Machine Tools for Machining

Girsang, Irving P.; Dhupia, Jaspreet Singh

doi:10.1007/978-1-4471-4670-4_4

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…For example, in MRI machines or medical diagnostic equipment, worm gear screw jack gearboxes ensure smooth and reliable motion [29,30]. Hence, the screw shaft is considered the soul of the worm gear screw jack gearbox [31]. The screw shaft, often subjected to heavy loads and high wear, requires a material with high hardness and strength to ensure stable performance and prolonged lifespan.…”

Section: Case 2: Selection Of Materials For Manufacturing Screw Shaftsmentioning

confidence: 99%

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Dua,

Duc,

Bao

et al. 2024

Eureka: PE

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Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determined

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Section: Case 2: Selection Of Materials For Manufacturing Screw Shaftsmentioning

confidence: 99%

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Dua,

Duc,

Bao

et al. 2024

Eureka: PE

View full text Add to dashboard Cite

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“…In the current study, the back (α b ) and side (α s ) rake angles of the cutting tool along with the cutting depth (Figure 5) [23], were selected as variables during turning Polyamide PA66. The other tool geometries, i.e.…”

Section: Design Of the Experimentsmentioning

confidence: 99%

The Effect of Tool’s Rake Angles and Infeed in Turning Polyamide 66

HamaSur,

Abdalrahman

2023

Eng. Technol. Appl. Sci. Res.

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Polyamide PA66 has been adopted by a variety of industries, and engineering fields. It is used in machinery part production due to its good properties. Machining is the most commonly used processing technique when high quality of part dimension and surface is required. There is a lack of knowledge about the impact of the tool’s rake angles when turning polyamide PA66, therefore, this study aims to define an optimal condition that can provide the highest performance in machining polyamide PA66 at the lowest cutting force. The impact of the tool’s side rake angle, back rake angle, and cutting depth on cutting force was studied during turning polyamide PA66 with the HSS tool. Three levels were considered for each variable and Taguchi's Orthogonal Array (OA) was used to design nine test configurations. The tests were performed experimentally on a conventional lathe machine. The resultant cutting force was calculated as the response data. The values were converted to signal-to-noise (S/N) ratio to facilitate the analysis using the Taguchi method and analysis of variance (ANOVA). Accordingly, the cutting depth showed the greatest impact on cutting force (57.12%), followed by the side rake angle (27.9%) and back rake angle (8.21%). An optimal condition set to turn polyamide PA66 at the lowest cutting force (Fc) is identified as 1 mm depth of cut, side rake angle αs = 21°, and back-rake angle αb = 8°. Finally, the optimal condition set was evaluated by conformation tests, and the results agreed with the calculations to a large extent.

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“…In the manufacturing industry, the milling process plays a crucial role because of its flexibility in production [1]. The productivity, quality, and cost of the final product depend directly or indirectly on the lifespan of the cutting tool during the machining [2].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

et al. 2021

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An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

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Machine Tools for Machining

Cited by 11 publications

References 10 publications

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

Integration of objective weighting methods for criteria and MCDM methods: application in material selection

The Effect of Tool’s Rake Angles and Infeed in Turning Polyamide 66

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

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