Perspective Chapter: Integrating Industry 4.0 and CNC Machines through CNC Tools

3.1 The concept of Industry 4.0

The term Industry 4.0 was first introduced in 2011 at the Hannover Fair as part of Germany’s High-Tech Strategy for industrial innovation. Then, two years later, in 2013, a German working group published a detailed report that laid the foundation for the concept [1]. This report described an industrial environment characterized by highly customized products and flexible mass production. It also introduced the idea of self-organizing systems capable of autonomous automation, configuration, and diagnostics, designed to connect the physical world of people and machines with the digital world [14, 16, 17, 18].

Industry 4.0 is driven by the intensive use of advanced Information and Communication Technologies (ICT), aiming to make production systems more adaptable and responsive. This shift is largely a response to the growing complexity of products and supply chains [19, 20, 21]. The concept has since gained global momentum, with stakeholders across industries launching initiatives to stay competitive in the face of digital transformation [1].

The main purpose of Industry 4.0 is the creation of smart factories: interconnected environments capable of handling complex processes with minimal disruption and maximum efficiency. In these factories, the interaction between humans, machines, and resources mirrors the dynamics of a social network [1, 13]. CPS continuously monitor operations and make decentralized decisions, while physical components are transformed into IoT devices. These devices communicate and collaborate in real time, using both wired and wireless networks connected to the Web [3, 14, 22].

3.2 CNC machines and CNC tools with CPS capabilities in Industry 4.0

A cutting-edge tool has recently entered the CNC machining space, bringing with it the transformative potential of cyber-physical technology. Unlike traditional tools, which operate in isolation and lack real-time data capabilities, this new system integrates sensors, smart algorithms, cloud computing, data analytics, and IoT connectivity [23, 24].

This advancement opens the door to a deeper understanding of production processes. Transmitting operational data in real time allows manufacturers to analyze performance and anticipate future production needs. In this research, the tool will be used to explore how such insights can reshape CNC operations. As digitalization continues to evolve, it offers powerful opportunities to boost both productivity and efficiency across the manufacturing sector [24, 25]. This tool introduces novel cyber-physical technological capabilities. CPS encompass the integration of physical and virtual components within a networked environment, enabling the coordination of physical resources and operational processes [2]. Within such networks, information flows from diverse sources, continuously monitored and synchronized between the factory floor and its digital counterpart [2, 26]. The tool reflects two fundamental pillars of Industry 4.0: the deployment of CPS and the interconnectivity facilitated by the IoT. Collectively, these elements mark a transition toward more intelligent, adaptive, and responsive production systems.

3.3 Cyber-physical systems and CNC tools

The concept of CPS is reshaping industrial automation by bridging the gap between digital intelligence and physical machinery. CPS integrates computational applications with physical devices, forming a network of interactive elements that work together to monitor and control real-world infrastructure [26].

In the context of CNC machine tools, CPS plays a vital role. The physical system includes all the manufacturing resources needed to perform machining tasks, such as cutting tools, fixtures, machine components, and even environmental factors like vibration and temperature. The CNC machine tool performs a defined manufacturing task utilizing designated production resources. By analyzing operational status data, the outcomes in terms of work performance, product quality, and productivity can be represented through characteristic parameters associated with the machining process [23].

A CPS model for CNC machining consists of two key inputs: the work task and the manufacturing resources. The output is the operation status data, which reflects how the machine performs during the task [2, 27]. This model runs in parallel across both the physical and digital domains, allowing for real-time analysis and optimization.

Sensors integrated into the tools and connected software form the support of this system. These tools continuously collect data during machining, feeding it into cloud-based dashboards that integrate with the user’s software and machine environment [2]. The result is a two-way communication channel that ensures quality data flow and synchronization between the factory floor and cyberspace [28].

The CPS-enabled tool collects data, and it empowers operators. Information is instantly relayed to both the CNC machine and its operator via connected software and hardware. This allows for real-time monitoring, precision control, and immediate response to any anomalies [23]. For example, if the system detects excessive force or vibration, it can alert the operator and trigger preprogrammed corrective actions to prevent damage to the machine or the workpiece.

Enabling this level of responsiveness, CPS transforms CNC machining from a reactive process into a proactive one, where problems are anticipated and solutions are arranged instantly. It’s a leap forward in manufacturing intelligence, offering greater precision, higher productivity, and smarter automation.

