Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS

Pawanr, Shailendra; Garg, Girish Kant; Routroy, Srikanta

doi:10.1016/j.procir.2020.01.036

Cited by 32 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many of the previous research works have shown that cutting process parameters have a significant effect on surface roughness [ 27 , 34 , 42 ]. The feed per tooth has a significant effect on the cutting process [ 38 ] and on surface roughness [ 27 , 34 , 36 , 42 , 46 ]; however, the cutting speed and depth of cut also significantly affect the surface quality, as shown in the works [ 36 , 42 , 52 ]. To limit the effect of cutting parameters on the results of analyses of the effects of face milling strategies on the surface roughness, one set of cutting process parameters was used in the experimental study.…”

Section: Methodsmentioning

confidence: 99%

“…Cutting edge wear during the cutting process and general cutting process can be assessed by monitoring cutting forces [34], whereas the monitored cutting forces can be used to predict the quality of the machined surface [35]. A lot of research works have been devoted to the analysis and optimisation of cutting process parameters to achieve low surface roughness levels [36][37][38], high dimensional accuracy [39], high process efficiency [40,41] and reduced energy consumption during the machining process [42]. Many previous works have also been devoted to different strategies for implementing cooling and lubrication of the cutting process of aluminium alloys and their effect on the surface quality after machining [43,44].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

et al. 2021

View full text Add to dashboard Cite

Lightweight alloys made from aluminium are used to manufacture cars, trains and planes. The main parts most often manufactured from thin sheets requiring the use of milling in the manufacturing process are front panels for control systems, housing parts for electrical and electronic components. As a result of the final phase of the manufacturing process, cold rolling, residual stresses remain in the surface layers, which can influence the cutting processes carried out on these materials. The main aim of this study was to verify whether the strategy of removing the outer material layers of aluminium alloy sheets affects the surface roughness after the face milling process. EN AW-6082-T6 aluminium alloy thin plates with three different thicknesses and with two directions relative to the cold rolling process direction (longitudinal and transverse) were analysed. Three different strategies for removing the outer layers of the material by face milling were considered. Noticeable differences in surface roughness 2D and 3D parameters were found among all machining strategies and for both rolling directions, but these differences were not statistically significant. The lowest values of Ra = 0.34 µm were measured for the S#3 strategy, which asymmetrically removed material from both sides of the plate (main and back), for an 8-mm-thick plate in the transverse rolling direction. The highest values of Ra = 0.48 µm were measured for a 6-mm-thick plate milled with the S#2 strategy, which symmetrically removed material from both sides of the plate, in the longitudinal rolling direction. However, the position of the face cutter axis during the machining process was observed to have a significant effect on the surface roughness. A higher surface roughness was measured in the areas of the tool point transition from the up-milling direction to the down-milling direction (tool axis path) for all analysed strategies (Ra = 0.63–0.68 µm). The best values were obtained for the up-milling direction, but in the area of the smooth execution of the process (Ra = 0.26–0.29 µm), not in the area of the blade entry into the material. A similar relationship was obtained for analysed medians of the arithmetic mean height (Sa) and the root-mean-square height (Sq). However, in the case of the S#3 strategy, the spreads of results were the lowest.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Hu et al improved the feature cutting model based on the energy consumption of the machine tool standby state and feed motion, and comprehensively considered the influence of the feature machining sequence on the actual cutting area [7]. Other scholars have established energy consumption models of machine tools through different optimization methods, effectively predicting the energy consumption status of machine tools in the process of machining parts, and providing basis for part design and process optimization [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Energy consumption modeling and optimization of part machining process based on feature

Song

Zhou²

2023

2022 Workshop on Electronics Communication Engineering

View full text Add to dashboard Cite

Aiming at the influence of the part manufacturing process energy consumption by the process sequence and the choice of the machine tool, the energy consumption modeling and optimization of part machining process based on feature was proposed. The concept of feature process element was proposed, incorporating feature combinations on the basis of comprehensive single feature description, and the corresponding sorting rules were established. Analyzed the machining capability and status information of machine tools, the model of the cutting energy consumption of the machine tool equipment and the energy consumption of the equipment empty cutting between the two features was established. Based on the correlation analysis of the direct and indirect energy consumption of the manufacturing process and the part feature process elements was carried out, the energy consumption correlation model of the part processing process was established, and the depth-first search algorithm was used to optimize the model. Taking a box part as an example to conduct application analysis, the feasibility and effectiveness of the above models and methods were verified.

