Industry 4.0 (I4.0) technologies encompass various components, including the Internet of Things (IoT), big data and analytics, artificial intelligence (AI), cybersecurity (CS), cloud computing (CC), augmented and virtual reality (AR/VR), advanced robotics, digital twin, and additive manufacturing (AM). Among them, AM has the most potential to create new methods and innovate manufacturing processes.
Additive manufacturing (AM) is a technique to produce parts by depositing material layer-by-layer according to the three-dimensional computer model. Compared to traditional subtractive manufacturing methods, AM gets particular attention due to its ability to minimize material waste while producing intricately shaped and multi-material components. In addition to its advantages in rapid prototyping, AM enables low-batch, customer-centric mass manufacturing by facilitating quick responses to changes in customer requirements. With its numerous benefits, the AM method finds extensive application in diverse industries ranging from aerospace to biomanufacturing.
2. Implementation-Based Monitoring and Control
Process Monitoring and Control—With the help of digital technologies such as I4.0, using the data gathered from the process/machine to optimize and develop the process will be the key technological development of the coming years. This part summarizes the literature related to process-specific monitoring and control systems which receive significant research efforts in both industry and academia to address the challenges associated with collecting data and their manipulation.
McCann et al. 
reviewed the state-of-the-art on-line monitoring and control methods for the laser-based powder bed fusion AM methods. They explained using different kinds of sensors (e.g., acoustic, optical and thermal) in process monitoring, and concluded that integrating multiple sensors would increase monitoring performance. Additionally, they discussed advanced technologies and possibilities of using machine learning-based algorithms to keep the process under control. They pointed out the necessity of the monitoring and control systems and their effectiveness considering the cost and accuracy, and that research should focus on innovative sensing systems and their combined approaches.
Arrizubieta et al. 
designed a smart nozzle for the laser metal deposition process. It measures the melt pool temperature to decide the required amount of laser power and keeps the powder flow constant along the surface. The nozzle also examines the geometrical accuracy of the deposited material by an optical sensor to help minimize post-processing and overall cycle time. Oehlmann et al. 
used a nozzle equipped with a force sensor and thermistor to analyze and forecast the force into the nozzle in the fused filament fabrication method of AM. They trained an artificial neural network (ANN) by theoretical data as well as the real-time force and temperature data collected from the process. Although processing speed and printed part quality were optimized well, the need for more comprehensive models was emphasized by the researchers. Furthermore, Kadam et al. 
installed a low-cost RGB camera on a Fused Deposition Modeling (FDM) machine and predicted defects by processing captured image data of each printed layer. They compared the accuracy and computational speed of different combinations of various pre-trained models and classification algorithms to identify the layer quality as good or bad. For on-line fault monitoring, the authors showed that Alexnet and Support Vector Machine algorithm combination showed the best performance.
The quality of the parts produced by AM is a challenge in the manufacturing industry due to many parameters and uncertainties. Kim et al. 
followed a model-based engineering approach to decide key process parameters and optimize their values in a dynamically changing environment. They demonstrated their smart manufacturing system on a laser-based AM process. A regression model was first used to predict the performance metrics according to the changes around the process parameters, and then a multi-objective optimization was formulated with desired outputs. The proposed systematic approach would have challenges with uncertainty quantification and optimization stages when physics-based computer simulations replace or support the empirical models.
Digital twin technology can be explained as replicating the monitoring and control of the real system in a virtual environment. Gunasegaram et al. 
explained the difficulties in comprehensive modeling to support the digital twinning of AM and discussed how they can be resolved. For the technical barriers caused by the complexities of AM process such as its multiscale-multiphysics nature, the authors pointed out that there is a strong need for high-fidelity computational models to explain the missing information in the experimental data. From non-technical aspects, standardization of the AM processes and lack of collaboration between different institutions is another challenge highlighted by the authors. In another study, Gunasegaram et al. 
argued that the digital twinning of AM will achieve repeatability and cost-effective manufacturing. Zheng and Sivabalan 
developed a digital twin with three pillars: (1) a Digital model visually represents the system and its working environment; (2) a graph-based model which applies constraints related to laws of nature, (3) a computational model that assesses process conditions to monitor and control the systems.
Following the developments in the digital twin of AM, Okwudire et al. 
proposed a cloud-based control system for 3D printers. Instead of using the high-level G-code commands locally, the authors took advantage of the fast computational speed of cloud computing engines in Australia and South Carolina to directly generate low-level cloud-based motor control commands. While maintaining similar print quality, using a cloud-based controller resulted in printing time which is more than twice as fast as when the local controller was used.