3.4 Internet of Things and CNC tools

The IoT is a transformative shift that allows everyday objects to connect, communicate, and compute. With embedded sensors and smart protocols, devices like CNC tools can gather data, share insights, and make informed decisions to optimize machining performance [29]. When CNC machines are connected through IoT, they become intelligent systems capable of monitoring operations in real time. They can analyze performance, detect inefficiencies, and adjust processes based on accurate data. This is made possible through a combination of cloud-based analytics and local control systems, which together harness the full potential of the data generated during machining [2].

The design and deployment of CPS necessitate the utilization of IoT-enabled architectures and standardized protocols, as these provide the capacity to manage extensive datasets and facilitate the execution of intricate processes essential for the monitoring and regulation of such systems across varying scales [2]. At a large scale, IoT-driven CPS can be effectively sustained through cloud computing infrastructures and platforms, which deliver elastic computational resources, virtualization capabilities, and high-volume data storage, while simultaneously upholding principles of security, protection, and privacy. Within this context, IoT represents a primary domain for the generation of continuous data streams, the magnitude of which is projected to expand substantially.

This makes it a critical domain for sensing and interpreting the physical world, especially in manufacturing environments [29].

In a CNC setup, IoT sensors act as the eyes and ears of the system. They capture real-world conditions, like temperature, vibration, and tool wear, and translate them into digital signals. These signals are then transmitted via the Internet to cloud platforms, where they’re processed, stored, and analyzed. The result is a flow of information that connects the shop floor to the digital world, enabling smarter decisions and more precise control. By integrating IoT with CPS, manufacturers gain a powerful toolset for improving productivity, enhancing quality, and optimizing their operations.

3.5 Integrating cyber-physical tools with CAM and CNC systems

The advent of CPS in CNC machining introduces new opportunities and prospects, particularly in exploring the integration of these systems with CAM software and CNC equipment [2]. This section seeks to illustrate the role of CAM software in facilitating communication between CPS-enabled tools and CNC machines.

The application of computer graphics further enables the generation of virtual workpieces, providing a digital environment in which they can be manipulated and analyzed. This digital approach has revolutionized manufacturing, with CAM playing a central role in process planning, CNC programming, and engineering workflows [30].

The steps for creating a manufacturing model in CAM to generating CNC code take place in the cyber world. CAM software allows users to simulate machining processes virtually. If the simulation runs smoothly, the process can then be executed in the physical world. These two stages, simulation and execution, are sequential, not simultaneous.

With CPS-enabled CNC tools and supporting software packages, it becomes crucial to understand how this workflow might evolve. Take, for example, the tool developed by Sandvik Coromant [23], which is used in this study to illustrate key concepts. This tool includes Tool Guide software that connects with CAM platforms, helping users select the optimal tool and machining parameters before running a simulation. Tool Guide uses an open API, allowing it to integrate with CAM tool libraries and management systems. An application programming interface (API) is a set of protocols that allow different software systems to communicate and share data. This means programmers don’t need to manually create a virtual tool for simulation; the Tool Guide already provides it. However, full real-time integration between CAM software, CNC machines, and CPS-enabled tools is still a work in progress [2]. While the systems aren’t yet fully synchronized during live machining, the current setup significantly reduces simulation time and improves process planning.

As machining data is collected in real time, it can be stored in the cloud and later fed back into CAM software. This feedback loop helps programmers make more informed decisions, choosing the right tools and parameters for specific materials based on actual performance data. The result is a smarter, more efficient programming process that bridges the gap between simulation and execution.

Thus, while full integration is still evolving, CPS tools paired with CAM software are already transforming how manufacturers plan, simulate, and optimize their machining operations.

3.6 Integrated manufacturing with CPS, CNC, and CAM in Industry 4.0

This section explores how a CNC tool equipped with CPS capabilities can integrate with CAM software and CNC machines through a unified communication architecture. This technological trio – CPS-enabled tools, CNC machines, and CAM platforms – represents a major leap forward in transforming traditional manufacturing into a smart, connected ecosystem within the Industry 4.0 framework [31].

