{"title":"Employing the electrode of different diameters to join dissimilar Al-Cu thin sheets using resistance spot welding","authors":"Rajdev Singh, Amit Choudhary, Navneet Arora","doi":"10.1016/j.mfglet.2024.09.055","DOIUrl":"10.1016/j.mfglet.2024.09.055","url":null,"abstract":"<div><div>This study aims to clarify the intricate connection between using different electrode tip diameters and the quality of spot joints. By investigating basic principles and process parameters, the research highlights how various combinations of electrode sizes affect weld quality. Specifically, to join aluminum (Al) and copper (Cu), two electrode sizes were employed: 4 mm and 8 mm tip diameter. Given that copper has higher conductivity (398 W/mK) and a higher melting temperature (1085 °C) compared to aluminum (237 W/mK and 660 °C respectively), efforts were made to enhance current density towards the copper side by using the smaller electrode tip diameter (4 mm) on that side. Experiments were conducted using two different combinations of sheet thicknesses (0.5 mm and 1 mm), revealing the need for optimized electrode tip diameter combinations for varying sheet thicknesses and materials with different thermos-physical properties. Overall, this study seeks to deepen our understanding of resistance spot welding, specifically focusing on the importance, challenges, and future prospects associated with varying electrode tip diameters in joining dissimilar metals.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 457-461"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Surface insight: Leveraging high-density dataset fusion for enhanced roughness classification","authors":"Ronit Shetty, Ahmad Al Majali, Lee Wells","doi":"10.1016/j.mfglet.2024.09.022","DOIUrl":"10.1016/j.mfglet.2024.09.022","url":null,"abstract":"<div><div>The ability to assess the surface quality quickly and accurately is of immense importance in manufacturing system. Modern metrology system along with machine learning is great at classification but requires more time. Traditionally accessing surface roughness is a time-consuming process. The progress in manufacturing technology necessitates improved approaches for quality control, specifically in the categorization of surface roughness, which has a substantial impact on the performance of materials. This research study introduces a novel method for classifying surface roughness by combining image data and point cloud data to create a comprehensive model. It then compares the performance of this model with a model that just relies on image data. A comprehensive analysis is conducted in this study, where image and point cloud data is collected and analysed. Multilinear principal component analysis (MPCA) along with random forest classifier is employed to create a model that classifies the surface texture. The primary goal is to showcase the enhanced precision and comprehensive understanding offered by the fused data model compared to the model that solely relies on images.</div><div>Furthermore, the work presents a pragmatic approach for developing this enhanced model offline and applying it online in real-time production environments, with a particular focus on using only image data. This strategy is in line with the objectives of Industry 4.0, which seeks to achieve more intelligent and data-driven manufacturing processes. Subsequent investigations will prioritize expanding the model’s suitability to various manufacturing settings, particularly highlighting its capacity to ensure quality in manufacturing lines through the utilization of images.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 182-190"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao Tang, Yixuan Ye, Yu Zhang, Binghan Huang, Tao Huang, Dong Zhang, Xiaoming Zhang, Chang Ye
{"title":"Improving surface integrity of GH4169 alloy through magnetic-assisted cutting","authors":"Chao Tang, Yixuan Ye, Yu Zhang, Binghan Huang, Tao Huang, Dong Zhang, Xiaoming Zhang, Chang Ye","doi":"10.1016/j.mfglet.2024.09.077","DOIUrl":"10.1016/j.mfglet.2024.09.077","url":null,"abstract":"<div><div>GH4169 alloy presents superior properties such as high strength and resistance to high temperature, but possesses poor machinability. To ameliorate the problem and improve the machined surface integrity of GH4169 alloy, this paper focused on the application of magnetic-assisted cutting (MAC) for GH4169 alloy. In the MAC process, a permanent magnetic field (the magnetic field intensity is 0.25 T) was applied to the workpiece material during cutting, and its impact on chip morphology, tool damage and surface integrity was investigated. By comparing to traditional cutting (TC), the introduction of a magnetic field results in a reduction in the chip thickness and minimizes chip serration, leading to smoother cutting process and reduced fluctuations in cutting forces. Meanwhile, the introduction of magnetic field resulted in a substantial decrease in the notch wear and abrasion of cutting tool, and mitigated the excessive growth of built-up edge (BUE), which improved the tool life and machined surface integrity. By analyzing the machined surface at the end of TC and MAC, it was found that the surface roughness at the end of MAC was reduced by 22.4 %. Meanwhile, the cavity, side flow and debris of BUE, which tend to occur in the machined surface during the TC process, are effectively suppressed after MAC. Furthermore, Microstructural analysis of the machined surface indicated an enhancement in the dislocation density on the machined surface layer, suggesting the magnetoplastic effect of the magnetic field on GH4169 alloy.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 605-609"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian Lang , Sofia Talleri , Josef Mayr , Konrad Wegener , Markus Bambach
