density||Powder flowability||Layer thickness|
|1||Level 1||Level 1||Level 1|
|2||Level 1||Level 2||Level 2|
|3||Level 2||Level 1||Level 2|
|4||Level 2||Level 2||Level 1|
Summary of the findings
|Stage||Problems solved||Problems yet to be investigated|
|Product design||Standard 3D model file formats
Algorithms for converting specific 3D databases
|Guidelines for determining the minimal spatial resolution of a 3D scanning system
Algorithms for converting other types of 3D databases
Enhancing low-resolution 3D models
|Process planning||Segmentation algorithms for high-resolution and enhanced 3D models
Application of full factorial or subjective DOE
|Systematic accumulation of acquired knowledge and experience
Segmentation algorithms for low-resolution and nonenhanced 3D models
Applications of Taguchi method and objective DOE
|IQC||Replacing materials unsuitable for use
No need to conduct extensive IQC
|More types of materials
More methods of using the materials
Application of the Taguchi method and objective DOE
SQC guidelines for specific 3D printing processes
|Guidelines for early termination
Managing variation between products
Dedicated control charts
SQC guidelines for other 3D printing processes
|OQC||Selective and subjective OQC
Clear guidelines for OQC
Acosta-Vélez, G.F., Linsley, C.S., Craig, M.C. and Wu, B.M. (2017), “Photocurable bioink for the inkjet 3D pharming of hydrophilic drugs”, Bioengineering, Vol. 4 No. 4, p. 11.
Alfieri, V., Argenio, P., Caiazzo, F. and Sergi, V. (2017), “Reduction of surface roughness by means of laser processing over additive manufacturing metal parts”, Materials, Vol. 10 No. 12, p. 30.
American Society for Quality (2016), “Quality assurance vs. quality control”, available at: http://asq.org/learn-about-quality/quality-assurance-quality-control/overview/overview.html
Asadi-Eydivand, M., Solati-Hashjin, M., Farzad, A. and Osman, N.A.A. (2016), “Effect of technical parameters on porous structure and strength of 3D printed calcium sulfate prototypes”, Robotics and Computer-Integrated Manufacturing, Vol. 37, pp. 57-67.
Barclift, M.W. and Williams, C.B. (2012) “Examining variability in the mechanical properties of parts manufactured via polyjet direct 3d printing”, International Solid Freeform Fabrication Symposium, Texas, pp. 6-8.
Benchoff, B. (2015), “3D printering: Hobbs meters and 3D printer reliability”, available at: http://hackaday.com/2015/02/10/3d-printering-hobbs-meters-and-3d-printer-reliability/
Berman, B. (2012), “3-D printing: the new industrial revolution”, Business Horizons, Vol. 55 No. 2, pp. 155-162.
Bose, S., Vahabzadeh, S. and Bandyopadhyay, A. (2013), “Bone tissue engineering using 3D printing”, Materials Today, Vol. 16 No. 12, pp. 496-504.
Calì, J., Calian, D.A., Amati, C., Kleinberger, R., Steed, A., Kautz, J. and Weyrich, T. (2012), “3D-printing of non-assembly, articulated models”, ACM Transactions on Graphics, Vol. 31 No. 6, p. 130.
Campbell, R.I., Martorelli, M. and Lee, H.S. (2002), “Surface roughness visualisation for rapid prototyping models, CAD”, Computer Aided Design, Vol. 34 No. 10, pp. 717-725.
Chen, T. and Wang, M.J.J. (1999), “A fuzzy set approach for yield learning modeling in wafer manufacturing”, IEEE Transactions on Semiconductor Manufacturing, Vol. 12 No. 2, pp. 252-258.
Chohan, J.S. and Singh, R. (2017), “Pre and post processing techniques to improve surface characteristics of FDM parts: A state of art review and future applications”, Rapid Prototyping Journal, Vol. 23 No. 3, p. 4.
German, R.M. (1992), “Prediction of sintered density for bimodal powder mixtures”, Metallurgical Transactions A (Physical Metallurgy and Materials Science), Vol. 23 No. 5, pp. 1445-1465.
Grieser, F. (2015), “3D printing quality issues: 10 tricks to avoid them”, available at: https://all3dp.com/3d-printing-quality/
Herrmann, K.H., Gärtner, C., Güllmar, D., Krämer, M. and Reichenbach, J.R. (2014), “3D printing of MRI compatible components: why every MRI research group should have a low-budget 3D printer”, Medical Engineering & Physics, Vol. 36 No. 10, pp. 1373-1380.
Hopkinson, N. and Dickens, P. (2001), “Rapid prototyping for direct manufacture”, Rapid Prototyping Journal, Vol. 7 No. 4, pp. 197-202.
Hsiao, S.Y. (2015) “Study of Parameter Optimization of Gradient Color Process for Phto-curable 3D Printing System”, Ph.D. thesis, Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei.
Jacobs, S., Grunert, R., Mohr, F.W. and Falk, V. (2008), “3D-imaging of cardiac structures using 3D heart models for planning in heart surgery: a preliminary study”, Interactive Cardiovascular and Thoracic Surgery, Vol. 7 No. 1, pp. 6-9.
