Remanufacturing and Advanced Machining Processes for New Materials and Components

Gevorkyan, É. S.; Rucki, Mirosław; Nerubatskyi, Volodymyr; Żurowski, W.; Siemiątkowski, Zbigniew; Morozow, Dmitrij; Kharatyan, A. G.

doi:10.1201/9781003218654

Cited by 10 publications

(4 citation statements)

References 340 publications

(538 reference statements)

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“…In the present study, the initial powder materials were portioned in a way that provides the final phase composition with proportions 60 wt.% of Al 2 O 3 , 25 wt.% of mullite, and 15 wt.% of ZrO 2 . Moreover, previous studies on alumina–zirconia–mullite composites, as well as research on sintering of alumina and zirconia, reported better sintering ability and enhanced properties of this composite in the abovementioned proportion [ 33 , 34 , 35 ].…”

Section: Theoretical Backgroundmentioning

confidence: 91%

Peculiarities of the Phase Formation during Electroconsolidation of Al2O3–SiO2–ZrO2 Powders Mixtures

et al. 2022

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This paper is devoted to the sintering process of Al2O3–SiO2–ZrO2 ceramics. The studied method was electroconsolidation with directly applied electric current. This method provides substantial improvements to the mechanical properties of the sintered samples compared to the traditional sintering in the air. The research covered elemental and phase analysis of the samples, which revealed phase transition of high-alumina solid solutions into mullite and corundum. Zirconia was represented mainly by tetragonal phase, but monoclinic phase was present, too. Electroconsolidation enabled samples to reach a density of 3.0 g/cm3 at 1300 °С, while the sample prepared by traditional sintering method obtained it only at 1700 °С. For the composite Al2O3—20 wt.% SiO2—10 wt.% ZrO2 fabricated by electroconsolidation, it was demonstrated that fracture toughness was higher by 20–30%, and hardness was higher by 15–20% compared to that of samples sintered traditionally. Similarly, the samples fabricated by electroconsolidation exhibited elastic modulus E higher by 15–20%. The hypothesis was proposed that the difference in mechanical and physical properties could be attributed to the peculiarities of phase formation processes during electroconsolidation.

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Section: Theoretical Backgroundmentioning

confidence: 91%

Peculiarities of the Phase Formation during Electroconsolidation of Al2O3–SiO2–ZrO2 Powders Mixtures

et al. 2022

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“…Prosedur pembuatan terlibat langsung dengan modifikasi bentuk, dimensi, dan kualitas tertentu suatu bagian. Pemotongan, penggilingan, pembengkokan, penempaan, perlakuan panas, pengeleman, mematri, pengelasan, kebiruan, pengelasan difusi, etsa industri, elektrolisis, pengerasan dalam dan permukaan, pengelasan eksplosif, pemotongan jet air, peledakan pasir, dan pengolahan arus frekuensi tinggi, semuanya dapat digunakan untuk mencapai tujuan [3].…”

Section: Pendahuluanunclassified

Proses pembuatan gate hollow dengan menggunakan mesin bor radial Csepel tipe RF 22/B

Umary,

Supriyatna

2023

JTMI

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Proses pemesinan atau dapat disebut proses manufaktur adalah suatu proses produksi bahan mentah ataupun bahan yang sudah jadi diubah menjadi barang yang diinginkan. Salah satu proses yang ada pada industri manufaktur adalah proses bor (drilling). Tujuan dari kajian ini adalah mengetahui proses penggunaan mesin bor radial dalam membuat gate hollow dan mengetahui kesalahan apa saja yang terjadi ketika proses pembuatan gate hollow berlangsung. Kajian ini menggunakan metode studi literatur dan kajian lapangan (field research). Hasil menunjukkan berdasarkan keseluruhan proses pembuatan gate hollow, maka didapatkan sebuah kesalahan dalam proses pembuatan gate hollow mengenai diameter lubang yang seharusnya 15 mm menjadi 18 mm dan terdapat kesalahan antara jarak lubang yang sudah dibuat.

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“…This human interaction in analyzing and interpreting such reports can cause unpredictability in the manufacturing process. Besides that, the ability to control all the machining process variables has surpassed human limits, delegating to machines and intelligent systems a good part of the tasks that ensure precise manufacturing [5].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the complex interrelationships between various factors that compose the journal-bearing housing manufacturing process, techniques based on machine learning have become essential tools due to their speed, robustness, and non-linear characteristics when working with data. These techniques sustain the intelligent utilization of manufacturing resources and the adaptive adjustment of manufacturing processes that support an organization called smart factories [5].…”

Section: Introductionmentioning

confidence: 99%

Decision tree predictive model for dimensional control of side flange bearing housings

Soares

Dalpra

Silva

2023

Int J Adv Manuf Technol

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This paper introduces a prediction model based on machine learning techniques for dimensional control in the manufacturing process of side ange bearing housings, according to the technical standard DIN 31693. The process is implemented in a journal-bearing manufacturing industry positioned among the three brands with the highest participation in the international market in 2023. The manufacturing process consists of rigid machining processes composed of a universal horizontal machining center and dimensional control composed of a coordinate measuring machine. After machining, the piece is measured, and its dimensional report is generated. Quali ed professionals use deviations obtained from this report to support the decision-making. The method used is based on the holistic monitoring of the surface geometry of the machined piece. The approach used to compensate for dimensional deviations is based on monitoring and modeling the total deviation. In this context, the effects of all sources of systematic errors are compensated regardless of their origin. The heuristic is used for the steps that make up the decision-making process. The way to implement the predictive model in the production line is based on the interaction between human and machine experience. This paper proposes using the regression decision trees for de ning the displacement parameters of the machining center axes from the dimensional results of housings obtained in the coordinate measuring machine. The model is validated if the mean absolute error is less than or equal to 0.003mm. A comparison between an assembled model is performed to verify the performance between different predictive models.

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Remanufacturing and Advanced Machining Processes for New Materials and Components

Cited by 10 publications

References 340 publications

Peculiarities of the Phase Formation during Electroconsolidation of Al2O3–SiO2–ZrO2 Powders Mixtures

Peculiarities of the Phase Formation during Electroconsolidation of Al2O3–SiO2–ZrO2 Powders Mixtures

Proses pembuatan gate hollow dengan menggunakan mesin bor radial Csepel tipe RF 22/B

Decision tree predictive model for dimensional control of side flange bearing housings

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