{"title":"Kalman filter-driven state observer for thermal error compensation in machine tool digital twins","authors":"Sebastian Lang , Sofia Talleri , Josef Mayr , Konrad Wegener , Markus Bambach","doi":"10.1016/j.mfglet.2024.09.025","DOIUrl":"10.1016/j.mfglet.2024.09.025","url":null,"abstract":"<div><div>Sustainable reduction of thermal errors during production is the key challenge in modern high-precision manufacturing. Numerical compensation models provide an energy-efficient solution, but in the case of data-driven models, high-quality experimental data must be time-consuming and expensive to produce, negatively impacting overall productivity. Furthermore, robustness concerns arise in the case of new operating conditions, which were not contained in the training data. This paper presents a novel use of a Kalman filter together with model order reduced finite element models to observe the entire thermal state, which allows the subsequent solution of the mechanical model and computation of the thermal errors in real-time without requiring any training data but instead purely based on the physical system model. The effectiveness of this approach is evaluated using experiments on a thermal test bench with 16 out of 40 temperature sensors employed for observation and demonstrated on a 5-axis machine tool (MT) with 13 out of 25 temperature sensors used. Due to the combination of the reduced order model and Kalman filter these 13 temperature sensors are sufficient to represent a MT mesh of more than 350’000 elements. The entire temperature profile of the thermal test bench is reconstructed to achieve a root mean square error (RMSE) of the unobserved temperature sensors of only 2.7 °C, which accounts for more than 83% of all temperature variations and 1.3 °C for the 5-axis MT. For the thermal error of the thermal test bench, the RMSE could be reduced from <span><math><mrow><mn>67.4</mn><mspace></mspace><mi>μ</mi><mtext>m</mtext></mrow></math></span> to <span><math><mrow><mn>33.3</mn><mspace></mspace><mi>μ</mi><mtext>m</mtext></mrow></math></span>, corresponding to a reduction of 52.7 %. This could be achieved without the need for experimental data for model calibration, in a real-time capable physics-based model.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 208-218"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Nanotechnology-enhanced castability of wrought aluminum alloys 2024 and 6063","authors":"Guan-Cheng Chen , Xiaochun Li","doi":"10.1016/j.mfglet.2024.09.033","DOIUrl":"10.1016/j.mfglet.2024.09.033","url":null,"abstract":"<div><div>In the realm of high-performance applications, wrought aluminum alloys are esteemed for their high mechanical properties and excellent strength-to-weight ratio. However, their limited castability poses challenges in economically producing intricate structures through casting processes. To address this issue, a small proportion of TiC nanoparticles is introduced into the melts of AA 2024 and AA 6063 for nano-treating. This nano-treatment imparts several beneficial effects, including the delayed release of latent heat, inhibition of grain growth, and improvement of wettability. These effects enhance the fluidity of the melt, eliminate hot cracking, and elevate the surface quality of the castings. The outcomes underscore the promising potential of emerging nano-treatment technology in rendering traditionally non-castable wrought aluminum alloys suitable for cost-effective casting processes, ultimately delivering high-performance products for a wide range of applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 281-286"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Austin Ngo , Noah Kohlhorst , Svitlana Fialkova , Bradley Jared , Tony Schmitz , Glenn Daehn , Jennifer L.W. Carter , Jian Cao , John J. Lewandowski
{"title":"Mechanical property improvements of LPBF-AlSi10Mg via forging to modify microstructure and defect characteristics","authors":"Austin Ngo , Noah Kohlhorst , Svitlana Fialkova , Bradley Jared , Tony Schmitz , Glenn Daehn , Jennifer L.W. Carter , Jian Cao , John J. Lewandowski","doi":"10.1016/j.mfglet.2024.09.072","DOIUrl":"10.1016/j.mfglet.2024.09.072","url":null,"abstract":"<div><div>Additive Manufacturing (AM) processes have versatile capabilities but are susceptible to the formation of as-cast non-equilibrium microstructures, process-induced defects, and porosity, which have deleterious effects on the mechanical performance. As part of our NSF-ERC-HAMMER program, isothermal forging was investigated as a novel post-processing technique for refining microstructure, reducing process defect severity, and thereby improving mechanical properties. Specimens of Laser Powderbed Fusion (LPBF) AlSi10Mg were fabricated over a range of process parameters and tensile tested as a baseline. Initial work focused on duplicate AM material that was then hot forged with 20 % strain to investigate the effects of isothermal forging at one temperature and strain rate on the microstructure, tensile, and fatigue properties of the as-deposited materials. The microstructures, process-induced defect populations, and tensile/fatigue properties of both as-deposited and forged materials were quantified and analysed by OM, EBSD, XCT, and SEM by various NSF-ERC-HAMMER team members. Isothermal hot forging was found to induce recrystallisation and modify process-induced defect geometry along with increasing tensile ductility. The effects of AM deposition parameters and forge post-processing conditions on LPBF AlSi10Mg will be discussed in terms of microstructure, mechanical properties, and fractography.