Computer Simulation of Three-Dimensional Chip Kinematics in Machining

The reliability of machining operation is an essential requirement in a modern production system. Chip control is one major aspect with regard to turning operation and becomes significant in industrial automation. At present, toolpath simulation is available in most computer aided manufacturing (CAM) systems. However, only the rendering scene of the stock material shape changing during cutting and the resultant part are shown. The chips formed in the machining process are not modelled or displayed in the simulation. To assess the chip control properly, many models and simulation methods were developed for predicting the chip forms produced in the cutting operation. If the chip forms can be predicted using some of the mathematical models, the design and planning of the machining operation can be greatly assisted, especially in the shop-floor level. Apart from chip form prediction, modelling of the machining process and chip formation are also issues that must be considered. To do this, a chip modelling and simulation system is proposed. Based on the machining parameters settings, the corresponding chip model can be calculated and simulated for more realistic analysis. The present paper first outlines the chip form mathematical model being used. The criteria to ensure that the theoretical model can be used to calculate the practical chip models effectively are described. Then the modelling of the chips and the simulation of the chip forming with computer graphic animation are explained. Several sets of turning parameters are used as examples to demonstrate how the corresponding chip models are calculated from the proposed system.

Recent Developments in Chip Control Research and Applications

During machining operations, user safety is a big factor need to consider. The more interaction of the user with the machine, the more the user is prone to accident due to mechanical or tool failures. Like in CNC systems, especially in milling and router machines, user interaction can be seen during manual tool change operations. This study presents the development of an automatic tool changer for the 3-axis computer numerically-controlled (CNC) router machine of the Metals Industry Research and Development Center (MIRDC). ATC reduces the cycle time of tool-change operations, thus, improving the machine productivity. Furthermore, ATC also ensures the user's safety during tool change operations. It allows complete machining where different toolings are required to make different operations like cut, engrave, drill and mill. This includes the use of a pneumatic or quick-change electrospindle and an ISO-20 tool holder system. The tool holder system makes the CNC router adaptable and flexible to different routing operations.

In this research, the working hours and posture of CNC (Computer Numerical Control) machineoperators were investigated to determine the physical workload that they experienced during theirdaily work. Based on our observations, the operator works 8-hour regular shifts or 12-hour longshifts for 5 – 6 days a week. During these shifts, the operators work in front of the CNC machinewhile standing upright without any tools to rest. Preliminary research was conducted to evaluatethese conditions the results show 13 out of 20 CNC machine operators complained about physicalfatigue while working the CNC machines. It can also be concluded that the operators areexperiencing excessive workload based on a workload assessment using the Quick Exposure Check(QEC) method. To solve this problem, a standing chair is designed with ergonomics in mind. Thedimensions of the standing chair are based on the anthropometry of the CNC machine operators,which was never used before. The design process is conducted using the Theory of InventiveProblem Solving (TRIZ) methodology. Need statements from the CNC machine operators anddesign requirements from the stakeholders are converted into design specifications using TRIZmethodology. Parameters are determined to increase the capability of the existing design based onthe design specifications. The result of this research is a standing chair that is suitable for theoperator’s needs. Based on the analysis of the design result and the stress analysis simulation, it canbe concluded a functional standing chair can be ergonomically designed with the TRIZmethodology.

This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).

This work aims to study the processing performance and optimize the CNC (Computer Numerical Control) turning process to reduce the environmental impact of CNC machining operations while maintaining efficiency and productivity. Experiments are conducted using an I-optimum design that takes into account four factors: spindle speed (x1), depth of cut (x2), feed rate (x3), and environment (x4) (wet and dry). The desirability function approach is used to evaluate the effects of these factors and optimize material removal rate (y1), surface roughness (y2), energy consumption (y3), and machining cost (y4). The optimal setting obtained is x1(250 rpm), x2 (1.5 mm), x3 (0.20 mm/min), and x4 (dry condition) and optimized responses are y1 (MRR) as 284.42 mm3/min, y2 (Ra) as 1.07 µm, y3 (energy consumption) as 1769.44 kJ, and y4 (total machining cost) as Rs. 80.47, respectively. The findings of the study are validated through confirmatory trials. By implementing the results of the study, manufacturers can reduce the environmental footprint of machining operations and thus contribute to the overall goal of achieving a more sustainable manufacturing industry.