show abstract

“…Menghasilkan produk sesuai kualitas yang ditentukan dengan menggunakan pahat dengan tingkat keausan minimal merupakan salah satu tujuan utama kegiatan produksi menggunakan mesin perkakas. Tujuan ini dipengaruhi oleh sejumlah variabel seperti kecepatan potong, kedalaman potong, dan gerak makan (Prasetyo, 2015;Giyatno 2009;Sriyanto, et al 2015), serta cairan pendingin dan sudut ujung pahat (Sunengsih, et al 2017) dan juga konsumsi energi (Pawanr, et al 2019).…”

unclassified

“…Selanjutnya Prasetyo (2015) melakukan optimasi kecepatan potong, kedalaman potong, dan gerak makan mesin bubut terhadap kekasaran permukaan dan keausan pahat. Demikian pula halnya Pawanr, et al (2019) melakukan optimasi kecepatan potong, kedalaman potong, dan gerak makan mesin bubut untuk minimasi kekasaran permukaan dan konsumsi daya.…”

unclassified

Optimasi Parameter Pemesinan Untuk Minimasi Keausan Pahat Pada Pembubutan Baja Karbon Rendah

Fahrizal

Suprapto

Priyono

et al. 2022

JPTM

View full text Add to dashboard Cite

AbstrakMetode optimasi merupakan salah satu metode yang digunakan untuk pengambilan keputusan dengan fungsi tujuan minimasi atau maksimasi. Optimasi kecepatan potong, kedalaman potong, dan gerak makan mesin bubut bertujuan untuk mencari respon yang optimal dengan melakukan kombinasi antar parameternya. Penelitian ini bertujuan untuk mendapatkan kombinasi parameter pemotongan optimal untuk meminimalkan keausan pahat pada proses pembubutan baja karbon rendah. Tujuan penelitian selanjutnya adalah mendapatkan urutan parameter pemotongan yang berpengaruh signifikan terhadap minimasi keausan pahat. Mesin bubut yang digunakan dalam penelitian adalah HMT type L17, sedangkan pahat bubut dari bahan HSS (High Speed Steel). Penelitian dilaksanakan Laboratorium Pemeliharaan dan Pengujian Material Politeknik Negeri Kupang. Optimasi parameter pemotongan menggunakan metode Taguchi dengan matriks orthogonal L9(33). Persentase kontribusi masing-masing parameter menggunakan ANOVA (Analysis of Variance). Hasil penelitian diperoleh bahwa keausan pahat minimal diperoleh pada kombinasi jumlah putaran 180 rpm, kedalaman potong 1,5 mm, dan gerak makan 0,8 mm/menit. Berdasarkan hasil analisis variasi diperoleh kontribusi setiap faktor terhadap keausan pahat, masing-masing gerak makan memberikan kontribusi terbesar yaitu 35%, kemudian disusul kedalaman potong 10%, dan faktor kecepatan potong tidak memberikan kontribusi.Kata kunci: Parameter pemotongan, Metode Taguchi, ANOVAAbstractOptimization is one of the methods used for decision making with the objective function of minimization or maximization. The optimization of cutting speed, depth of cut, and feeding of turning aim to find the optimal response by combining the parameters. This study aims to obtain the optimal combination of cutting parameters to minimize tool wear in the low carbon steel turning process. The purpose of further research was to obtain a sequence of cutting parameters that have a significant effect on minimizing tool wear. The lathe used in this research is HMT type L17, while the lathe is made of HSS (High Speed Steel). The research was carried out at Kupang State Polytechnic Materials Testing and Maintenance Laboratory. Optimizing the cutting parameters used taguchi method with an orthogonal matrix L9(33). The percentage contribution of each parameter used ANOVA (Analysis of Variance). The results showed that minimal tool wear was obtained at a combination of 180 rpm rotation, 1.5 mm depth of cut, and 0.8 mm/minute of feeding. Based on the results of the analysis of variation, it was found that the contribution of each factor to tool wear, i.e. feeding gave the largest contribution, namely 35%, followed by the depth of cut 10%, and