When real-time data exchange between the physical and digital worlds is enabled, CPS tools allow machines, software, and human operators to interact dynamically. This integration boosts productivity, reduces downtime, and improves decision-making across the production line [22].

The main purpose of Industry 4.0 in manufacturing is the creation of a smart factory to be a highly adaptive, efficient, and resilient production environment where machines, humans, and resources communicate [1]. In this setting, every manufacturing resource is interconnected in real time, enabling faster responses and smarter operations [32].

The combination of CNC machines, CAM software, and CPS-enabled tools creates a bridge between the physical shop floor and the digital cloud. Operators and managers gain real-time visibility into machining processes, allowing them to make informed adjustments and elevate traditional workflows into intelligent, data-driven manufacturing. Real-time process monitoring: traditional CNC tools lack the ability to report live data during machining, such as cutting temperature, surface roughness, or vibration. CPS-enabled tools, however, use embedded sensors to capture and transmit this data instantly. Operators can monitor critical parameters and intervene when needed, creating a dynamic feedback loop between the physical and virtual environments [23]. Also, machine health insights: conventional tools don’t provide direct feedback on machine condition. While external systems may offer diagnostics, CPS tools bring this capability into the tool itself. They monitor machine health during operation, detect anomalies, and trigger alerts or maintenance actions [23]. This aligns with Industry 4.0’s vision of synchronized data across factory and cyberspace [2, 28, 33]. Therefore, this change could improve the traditional manufacturing process, upscaling smart manufacturing processes in the machining area. The main adaptations/transformations are:

1. CPS-enabled CNC tools for real-time process monitoring:

   In conventional CNC machining, there is no inherent capability to provide feedback on process parameters such as cutting temperature, surface roughness, or vibration during execution. By contrast, a CNC tool integrated with CPS functionalities incorporates sensors that transmit real-time data to connected software systems. This allows operators and managers to observe critical performance indicators, make timely adjustments, and ensure process optimization. Such integration exemplifies the cyber-physical interaction central to the Industry 4.0 paradigm [2].

2. CPS-enabled CNC tools for machine condition monitoring:

   Traditional CNC tools also lack the capacity to generate direct feedback about the operational health of the machine. While auxiliary diagnostic technologies may exist, this functionality is not embedded within the CNC system itself. When CPS capabilities are implemented, CNC tools can autonomously relay information on machine condition, enabling early fault detection during machining operations and facilitating predictive maintenance strategies. This reflects the Industry 4.0 framework of merging physical machinery with synchronized digital environments [2, 28, 33].

3. Facilitating human interaction in real-time:

   In traditional CNC machining, human intervention is absent once the operation has commenced, with corrective action possible only after the machining cycle is completed. However, the integration of CPS-enabled CNC tools allows process-related information to be shared with both operators and machines in real time. In the event of anomalies or tool-related issues, the system can support immediate corrective interventions, thereby improving responsiveness and operational reliability.

4. Cloud-based storage of cutting tool data:

   Conventional CNC tools lack mechanisms for storing cutting process information in a centralized or easily accessible manner. In contrast, CPS-based CNC tools can seamlessly upload process data to cloud environments in real time. This enables the preservation of detailed machining records, supports knowledge reuse for future operations with similar characteristics, and provides a foundation for advanced IoT and data-driven applications. By leveraging cloud computing, large volumes of messages and datasets can be rapidly processed to enhance decision-making and efficiency.

5. Integration of CAM software with CPS-enabled CNC systems:

   Through the use of APIs, CPS-based CNC tool software can be directly connected to CAM systems, allowing virtual representations of CPS-enabled tools to be used in digital simulations of machining processes. Data entered by operators into CAM platforms can subsequently be transferred during postprocessing, ensuring consistency between virtual planning and physical execution. Such interoperability, supported by high-performance software, user-oriented interfaces, and digital networks, expands the functional capabilities of CNC manufacturing and reflects the broader transformation envisioned by Industry 4.0 [34].