The objective of all manufacturers is to produce high-quality products at reasonable prices. In high-volume mass production systems, optimizing the operation sequence (OS) not only reduces product cost but also increases production efficiency. An optimal OS can be achieved by trimming unnecessary segments of motion from the tool path (TP), which reduces production time. Currently, a computer numerical control (CNC) program can be generated for a given TP by the number of automatic programing packages (APPs) that are currently available. However, TP optimization is not included in most APPs. In this study, we propose genetic algorithms (GAs) to determine the optimal OS for the TP pass through a set of operations located asymmetrically in multiple levels and using one or more cutting tools. Then, the CNC program of the TP will be generated. The traveling salesman problem is introduced to define the OS problem. A GA based on the TSP formulation can determine the OS that achieves the shortest TP. The incorporation of a GA and TSP can be integrated into the APP for TP optimization before creating CNC programs.

The computer numerically controlled (CNC) machine is a state of the art technology that has revolutionized the manufacturing processes. The main objectives of the project described were to manufacture a basic study model having the capability of performing simple machining operations. A software-oriented machine was decided to be worked upon. The reasons for developing a software-oriented system were mainly its flexibility and economics. This CNC control system developed in Visual Basic works on Windows-95 and later. The simulations mainly depend upon the digital differential analyzer (DDA) method of interpolation. The linear and circular interpolations are calculated in this software control of the CNC machine. Screen animation is another main feature of the software control that enables the operator of the machine to control any mistakes of entries. The software accesses the interface card through parallel port. The program can easily be combined with a programmable graphics (PG) system relieving the part programmer from writing part programs.

Constant quality improvement through automation of production processes is an important prerequisite for increased viability of mining machines and plants. Factors that limit the automation of the cutting machining operations include the problem of controlling the chip formation and chip crushing. Solution of this problem necessitates theoretical description of the material cutting conditions for tools with curvilinear surfaces. The paper describes basic principles of modeling the cutting process using complex-geometry tools with curvilinear rake. The theory is based on the concept of chip formation as a process of inhomogeneous strain in the plastic zone where the chip originates. Based on the analysis of the stress-strain state in the cutting zone, criterial relationships were derived that correlate the geometric parameters of the chip shape and machining conditions of the curvilinear-rake tool. Prerequisites for chip breaking are stability of the chip shape during cutting, stable chip-to-obstacle contact, high chip stiffness and low flexibility. The machining conditions leading to chip fragmentation could be found by solving the strength problem. Through establishing the cause-and-effect relationships of the processes of chip formation, curling and breaking, new approaches to achieving favorable chip shape may be found by exerting deliberate impact on the plastic zone of the chip formation through optimizing the conditions for the chip flow off the tool. The established relationships between the output parameters of the cutting process and process conditions of cutting with a complex-geometry tool offer the way to control the chip flow parameters in various machining operations. The research is aimed at creating scientifically informed design codes and optimization of cutting parameters for tools with curvilinear chip-curling and chip-breaking rake surfaces.

The deformation conditions in machining of metals and alloys offer a unique route for materials processing with remarkable advantages over conventional deformation processes. The intense shear strain and high strain-rates in machining can be applied to form chips with controlled geometry. That is, the chip formation in machining can be used directly as a materials processing route wherein the chip becomes the product. The technical details for two of these processes — hybrid cutting-extrusion (HCE) and modulation-assisted machining (MAM) — are discussed and recent experimental results are presented. Both processes involve direct control of the shear-based deformation in machining. HCE applies an additional constraint in cutting which converts the otherwise uncontrolled chip thickness to a controlled format of specific size and shape. In HCE processing of sheet and strip, the deformed chip thickness is less than the deformed chip thickness in conventional cutting. The superimposed oscillation in MAM converts the otherwise continuous cutting process into a series of discrete cutting events. The control of the MAM and cutting conditions enable unique control of chip formation and the production of equiaxed, fiber, and platelet powder (particle) morphologies. The HCE and MAM processes demonstrate how chip control in machining can provide a route to applications opportunities in materials manufacturing.