the cutting speed did not contribute.Keywords : Cutting parameters, Taguchi method, ANOVA DAFTAR PUSTAKAAriani, D.W. (2003). Pengendalian Kualitas Statistik Pendekatan Kuantitatif dalam Manajemen Kualitas. Yogyakarta: Penerbit AndiApreza, S., Kurniawan, Z. & Subhan, M. (2017). Optimasi Kekasaran Permukaan Proses Pembubutan Baja ST. 42 dengan Menggunakan Metode Taguchi. Jurnal Manutech, 9(1): 74-85Chukwu, W.I.E. & Augustine, S. (2016). Optimization of Cutting Parameters of Turning Process in Engineering Machining Operation Using a Geometric Programing Approach. International Journal of Advanced Engineering Research and Technology, 4(11): 335-351Djami, R.J. & Sunaryo. (2014). Metode PCR-TOPSIS untuk optimasi Taguchi Multirespon. Jurnal Statistika, 2(1): 46-55Giyatno (2009). Optimasi Parameter Proses Pemesinan terhadap Keausan Pahat dan Kekasaran Permukaan Benda Hasil Proses CNC Turning dengan Menggunakan Metode Taguchi. Tesis tidak diterbitkan. Semarang: Prodi Teknik Mesin Universitas DiponegoroGupta, M., & Kumar, S. (2013). Multi-Objective Optimization of Cutting Parameters in Turning Using Grey Relation Analysis. International Journal of Industrial Engineering Computations, 4(4): 547-558Mahendra, R.A., Anggara, M.R.A., Kapel, K.E. & Miftahuddin, A.M. (2020). Analisa Efektifitas Uji Kekasaran Permukaan Baja ST 42 dengan Variabel Mekanik Mesin dengan Metode Taguchi. Jurnal JMMME, 1(1): 5-8Nakaminami, M., Tokuma, T., Moriwaki, T., & Nakamoto, K. (2007). Optimal Structure Design Methodology for Compound Multiaxis Machine Tool-I-Analysis of Requirements and Specification. Paper Manufacturing Technology Department. MORI SEIKI CO. LTDPrasetyo, A.B. (2015). Aplikasi Metode Taguchi pada Optimasi Parameter Permesinan terhadap Kekasaran Permukaan dan Keausan Pahat HSS pada Proses Bubut Material ST 37. Mekanika, 13(2): 86-97Pawanr, S., Garg, G.K., & Routroy, S. (2019). Multi-objective optimization of machining parameters to minimize surface roughness and power consumtion using TOPSIS. Procedia 7th CIRP Global Web Conference, 86: 116-120Rochim, T. (1993). Teori dan Teknologi Proses Pemesinan. Bandung: Institut Teknologi BandungSudjana. (1991). Desain dan Analisis Eksperimen. Bandung: Penerbit TarsitoSchey, J.A. (2009). Proses Manufaktur. Edisi Ketiga. Yogyakarta: Penerbit AndiSriyanto, Faisal, & Parkhan, A. (2015). Pengaruh Geometri Pahat Bubut HSS terhadap Nilai Kekasaran Permukaan dan Keausan Pahat Proses Bubut dengan Metode Taguchi. Jurnal Teknika, 13: 35-46Soejanto, I. (2019). Desain Eksperimen dengan Metode Taguchi. Surabaya: Graha IlmuSunengsih, N., Winarni, S. & Amazaina, T.G. (2017). Kajian Terhadap Metode Taguchi-TOPSIS pada Optimasi Multirespon. Seminar Satistika FMIPA Unpad. Bandung: Universitas PadjajaranSchonmetz, A. & Gruber, K. (2013). Pengetahuan Bahan dalam Pengerjaan Logam. Bandung: Penerbit Angkasa.Sularso & Suga, K. (2013). Dasar Perencanaan dan Pemilihan Elemen Mesin. Jakarta: PT. Pradnya ParamitaTriastuti, W. (2009). Metode Taguchi untuk Optimalisasi Produk pada Rancangan Faktorial. Media Statistika, 2(2): 81-92Wibowo, D.B., Soepangkat, B.O., & Pramujati, B. (2013). Optimasi Gaya Potong, Kekasaran Permukaan, dan Keausan Tepi Pahat pada Proses Bubut Baja S45C dengan Menggunakan Kombinasi Metode Taguchi dan Grey Relation Analysis. Seminar Nasional. Surabaya: Institut Teknologi Surabaya

show abstract

Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS

Cited by 32 publications

References 18 publications

Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

Energy consumption modeling and optimization of part machining process based on feature

Optimasi Parameter Pemesinan Untuk Minimasi Keausan Pahat Pada Pembubutan Baja Karbon Rendah

Contact Info

Product

Resources

About