Smart Machining Systems Research Articles

Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System

Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System

Hard Milling Process Based on Compressed Cold Air-Cooling Using Vortex Tube for Sustainable and Smart Manufacturing

Improving machining performance and meeting the requirements of sustainable production at the same time represents a major challenge for the metalworking industry and scientific community. One approach to satisfying the above challenge is to apply different types of cutting fluids or to optimise their usage during the machining process. The fact that cutting fluids are well known as significant environmental pollutants in the metalworking industry has encouraged researchers to discover new environmentally friendly ways of cooling and lubricating in the machining process. Therefore, the main goal is to investigate the influence of different machining conditions on the efficiency of hard machining and find a sustainable solution towards smart manufacturing. In the experimental part of the work, the influence of various machining parameters and conditions on the efficiency of the process was investigated and measured through the surface roughness, tool wear and cutting force components. Statistical data processing was carried out, and predictive mathematical models were developed. An important achievement is the knowledge of the efficiency of compressed cold air cooling for hard milling with the resulting lowest average flank wear of 0.05 mm, average surface roughness of 0.28 µm, which corresponds to grinding procedure roughness classes of N4 and N5, and average tool durability increase of 26% compared to dry cutting and conventional use of cutting fluids. Becoming a smart machining system was assured via technological improvement achieved through the reliable prediction of tool wear obtained by radial basis neural networks modelling, with a relative prediction error of 3.97%.

Reducing of process model uncertainty towards smart machining systems

This paper presents an approach of empirical modeling of cutting process physical phenomena with measurement uncertainty parameters accompanied to the model exponents/ /coefficients. The approach is presented trough an example of creating a power mathematical model for average cutting temperature in turning with details about the uncertainty contributions from different experimental plans. The approach is proposed to be implemented as usual practice during empirical modeling, in order the resulting models to fit with the needs of the smart machining systems and the needs of interoperability between researchers.

Corrigendum to “Development of smart machining system for optimizing feedrates to minimize machining time” [J. Comput. Des. Eng. 5 (2018) 299–304

Corrigendum to “Development of smart machining system for optimizing feedrates to minimize machining time” [J. Comput. Des. Eng. 5 (2018) 299–304

Development of smart machining system for optimizing feedrates to minimize machining time

Abstract Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.

Smart Adaptive CNC Machining - State of the Art

Condition monitoring is becoming popular in industry because of its efficient role in detecting potential failures. The use of condition monitoring techniques will generally improve plant production availability and reduce downtime cost. A reliable adaptive control system can prevent downtime of the machine or avoid unwanted conditions such as chatter vibration, excessive tool wear by allowing the optimum utilization of the tool life. To ensure the quality of machining products, reduce the machining costs and increase the machining efficiency, it is necessary to adjust the machining parameters in real time. A survey of actual researches is presented in this paper in purpose to define new directions of improvement of adaptive control towards smart machining systems.

Development of a smart machining system using self-optimizing control

In recent years, due to intense competition through globalization, most manufacturing companies have focused on increasing the added value and reducing the production costs of their products. Inevitably, this has led to the use of advanced technology to carry out manufacturing in an effective and efficient way. Monitoring and control of machining processes are becoming increasingly important for maintaining consistent quality in machined parts. The quality of product can be affected by disturbances during machining process. The paper presents a self-optimizing control system (SOCS) for smart machining that applies information science to enable next-generation quality control, in which the need for expensive post-process inspection is eliminated. In the smart machining system with SOCS, each machine is an autonomous entity. The machining system reacts to disturbances autonomously based on the reaction of each autonomous entity or the cooperation among them. In order to develop the SOCS, the disturbances that happened in the machining shop for manufacturing the clutch housing products were analyzed to classify them and to find out the corresponding management methods such as non-negotiation, negotiation, and rescheduling. To prove that the proposed SOCS is self-monitoring, self-adjusting as well as cooperation, a machining process related to tool conditions was considered in this paper. If the disturbance belongs to the non-negotiation type, for example the tool wear, the machine with SOCS adjusts the cutting parameters in consideration of the amount of tool wear to keep the quality of the machined part. In case the disturbance belongs to the negotiation type such as the tool wear exceeding the allowed limit or tool broken, ant colony inspired cooperation among machines is implemented to find out the most appropriate machine for carrying out the machining operation. The best solution is chosen based on the evaluation of pheromone values of the alternative machines in case many machines satisfy the requirements. The work of the machine in which the disturbance happens is performed at another machine in order to keep the machining system running. The experimental results prove that the mechanism of the proposed SOCS enables the system to adapt to the disturbances successfully.

Quality Oriented Self-Optimizing Control in Smart Machining Systems

In recent years, due to the hard competitiveness through globalization most manufacturing companies have focused on manufacturing higher value added products with low production costs. So, it is inevitable to apply the advanced technology to carry out it in effective and efficient way. Monitoring and control of machining processes are becoming increasingly important for keeping a consistent quality of machined parts. The paper presents in-process sensing and control techniques with applying information science for the next generation of quality control that eliminates the need for expensive post-process inspection. In this research, a machining process of machine tool is considered in terms of a quality oriented control. The self-optimizing control is proposed for enabling intelligent process monitoring and control which can detect the cutting conditions and status of machining operations. To prove the proposed self-optimizing control system, self-monitoring and self-adjusting, the machining process related to the tool condition such as tool wear was considered in this paper. The most crucial and determining factor to successful maximization of the manufacturing processes in any typical metal cutting process is tool wear. The evolutionary algorithm was developed to diagnose the tool wear. If the amount of tool wear is out of the allowed limit, the new cutting parameters should be generated and adjusted autonomously according to affecting the quality of the machined part through the tool wear.

Videooptical Surface Shape and Integrity Estimation in Robots Machining

Paper shows system to surface shape and quality control in machining using industrial robot. To surface control videooptical methods were used. Surface shape was controlled using the special reverse engineering system. To surface roughness measure machined surface reflectivity method was used. Used own constructions non contact system was equipped with red laser light and USB camera. Wrist of robot was equipped with grinding tool. In paper shows some algorithms of presented processes. Shown too examples of experiments results in surface roughness measure in start end of grinding process. First trials of presented system shows possibility to build smart machining system for finishing of surface with unknown shape.

A Smart Machining Systems application to reduce the machining costs within a multi-process production

Smart Machining Systems (SMS) provide new prospects to optimise the production of mechanical parts. In the case of mass production, 'Net-shape' products, realised by stamping, dieing, forging, etc., have low manufacturing costs. Nevertheless, additional shapes have to be realised by cutting. These machining operations are time consuming. In this context, the present contribution shows the possibility and the exploitation of the measurement devices integrated into a given machine tools to optimise a multi-process production within the framework of the SMS. For this, a realistic machining situation, where dimensions and tolerances must be respected during the turning of a groove, is studied. In the SMS approach developed, it is possible to guarantee the respect of the tolerance values of the design. The interest of this new approach is highlighted by comparison with a traditional turning procedure.

A module-based software system for spindle condition monitoring

Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.

Towards an information processing representation for dynamic process optimization in smart machining systems

Smart machining systems (SMS) are an important part of today’s manufacturing companies. SMS include the use of diverse technologies that once integrated together, provide the capabilities of: producing the first and every product correct; improving the response of the production system to changes in demand (just in time); realizing rapid manufacturing; and, providing data on an as needed basis. Each technology uses information with its own representation. In order to integrate the technologies an SMS must enable the seamless flow of information between each technology and the part of the SMS that optimizes the performance of machining processes. This paper reports on the initial developments of an information processing representation for dynamic process optimization of machining processes within a SMS.