Tool geometrical parameters had great influence on the cutting. The orthogonal finite element model was established. The model was used to simulate the influence of the geometrical parameters (rake angle, clearance angle, radius of tool tip) on the high speed cutting of aluminum alloy 7050T7451, and to investigate the influence of these parameters on the chip formation. In the same cutting condition, the cutting force experiment was carried out. A good agreement between cutting force of simulation and experiment was achieved. The results of simulation were proved to be correct. The study of geometrical parameters provided the base for the selection and optimization of tool in cutting of aluminum alloy 7050T7451.

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening.

This study presents a real-time remote monitoring system for computer numerical control (CNC) machining operations using MTConnect and a tool-order-based part detection algorithm. The system supports continuous data streaming, efficient storage, and real-time visualization to enhance process monitoring and part tracking. Unlike conventional methods that rely on M30 signals, the proposed approach identifies part production by analyzing tool transitions, making it effective even in batch operations and automated part loading (APL) environments. A comprehensive data pipeline was developed to collect and manage MTConnect data, supported by a relational database schema. An entity-relationship diagram (ERD) illustrates the organization of machine operation data, enabling efficient SQL queries for key parameters such as machine execution states and spindle load. SELECT query examples are provided to demonstrate how these parameters can be accessed for real-time monitoring using a web-based dashboard. The system’s effectiveness was validated by comparing algorithm-detected operations with operator shift reports, showing high accuracy. Analysis of spindle speed and Z-axis load profiles revealed consistent speed patterns across operations, while load variations highlighted potential indicators of tool wear, process anomalies, or part quality issues. This work contributes to scalable, data-driven monitoring for intelligent manufacturing.

Machining of titanium alloys is considered as difficult, because of their high temperature strength, low thermal conductivity and low E-modulus, which contributes to high mechanical loads and high temperatures in the contact zone between tool and workpiece. The generated heat in the cutting zone can be dissipated only in a low extent. When cutting steel materials, up to 75% of the process heat is transported away by the chips, contrary to only 25% when machining titanium alloys. As a result, the cutting tool heats up, which leads to high tool wear. Therefore, machining of titanium alloys is only possible with relatively low cutting speeds. This leads to low levels of productivity for milling processes with titanium alloys. One way to increase productivity is to use more cutting edges in tools with the same diameter. However, the limiting factor of adding more cutting edges to a milling tool is the minimum size of the chip spaces, which are sufficient for a stable chip evacuation. This paper presents experimental results on the chip formation and chip size influenced by high-pressure coolant supply, which can lead to smaller chips and to smaller sizes of the chip spaces, respectively. Both influences, the pressure of the supplied coolant and the volumetric flow rate were individually examined. Alpha-beta annealed titanium TiAl6V4 was examined in relation to the reference material quenched and tempered steel 42CrMo4+QT (AISI 4140+QT). The work shows that with proper chip control due to high-pressure coolant supply in milling, the number of cutting edges on the same diameter tool can be increased, which leads to improved productivity.

We are experiencing a trend of personal fabrication that allows non-experts to produce highly individualized objects. Beyond 3D printing, this maker movement also approaches larger-scale production machines such as computer numerically controlled (CNC) milling machines that are available in local fabrication laboratories (FabLabs). While the user interfaces and interaction techniques of small-scale 3D printers for household use adapted to the new requirements of non-experts in the last years, such an overhaul of the interfaces for larger machinery is still missing.
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\nIn this work, we explore the use of augmented reality methods to support novice users in the operation of CNC milling machines. As a first step towards better support for users, we provide a camera-supported graphical and easy-to-use interface for the measurement of raw workpieces inside the machine. In this paper, we contribute our concept CaMea alongside its' prototype implementation. We further report on the findings of a first early user study.