Planning the production with the help of I4.0 technologies received considerable research attention to control machine usage as well as materials and logistics that support fabrication. SLR by Bueno et al. 
showed the relationship between the five pillars of I4.0 technologies, IoT, CPS, BD, AI and AM, and production planning and control (PPC). They reported that IoT technologies are essential to develop and improve PPC processes by focusing on controlling both manufacturing operations and resources and helping plan capacity and manufacturing while allowing for optimization of planning to improve the sustainability of manufacturing.
Darwish et al. 
studied production planning by developing new algorithms for task allocation and scheduling. They aimed to minimize the shortage of personal protective equipment and spare parts for venting machines during the global COVID-19 pandemic. Their proposed models increased the utilization of 3D printers on the shop floor while balancing the distribution of tasks among them. Elhoone et al. 
established a cyber additive design and manufacturing system that consists of three stages. In the first stage, a database was created to identify the specifications of the 3D part design. In the second stage, AI was applied to decide a suitable AM method by using information such as the minimum wall thickness, post-processing method and printing resolution. The reported design accuracy of the ANN-based expert system was 90%. In the final stage, a cyber interface was employed to monitor and control the availability and capacity of the AM machines in the network. Their research showed that I4.0 technologies would be effective in distributing the tasks as well as in controlling and monitoring of the overall manufacturing system.
Customer-based manufacturing systems received particular attention in recent decades. Zawadzki and Zywicki 
focused on smart product design and production control systems to maximize the efficiency of production systems and minimize the prototyping time, especially for achieving mass customization. They showed that automated and knowledge-based design systems are the enablers of mass customization. To boost the capacity of customer-based production, smart factory-based applications such as Factory-as-a-service (FaaS) by Kang et al. 
have been developed. The multidirectional system serves for manufacturing, inspection, control and monitoring of the process as well as for visualization of the production environment, cloud-based work instructions and production planning. The developed model was used for two different scenarios to demonstrate the speed and effectiveness of the decision-making system.
Gupta et al. 
analyzed the critical parameters that have an impact on the development of Cloud-based Enterprise Resource Planning (Cloud ERP) systems and their effects on improving a company’s social, economic and sustainable performance. They concluded that the success of a Cloud ERP system in a company depends both on the size of the company and on the scope and type of cloud services. To support the cloud-based system planning and control, new communication methods are developed to enhance better communication between the systems and machines. In their study, Paszkiewicz et al. 
surveyed the possible network methods to communicate between additive manufacturing machines and controllers. Additionally, Mazur et al. 
developed a software-defined network to effectively allocate the resources in the system. The authors verified the planning and control-based system in their laboratory, and they emphasized that real-time algorithms should be developed for better resource allocation and hybrid models could be used in WAN communication environments. Xu et al. 
proposed a novel approach to achieve on-time and on-demand manufacturing of medications by using the light from the screen of a mobile device as a photopolymerized light in a stereolithography-based AM machine. Their proposed system helps with managing connected devices and communication between them through a user-friendly solution.
In AM, robot arms can be used as building plates or print heads, or as quality control equipment to evaluate the manufactured parts. For this purpose, research on robotic control and tool path planning strategy is required to achieve better flexibility in product design and to produce high-quality components. Bordron et al. 
equipped a robot with a laser sensor to collect measurement data from the additively manufactured parts and validate the surface quality and decide on the required post-processing operations. They developed an automatic path planning method that aims to achieve both the minimum digitalization time and high-quality point cloud. The motion flexibility of robots to access difficult-to-reach points in complex parts further improved productivity, resource efficiency and sustainability.
For robot-assisted metal AM, Zhu et al. 
developed a novel approach by merging three disciplines, 3D CAD design based on AM, slicer strategy and path planning of the robot’s head. They also created a virtual production environment to simulate the robot’s path during the AM process. Wu et al. 
developed a 3D profile maker for on-line analysis and control of the robotic cold spray coating process which was applied as a novel variant of AM. Firstly, the profile maker identifies dimensional errors by digitizing the surface using a 3D scanner. Then, the errors are compensated by updating the trajectory of the robot arm based on the deviations between the measured and desired surface. The proposed model shows positive outcomes from the perspective of adapting I4.0 technologies to improve current systems. For its application in smart factory settings, adaptation of I4.0 technologies was effectively demonstrated through a closed-loop, on-line system which monitors the workpiece quality, decides on the necessary actions while controlling the robot arms.
Quality Control and Maintenance—
By reviewing the current on-site monitoring and control methods that use sensing and machine learning technologies of I4.0, Di Cataldo et al. 
analyzed the barriers and gaps in optimizing the quality control and inspection process of the metal powder bed-based AM operations. Firstly, they discussed the necessity of solving complex multivariable problems to study the effects of process parameters that can influence the part quality and find their optimal combinations that achieve faster processes with defect-free and high-quality parts. Secondly, for metal PBF, they explained possible manufacturing defects related to dimensional accuracy, surface quality, and microstructural and mechanical properties. After summarizing the available off-the-shelf sensors and equipment to control and monitor the manufacturing process, the study focused on the applications of AI in the PBF process. Finally, the research emphasized the lack of standards in AM and difficulties in managing big data acquired from the process.
