Optimization of different parameters related to milling tools to maximize the allowable cutting depth for chatter-free machining

Mokhtari, Ali; Jalili, Mohammad Mahdi; Mazidi, Abbas

doi:10.1177/0954405420937536

Cited by 10 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chatter Avoidance [207] A chatter-free machining approach is developed to maximize the allowable cutting depth based on genetic algorithms. The method optimizes several tool parameters such as number of teeth, shank diameter, fluted section diameter, shank length, taper length, and length of fluted section Milling Online Chatter Avoidance [208] Constructing the transfer function of a spindle velocity controller by measuring the Frequency Response Function (FRF) of the system Drive motor current signals Milling…”

Section: Offlinementioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

View full text Add to dashboard Cite

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

show abstract

Section: Offlinementioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…optimization problems [75]. They have also been employed in the optimization of milling tools to maximize cutting depth for chatter-free machining, showcasing their effectiveness in addressing practical manufacturing challenges [76].…”

Section: Solution Approach-based Genetic Algorithmmentioning

confidence: 99%

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Leuveano,

Asih,

Ridho

et al. 2023

Journal of Robotics and Control

View full text Add to dashboard Cite

In Indonesia, the significant role of perishable products in food wastage has placed the country fourth globally in household food waste. Managing inventory for such products, with their short shelf life and stringent safety standards, emphasizes the need for efficient lot sizing planning. This study introduces a novel Dynamic Lot-Sizing (DLS) model, addressing perishable products and inventory constraints across multiple products, periods, and varying demands. The model aims to optimize production quantity and binary production, minimizing overall system costs. Employing a Genetic Algorithm (GA), this research solves the DLS model under constrained and unconstrained inventory capacities. Real-case data from a bread manufacturing company validates the model, while sensitivity analysis examines perishability's impact on the solution and model performance. The DLS-GA model not only reduces system costs but also effectively considers product perishability, offering optimal production plans.

show abstract

“…1 The most common surface roughness, form errors and dimensional errors after milling are the technical effect that need to be resolved during the process. 2,3 There are many factors that cause this case. The basis of these problems are the cutting forces that vary depending on the machining parameters and affect the tool.…”

Section: Introductionmentioning

confidence: 99%

Prediction of tool deflection using image processing in ball-end milling

Özdemir

Bahçe

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

In milling, some of the factors that contribute to the poor quality of products are the cutting forces. Depending on the machining parameters, the cutting forces may significantly affect the tool being used in the machining process. Tool deflection can be modeled as bending deformation. Tool deflection causes poor surface quality, geometrical and dimensional errors. For this reason, it must be addressed during milling and reduced by changing the machining parameters. In the determination of tool deflection, force-based analytical and finite element methods (FEM) and sensor measurement methods are widely used. These technologies have drawbacks such as not being able to obtain fast data, being expensive, demanding precise control, and requiring continual calibration. This study aims to determine the deflection of the tool by image processing dependent on the tool/material pair and machining parameters in the milling process. For this purpose, the AL7075 material with a free-form surface was machined on a CNC milling machine. A mathematical equation is proposed to estimate the tool deflection based on the image processing results. The method has shown that tool deviation can be detected more quickly and simply by image processing.

show abstract

Optimization of different parameters related to milling tools to maximize the allowable cutting depth for chatter-free machining

Cited by 10 publications

References 47 publications

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

Prediction of tool deflection using image processing in ball-end milling

Contact Info

Product

Resources

About