4.1 Real time measurement

In modern CNC machining, the convergence of CPS and sensor-enabled tooling opens new possibilities for in-process measurement. CNC machines operate within a three-dimensional space, defined by the X, Y, and Z axes, and feeding movements are executed either individually or simultaneously across these axes [35]. Similarly, three-dimensional measuring machines, such as CNC coordinate measuring machines (CMMs), are built on the same spatial principles and are specifically designed to measure components directly on the production line, keeping pace with modern machining demands. A CMM is a device used to measure the physical geometrical characteristics of an object. This study proposes a shift toward embedded metrology, where CPS-enabled finishing tools perform dimensional checks during machining. These tools, equipped with precision sensors, could collect real-time data on part geometry while executing final cuts. Looking ahead, CPS-enabled CNC tools may soon offer measurement capabilities comparable to those of traditional CMMs [36]. Nowadays, the most common method for measuring a workpiece involves collecting dimensional data point-by-point using a probe sensor. However, we propose a shift in this approach.

Instead of relying on a probe, measurement could be performed directly by the CNC tool with CPS capabilities during the machining process. As the tool follows its programmed path, it could gather dimensional data in real time and transmit it to the CNC machine. Because measurement is a highly sensitive operation, it still requires a sensor, whether a probe or a CPS-enabled tool, to ensure accuracy.

We suggest using the finishing tool, which removes minimal material and refines the surface for this purpose. As it moves along its trajectory, it simultaneously collects measurement data. If the CNC software supports automatic correction, the machine could adjust its movements to compensate for any dimensional variations detected by the tool. This would eliminate the need for manual measurement and reduce downtime caused by machine stoppages.

Considering the limitations and validation of in-process measurement: While CPS-enabled finishing tools offer promising capabilities for in-process metrology, it is essential to recognize the inherent limitations of this approach. Measurement uncertainty can arise from factors such as tool deflection, thermal drift, and dynamic vibration during cutting operations. These variables may compromise dimensional accuracy, especially in high-precision applications [36]. To ensure reliability, a validation plan should be implemented using benchmark comparisons against CMMs, including gauge repeatability and reproducibility (R&R) studies [35]. In-process measurement is most appropriate during finishing operations where minimal material is removed, and thermal stability is higher. For roughing or interrupted cuts, traditional offline metrology may still be necessary to ensure compliance with tight tolerances [7].

Moreover, governance and safety protocols must be embedded into the system architecture. As CPS-enabled tools begin to capture real-time data during machining, including potential operator interactions, it is essential to establish clear protocols for privacy and consent. If operator data is collected, it must comply with data protection regulations, ensuring informed consent and secure handling. For in-process measurement, traceability and calibration must be guaranteed through certified sensor systems and documented verification routines [37]. In safety-critical operations, any automatic compensation triggered by the system should be subject to approval by a qualified operator or supervisor, with override mechanisms in place to prevent unintended consequences. These governance measures are vital to maintaining trust, safety, and accountability in smart manufacturing environments [38].

Historically, CNC machines have not performed in-process measurements because traditional tools lacked the ability to transmit measurement data during execution. Measurements were only possible through physical contact with a probe. Now, with the introduction of CPS-enabled tools capable of real-time data transmission, in-process measurement is becoming a viable reality. The integration of these capabilities requires enhancements to CNC software, enabling it to interpret sensor data and execute corrective actions autonomously. CAM platforms must also evolve to simulate measurement operations virtually, allowing programmers to preview metrology outcomes during process planning.

4.2 Flow information model

The integration of CPS-enabled CNC tools into machining workflows introduces a paradigm shift in how measurement data is captured, transmitted, and utilized. Rather than reiterating the foundational principles of Industry 4.0, which have been extensively covered in prior sections, this section focuses on the operational architecture and governance mechanisms that enable real-time measurement and adaptive control. During CNC machining, tools equipped with cyber-physical system capabilities can transmit real-time data on surface roughness, vibration, cutting forces, and temperature. Figure 2 illustrates a bidirectional information flow between CAM software, CPS-enabled tooling, and CNC machines. The model is structured around two core capabilities:

• Embedded metrology: the finishing tool, equipped with precision sensors, collects dimensional data during machining. This data is transmitted live to the CNC controller, enabling immediate feedback and potential compensation.

• Cloud-integrated feedback loop: measurement data is stored in the cloud and accessed via open APIs by CAM platforms. This allows programmers to refine tool selection and machining parameters based on empirical performance data.

