Cost-effective supervisory control system in peripheral milling using HSM

Vallejo, Antonio J.

doi:10.1016/j.arcontrol.2009.05.008

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…In the open literature, reported data on the effectiveness of the AE sensor in monitoring the tool condition are contradictory when it comes to the two suggested locations for mounting the AE sensor; either on the spindle or on the workpiece. However, it produces more reliable signals when mounted on the spindle due to the closeness to the signal source at the cutting zone and the short signal transmission path [ 43 , 44 ]. While AE sensors are relatively inexpensive and easy to integrate on the machine, they must be calibrated properly as the signal transmission path, the reflective surfaces between the cutting zone and the sensor, and the machine condition, can influence the quality of the AE signal [ 17 ].…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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“…In the current literature, analysis, control, and optimization problems at a particular manufacturing level have been studied for decades (see, for instance, References [5][6][7][8][9][10][11][12][13]). In recent years, an increasing number of studies that intend to integrate the analyses of a manufacturing system from multiple levels have been attempted.…”

Section: Integrated Models Of Manufacturing Systemsmentioning

confidence: 99%

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun

Bragin

et al. 2022

J Adv Manuf & Process

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In a manufacturing system, production control-related decision-making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation-based virtual testbed that implements dynamic optimization, automatic information exchange, and decision-making from the process-level, system-level, and factory-level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python-based numerical computation program, discrete-event simulation software (Simul8), and an optimization solver (CPLEX) via a third-party master program. The application of this simulation-based virtual testbed is illustrated by a case study in a machining shop.

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“…Among many other studies, T. Gao et al [9] developed a new method for the prediction of the machined surface topography in the milling process and Honeycutt and Schmitz [10] employed time domain simulation and experimental results for surface location error and surface roughness prediction. Vallejo and Morales-Menendez [11] used neural networks for modeling Ra in peripheral milling, with different input variables, such as feed per tooth, cutting tool diameter, radial depth of cut, and Brinell hardness. Zain et al [12] modeled surface roughness with cutting speed, feed rate and radial rake angle as input variables in peripheral milling, and Quintana et al [13] employed neural networks for studying average roughness in vertical milling.…”

Section: Introductionmentioning

confidence: 99%

Optimal Machining Strategy Selection in Ball-End Milling of Hardened Steels for Injection Molds

et al. 2019

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In the present study, the groups of cutting conditions that minimize surface roughness and its variability are determined, in ball-end milling operations. Design of experiments is used to define experimental tests performed. Semi-cylindrical specimens are employed in order to study surfaces with different slopes. Roughness was measured at different slopes, corresponding to inclination angles of 15°, 45°, 75°, 90°, 105°, 135° and 165° for both climb and conventional milling. By means of regression analysis, second order models are obtained for average roughness Ra and total height of profile Rt for both climb and conventional milling. Considered variables were axial depth of cut ap, radial depth of cut ae, feed per tooth fz, cutting speed vc, and inclination angle Ang. The parameter ae was the most significant parameter for both Ra and Rt in regression models. Artificial neural networks (ANN) are used to obtain models for both Ra and Rt as a function of the same variables. ANN models provided high correlation values. Finally, the optimal machining strategy is selected from the experimental results of both average and standard deviation of roughness. As a general trend, climb milling is recommended in descendant trajectories and conventional milling is recommended in ascendant trajectories. This study will allow the selection of appropriate cutting conditions and machining strategies in the ball-end milling process.

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Cost-effective supervisory control system in peripheral milling using HSM

Cited by 14 publications

References 15 publications

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Tool Condition Monitoring for High-Performance Machining Systems—A Review

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Optimal Machining Strategy Selection in Ball-End Milling of Hardened Steels for Injection Molds

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