A concise review of uncertainty analysis in metal machining

Panda, Amlana; Sahoo, Ashok Kumar; Kumar, Ramanuj; Das, Diptikanta

doi:10.1016/j.matpr.2020.02.365

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…To confirm the efficacy of the Zende's-TOPSIS method, a confirmation experiment was performed on the identified optimal conditions (refer to table 10). For example, for experimental trial 1 and for maximization, the identified optimal conditions were: O(3), S(1), and RP (7). These optimal conditions were kept as constant in the confirmation experiment, and for these conditions, again, measurements of five hole diameters were carried out.…”

Section: Resultsmentioning

confidence: 99%

“…The study shows that this method is effective by conducting consistency evaluations, which ensure that the estimation of uncertainty in CMM measurements is reliable. Panda et al [7] explained that advancements in precision machining, specifically in hard part turning, have successfully resolved issues related to the accuracy of orientation and position during machine operations. This advancement is especially beneficial for the automotive sector and roller bearings.…”

Section: Introductionmentioning

confidence: 99%

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A novel Zende’s-TOPSIS method towards estimation of measurement uncertainty in hole diameters

Zende,

Pawade

2024

Eng. Res. Express

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The 'Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)' is one of the best methods for 'Multi-Criteria Decision-Making' and 'Multi-Objective Optimization'. The traditional TOPSIS method determines the best alternative under fixed conditions. However, it cannot determine the best upper limit and lowest limit values. This work explains the detailed methodology of the newly developed Zende's-TOPSIS method which was used to estimate the measurement uncertainty in hole diameters. Four identical holes and one center hole in an industrial component were measured to investigate measurement uncertainty. According to the experimental results, Zende's-TOPSIS method performed better than the traditional TOPSIS method. The percentage improvement in the Zende's-TOPSIS method over the traditional TOPSIS method ranges from 0.0209% to 0.3053%. Using Zende's-TOPSIS method, the percentage maximum measurement uncertainty for four identical holes varies from 0.8067% to 1.0222%, whereas for the center hole, it varies from 0.5261% to 0.5576%. Similarly, the percentage minimum measurement uncertainty for four identical holes varies from 0.3839% to 0.6406%, whereas for the center hole, it varies from 0.4014% to 0.4041%. The proposed method is also capable of estimating the machined tolerances of the component, which ranges from 18.0772 mm to 18.1708 mm for four identical holes and 49.2215 mm to 49.2572 mm for the center hole. The proposed method can solve various 'Multi-Objective Optimization' problems.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel Zende’s-TOPSIS method towards estimation of measurement uncertainty in hole diameters

Zende,

Pawade

2024

Eng. Res. Express

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“…The estimated net cutting force

μ_{F_{i}}

is used to generate an instance of the net cutting force

F_{i}

, which is randomly drawn from a normal random variable

N \sim (μ_{F_{i}}, σ_{F})

, where

σ_{F} = 20

N per our discussions from our industry partner and from literature. [ 48 ] This is done in order to model the uncertainties in the milling machine and underlying processes, such as variations in the workpiece geometry, coolant, the placement of the cutting tool with respect to the workpiece, and so forth. These uncertainties are significant since, like the measured tool wear uncertainty, it could exacerbate the running costs due to the modeling inaccuracies in the optimization scheme for determining the control configurations.…”

Section: Resultsmentioning

confidence: 99%

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Wilson

Shen

Awasthi

et al. 2022

J Adv Manuf & Process

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With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost-efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi-objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi-objective formulation. The optimization framework is tested on a high-fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to 15% as compared to the default control scheme.

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“…Prediction of cutting forces is hard, due to the number of factors that are involved in the machining process, such as tool deflection, material, and machine configuration. An appropriate prediction of cutting forces is a key factor, especially when optimizing a machining process [20]. There is a growing need for these processes, as the complexity of some machining processes, especially five-axis machining, presents some problems, as the everchanging geometrical parameters of the cutting area.…”

Section: Cutting Force Prediction Methodsmentioning

confidence: 99%

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Sousa

Silva

Fecheira

et al. 2020

Sensors

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Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works.

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A concise review of uncertainty analysis in metal machining

Cited by 6 publications

References 32 publications

A novel Zende’s-TOPSIS method towards estimation of measurement uncertainty in hole diameters

A novel Zende’s-TOPSIS method towards estimation of measurement uncertainty in hole diameters

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

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