Francis and Bian 
investigated the distortion issue that causes dimensional inaccuracy of additively manufactured parts. They developed a deep learning method to predict distortion using Big Data by correlating each location to more than 21,000 thermal images captured by a pyrometer within the total production duration (i.e., 66 min). The root mean square (RMS) error of predicting distortion of disk-shaped component (with 45 mm diameter and 5 mm thickness) was 24 μm for the training set. The authors pointed out that this value does not only meet the tolerance requirements, but it is also competitive with the outcome of machining processes. Moreover, the test set’s RMS error was 56 μm, reassuring the promising performance of the proposed method. Omairi and Ismail 
comprehensively reviewed the literature on AM technology, as well as the machine learning techniques to observe imperfections in AM and heuristic algorithm implementations for prediction models. Three AI-based error compensation methods were summarized, and each method showed success to compensate for imperfections such as thermal distortion and rate of shrinkage. The authors pointed out that the security issues of using cloud-based systems need to be solved. In a related study, Scimone et al. 
developed a statistical model for monitoring the dimensional changes in complex shaped parts. Their model used point clouds to calculate the variations with the help of sensor technologies.
3. Field of Application-Based Monitoring and Control
Chemical and Healthcare Applications—
ML/AI technologies can provide new ways to monitor, control and improve healthcare and chemical applications. Elbadawi et al. 
reviewed the potential ways to implement machine learning (ML) methods on the AM applications in drug development, e.g., design depending on specific dosage, drug release performance and quality control process. They argued that ML technologies will have a crucial impact on customized, patient-based medicine in the near future. Muniz Castro et al. 
reviewed 114 articles and then created 968 formulations to guess the 3D printing process variables and in vitro dissolution characteristics of the drug delivery systems. Selected ML algorithms achieved 93% accuracy, they also successfully forecasted drug release information of the 3D printed medicines. The authors stated that 3D printing datasets, with the help of ML technologies, will have a crucial role in future discoveries. In the field of healthcare, Zhu et al. 
argued that the assistance of AI is essential to enable in situ organ printing in the future. AI will help to understand, analyze and adapt to the condition of the manufacturing environment, and it will guide the entire process chain from design to production of patient-specific organs.
Awad et al. 
reviewed the implementation of digital technologies such as sensors, robotics, 3D printing, and IoT technologies in healthcare. The main application areas are sensor data collected from the human body to support the diagnosis of diseases, and 3D printing technologies to produce personalized drugs and treatments. Using robots in the drug delivery process and the identification of diseases is another promising technology to minimize the required time to diagnose and treat diseases. However, the authors pointed out that the rapid change of technology and its applications in healthcare would require more attention to accuracy, safety, and standards. Tai et al. 
proposed a comprehensive model involving AI and data science to demonstrate the potential future directions in laboratory developments. The AM system, which consists of multiple chemical steps, was equipped with embedded sensors and cyber systems to analyze the potential applications and the future directions of the term “closing the loop”. The use of AI technologies was evaluated in optimizing the process quality according to the target product (i.e., inverse design) rather than using the initial parameters (i.e., forward design), and in redesigning the process with sustainable improvement opportunities.
O’Reilly et al. 
studied AI-assisted manufacturing of drug delivery systems, particularly of orodispersible films (ODFs) to achieve personalized and just-in-time medicines sustainably. They explained that 3D printing technology can address the inherent sustainability-related issues of conventional manufacturing of ODFs by minimizing the need for post-processing and the amount of wasted materials. To resolve the manufacturing and quality control challenges of 3D printed medicines, they used ML technology to automate the analysis of near-infrared (NIR) spectra and classify the active elements with 100% accuracy, while using machine vision technology to identify physical defects. They showed that manufacturing of ODFs can become automated and more accurate using AI.
In relation to the product life cycle, previous research shows that resource selection, logistics, production and recycling steps contribute significantly to the overall sustainability of a manufacturing process. Hence, monitoring, controlling and developing the existing systems using I4.0 technologies are required to support the sustainability goals.
D’Aniello et al. 
designed a cyber-physical system to monitor and control the workplace with the help of a multi-agent system to address issues related to dynamic task arrival and machine downtime. Their system aimed to create a sustainable manufacturing environment by using a “Scattered Manufacturing Network (SMN)” to minimize waste, CO2
emissions and production costs. Moreover, Dev et al. 
developed a reverse logistics virtual model using six pillars of I4.0 technologies, and demonstrated it on the transportation network system of a refrigerator producer in India. By presuming that some parts of refrigerators are common, the proposed model helped develop the return system to improve the sustainability of the manufacturing system. The integration of the model with I4.0 technologies opened ways to investigate customer behavior as well. Even though the research is mostly at a theoretical level, it showed the necessity of a wide range of cyber-physical social networks.