Kuo, Y., Yang, T. and Huang, G.W. (2008), “The use of grey relational analysis in solving multiple attribute decision-making problems”, Computers & Industrial Engineering, Vol. 55 No. 1, pp. 80-93.
Lanzetta, M. and Sachs, E. (2003), “Improved surface finish in 3D printing using bimodal powder distribution”, Rapid Prototyping Journal, Vol. 9 No. 3, pp. 157-166.
Lin, Y.C. and Chen, T. (2017), “A ubiquitous manufacturing network system”, Robotics and Computer-Integrated Manufacturing, Vol. 45, pp. 157-167.
Manufacturing Behavioral Science (2010), “In-process quality control: An introduction”, available at: www.manufacturingbs.com/CB/CB-01.pdf
Mendibil, X., Llanos, I., Urreta, H. and Quintana, I. (2016), “In process quality control on micro-injection moulding: the role of sensor location”, International Journal of Advanced Manufacturing Technology, Vol. 89 Nos 9/12.
Mironov, V., Kasyanov, V. and Markwald, R.R. (2011), “Organ printing: from Bioprinter to organ biofabrication line”, Current Opinion in Biotechnology, Vol. 22 No. 5, pp. 667-673.
Mohammad, A., Al-Ahmari, A.M., AlFaify, A. and Mohammed, M.K. (2017), “Effect of melt parameters on density and surface roughness in electron beam melting of gamma titanium aluminide alloy”, Rapid Prototyping Journal, Vol. 23 No. 3, p. 2.
Montgomery, D.C. (2008), Introduction to Statistical Quality Control, John Wiley & Sons, New York.
Norman, J., Madurawe, R.D., Moore, C.M., Khan, M.A. and Khairuzzaman, A. (2016), “A new chapter in pharmaceutical manufacturing: 3D-printed drug products”, Advanced Drug Delivery Reviews, Vol. 108.
Orient Semiconductor Electronics. (2016) “Quality control process”, available at: www.ose.com.tw/electronics-manufacturing/commitment-to-quality/quality-control-process
Orr, T. (2014), “ASTM International proposes new 3D printing guidelines for powder bed fusion machines”, available at: https://3dprint.com/13367/astm-international-standards/
Rengier, F., Mehndiratta, A., von Tengg-Kobligk, H., Zechmann, C.M., Unterhinninghofen, R.H., Kauczor, U. and Giesel, F.L. (2010), “3D printing based on imaging data: review of medical applications”, International Journal of Computer Assisted Radiology and Surgery, Vol. 5 No. 4, pp. 335-341.
Shang, J. (2016), “Luxexcel and Automation & Robotics collaborate on a quality control program”, available at: https://3dprintingindustry.com/news/luxexcel-automationrobotics-collaborate-quality-control-program-95826/
Silva, D.N., De Oliveira, M.G., Meurer, E., Meurer, M.I., da Silva, J.V.L. and Santa-Bárbara, A. (2008), “Dimensional error in selective laser sintering and 3D-printing of models for craniomaxillary anatomy reconstruction”, Journal of Cranio-Maxillofacial Surgery, Vol. 36 No. 8, pp. 443-449.
Straub, J., Kading, B., Mohammad, A. and Kerlin, S. (2015), “Characterization of a large, low-cost 3D scanner”, Technologies, Vol. 3 No. 4, pp. 19-36.
Tarantola, A. (2016) “Makerbot just made a more reliable 3D printer head”, available at: www.engadget.com/2016/01/04/makerbot-built-a-longer-lasting-and-more-reliable-extruder/
UL (2015), “3D printing & additive manufacturing equipment guideline”, available at: www.ul.com/wp-content/themes/countries/downloads/am/3D-PRINTING-EQUIP-SAFETY-GUIDELINE_EDITION2.pdf
Weller, C., Kleer, R. and Piller, F.T. (2015), “Economic implications of 3D printing: market structure models in light of additive manufacturing revisited”, International Journal of Production Economics, Vol. 164, pp. 43-56.
Wittbrodt, B.T., Glover, A.G., Laureto, J., Anzalone, G.C., Oppliger, D., Irwin, J.L. and Pearce, J.M. (2013), “Life-cycle economic analysis of distributed manufacturing with open-source 3-D printers”, Mechatronics, Vol. 23 No. 6, pp. 713-726.
Wu, H.C. and Chen, T. (2015), “CART–BPN approach for estimating cycle time in wafer fabrication”, Journal of Ambient Intelligence and Humanized Computing, Vol. 6 No. 1, pp. 57-67.
Yang, K. and El-Haik, B. (2008), “Taguchi’s orthogonal array experiment”, Design for Six Sigma: A Roadmap for Product Development, Vol. 1, pp. 469-497.
Young, J. (2015), “3D Printed Aircraft Parts and Engines Could Lighten Aircrafts by 50%”, available at: http://3dprinting.com/aviation/3d-printed-aircraft-parts-could-lighten-aircrafts-by-fifty-percent/
Zavorotnitsienko, D. (2015), “Understanding 3D printer quality & resolution”, available at: www.ilios3d.com/en/product-documentation/ilios-documentation-3dprint-quality