To enhance process architecture, we propose two key dimensions: tool-enabled measurement and communication through CPS-integrated networks (Figure 2). Our proposal centers on using CPS-enabled CNC tools to measure the workpiece during machining. With the embedded sensors within the tool, dimensional data can be collected throughout the process. When the operation is complete, the workpiece will already be measured, eliminating the need for separate inspection steps and avoiding costly machine interruptions. The tool follows its programmed path along the X, Y, and Z axes, gathering measurement data and transmitting it to the CNC machine via integrated software and hardware.

Because measurement is a highly sensitive task, it requires precise sensors. We suggest using the finishing tool, which removes minimal material and refines the surface, for this purpose. As it moves, the tool’s software sends sensor data to the CNC system (Figure 2). To support this functionality, the CNC machine’s software must be upgraded to handle real-time data collection and automatic adjustments. This capability is essential for enabling automatic compensation, where the machine corrects deviations based on live feedback from the tool.

Therefore, both CNC machines and coordinate measuring systems operate within a three-dimensional space. This makes the integration of CPS-enabled tools with CNC software and 3D measurement systems a viable technological advancement. The tool and machine software must work together, exchanging measurement data continuously during execution.

It is important to note that many variables influence machining performance, such as material type, spindle speed (RPM), feed rate, and tool geometry. Therefore, the first step in implementing this proposal is to conduct measurement trials on CNC lathe machines during finishing operations. This initial phase will serve as a foundation for testing, validating, and refining the concept.

This model demonstrates how the continuous exchange of real-time data between physical machining tools and digital platforms facilitates more intelligent and autonomous decision-making during manufacturing operations. The CNC machine software would be capable of interpreting measurements dynamically as the three-dimensional machining process unfolds, automatically adapting to changes throughout execution in a manner comparable to the functionality of a coordinate measuring machine that responds in real time during finishing operations. Achieving this capability, however, necessitates significant advancements in current CNC software, which presently lacks the functionality to perform automatic corrections due to the unavailability of integrated in-process measurement tools.

To support this, a new measurement operation function could be introduced within CAM software. During process simulation in the digital (cyber) environment, programmers would be able to select this function to simulate in-process measurement virtually. This architecture supports closed-loop manufacturing, where simulation, execution, and validation are interconnected. The CAM environment simulates not only tool paths but also metrology operations, enabling predictive adjustments before physical execution.

As outlined in the model shown in Figure 2, the information flow begins with communication between CAM software and the CNC tool with CPS capabilities in the cyber world. The simulation process follows the same steps as current workflows until the CNC program is generated. The integration of CPS introduces new opportunities. Real-time data acquired by CPS-enabled tools during machining operations can be stored in cloud environments and subsequently leveraged to enhance the accuracy of future CAM simulations. By means of an open API, CAM software can access this data, supporting the programmer in the decision-making process by providing guidance on the most suitable tool selection as well as the optimal parameter configurations for specific workpiece materials.

Once the CNC program is sent to the machine for execution in the physical world, further innovations come into play. The CNC tool and machine work together, with sensors collecting data throughout the process. This data is stored in the cloud and shared with CAM software for future simulations. Additionally, it can be communicated to IoT (social networks), maintaining a continuous exchange of information with the CNC machine. A new feature of this system is its ability to connect with social networks through IoT. CPS-enabled CNC tools could interact with professional or dedicated networks, allowing users to discuss issues in real time or post them for collaborative feedback. This opens a new layer of communication and problem-solving within the manufacturing community.

The yellowish-brown fields in Figure 2 highlight the key differences between this proposed process and traditional workflows, illustrating how CPS integration transforms CNC machining into a smarter, more connected operation. To evaluate the effectiveness of this flow model, the following key performance indicators (KPIs) are proposed in Figure 3.

Figure 3 presents a set of KPIs designed to evaluate the effectiveness of the proposed information flow model. These indicators were selected to reflect the core functionalities and governance concerns introduced by CPS-enabled CNC tools. Measurement latency and compensation accuracy assess the system’s responsiveness and precision during live machining, while cloud feedback utilization gauges the impact of stored data on future CAM simulations. Governance compliance rate ensures that privacy and safety protocols are upheld in smart manufacturing environments. Together, these KPIs provide a structured approach to validating the model’s performance and its alignment with Industry 4.0 goals, offering a quantifiable path toward continuous improvement and accountability.