Majeed et al. 
proposed a BD-SSAM (“Big Data-driven sustainable and smart additive manufacturing”) framework that merges smart manufacturing, AM, Big Data analytics and sustainability. The framework was applied to the manufacturing stage of the product life cycle to optimize process parameters based on Big Data-driven information for improved productivity, resource efficiency and product quality. The framework has several stages in which the entire manufacturing process data are gathered, stored and processed with the help of IoT technologies, and controlled and monitored through data mining and decision-making algorithms which can also help implement sustainable production performance. The authors performed a case study to demonstrate how BD-SSAM can help optimize the parameters of a powder-bed-based AM process for a new component introduced to a company. For the first time in the literature, they defined the SSAM system and made a step change to collect and create meaningful information from the big data sets.
AM could also be used to produce parts that improve the sustainability of existing manufacturing systems. Caruso and Filice 
have designed and produced an innovative part via AM to increase resource efficiency in Aluminum alloy wire manufacturing. The role of the part is to provide an innovative deformation process by increasing the flexibility of the manufacturing process while controlling and adapting the mechanical strain of the wires. Furthermore, the new additively manufactured part helps minimize chip formation compared to traditional manufacturing methods.
The human–machine interface (HMI) is the main supporting technology of process monitoring and control as it enables gathering real-time machine/process data that can be further processed to optimize the parameters on-the-fly. Ardanza et al. 
developed a multi-purpose HMI to collect and manipulate real-time data from the machine with IoT technology. In the meantime, to maximize sustainability they equipped existing machines with external sensors to avoid the negative effect of the rapid change in technology (i.e., buying new generation machines) and controlled their energy consumption. The authors tried this HMI in three settings: Additive manufacturing, motor control of a CNC machine and the digital twin of a collaborative robot. Results showed that the developed HMI system is suitable for monitoring and control as well as for improving the sustainability of the machinery.
5. Cost-Effective Solutions
Salem and Elksasy 
developed a low-cost AM system (~$114) using off-the-shelf components to respond to AMs disadvantages such as low levels of monitoring and control, energy disruptions during the printing process and lack of remote control of AM machines. With its open-source software and hardware, the proposed system additionally aims to help manufacturers to cope with major disruptions (such as the global pandemic). Wang et al. 
developed “Multi-modal best subset” model to increase cost-effectiveness in smart manufacturing systems by choosing the correct sensors and deciding on sensor locations. As a case study, they installed an infrared sensor, accelerometers and thermocouples on an FDM-type 3D printer, and then successfully found the most relevant sensory data to monitor a quality variable.
In relation to I4.0 technologies, Menolotto et al. 
evaluated the state-of-the-art implementations of motion capture technology in various industries. They found that an optical camera is widely preferred in motion capture applications when compared to an inertial measurement unit. Although construction, robotics and automotive industries had significant use of motion capture technology for monitoring processes and goods, significant applications on health and safety applications were identified as well. The authors concluded that there is low-cost, easy-to-implement, off-the-shelf equipment that can be employed for specific use. Dobrilovic et al. 
studied the design and implementation of innovative cyber-physical systems in cost-effective ways such as using open-source software, I4.0 technologies and low-cost off-the-shelf equipment. Their research has two stages: In the first stage, 600+ dust images were collected at the shop floor, and they were implemented in AI-assisted simulation to model the ventilation system on the shop floor. In the second stage, several low-cost Arduino-based sensors were used to monitor the shop floor to verify the simulation results. The authors stated that the implementation part is limited to ~174 packets per second data flow. However, this type of implementation can still be useful for, e.g., SMEs which have fewer data flow and financial restrictions.
The AM could help achieve the production of cost-effective monitoring and control equipment as well. For example, Borghetti et al. 
used inkjet and aerosol jet printing methods not only to print electronics embedded in the parts but also to give them the functionality to serve as smart sensors in the industry. Thus, AM can be directly used to achieve low-cost, customer-based and flexible sensors specific to an industrial application and could help companies, e.g., SMEs, with financial inadequacies. As an example of the assistive use of AM, Mardonova and Choi 
designed a low-cost underground mine monitoring system by using open-source hardware and software. Arduino microcontroller was used to process the environmental data acquired by temperature, gas, humidity and dust sensors and to merge and visualize the gathered data within the MIT App Inventor software. To save space and resources, the designed system was assembled inside the 3D-printed case which was then mounted on a mine truck. The resulting low-cost monitoring system (~$47) was validated in real underground conditions.