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 568-574"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analytical temperature model for spindle speed selection in additive friction stir deposition","authors":"Tony Schmitz , Elijah Charles , Brett Compton","doi":"10.1016/j.mfglet.2024.09.090","DOIUrl":"10.1016/j.mfglet.2024.09.090","url":null,"abstract":"<div><div>This paper describes a physics-based, analytical model for additive friction stir deposition (AFSD) spindle speed selection to achieve a desired deposition temperature. In the model, power input to the feedstock, which enables plastic flow and deposition, is related to the material temperature rise and subsequent flow stress reduction using Fourier’s conduction rate equation. Power input is modeled as frictional heating at the deposit-surface interface and adiabatic heating due to plastic deformation. The flow stress is predicted using the strain, strain rate, and temperature-dependent Johnson-Cook constitutive model for the selected feedstock alloy. Model predictions are compared to AFSD numerical simulation results available in the literature and experiments for aluminum alloys.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 720-729"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Avik Samanta , Hrishikesh Das , Glenn J. Grant , Saumyadeep Jana
{"title":"Friction stir processing: A thermomechanical processing tool for high pressure die cast Al-alloys for vehicle light-weighting","authors":"Avik Samanta , Hrishikesh Das , Glenn J. Grant , Saumyadeep Jana","doi":"10.1016/j.mfglet.2024.09.061","DOIUrl":"10.1016/j.mfglet.2024.09.061","url":null,"abstract":"<div><div>This study uses friction stir processing (FSP) for thermomechanical processing of high-pressure die-casting (HPDC) to modify microstructure and improve mechanical properties. FSP is carried out on two different HPDC aluminum alloys: (a) general-purpose, high-iron, HPDC A380 alloy and (b) premium quality, low-iron HPDC Aural-5 alloy in thin wall, flat plate geometry. Subsequent mechanical testing shows ∼30 % and ∼65 % enhancement in yield strength and tensile ductility. In addition, FSP leads to ∼10 times improvement in fatigue life for A380 alloy and ∼70 % improvement in fracture toughness for Aural-5 alloy. These findings emphasize the capability of FSP to modify the microstructure of HPDC Al-alloys-based structural components so that they can demonstrate a good combination of strength, ductility, fracture toughness, and high fatigue properties for long-term durability and reliability.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 504-512"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarower Kabir , Shah Rumman Ansary , Yunze Li , Meng Zhang , Weilong Cong
{"title":"Rotary ultrasonic surface machining of silicon: Effects of ultrasonic power and tool rotational speed","authors":"Sarower Kabir , Shah Rumman Ansary , Yunze Li , Meng Zhang , Weilong Cong","doi":"10.1016/j.mfglet.2024.09.063","DOIUrl":"10.1016/j.mfglet.2024.09.063","url":null,"abstract":"<div><div>The surging demand for monocrystalline silicon materials in the production of microelectronic components highlights its crucial role in the semiconductor and optic industries. Hence it is inevitable to produce a silicon workpiece with high quality finish to meet the demand in semiconductor industries. Due to high brittleness, controlling the quality of silicon in surface machining is quite difficult. Traditional manufacturing processes induce issues like rough surfaces and edge chipping. It was reported that rotary ultrasonic surface machining (RUSM) can effectively reduce cutting force, roughness, and edge chipping in machining of brittle materials. There have been several studies on drilling and sliding silicon materials using rotary ultrasonic machining investigating the effects of machining parameters on the output variables such as cutting force, torque, edge chipping, surface roughness etc. However, to the best of the authors’ knowledge, there are no reported investigations on effects of machining variables (ultrasonic power and tool rotation speed) in surface machining of silicon materials using the rotary ultrasonic machining. This study aimed to investigate the impacts of ultrasonic power and tool rotation speed on the cutting force, edge chipping, and surface roughness. Experimental results show that the ultrasonic vibration and tool rotation speed had a notable impact on edge chipping and cutting forces. Lastly, the current research has paved the way for widening the research on investigating grinding of the silicon wafer in semiconductor manufacturing with ultrasonic vibration and high rotation speed. In semiconductor wafer manufacturing, grinding process is used to reduce the flatness but generate surface and subsurface damage. With further investigations, RUSM can contribute to reducing these damages.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 518-525"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Unsupervised detection and mapping of sparks in the Electrochemical Discharge Machining (ECDM) process","authors":"Prayag Gore, Yu-Jen Chen, Murali Sundaram","doi":"10.1016/j.mfglet.2024.09.052","DOIUrl":"10.1016/j.mfglet.2024.09.052","url":null,"abstract":"<div><div>Material removal in electrochemical discharge machining is caused by sparks generated in a tool immersed in an electrolytic solution. Being the primary machining agent in this non-contact machining process, mapping the locations of microscopic sparks is of great interest. The distribution of sparks around the tool surface could give insights into the machined hole properties like the size, surface finish, and depth as compared to the machining parameters such as applied voltage, tool size, rotation speed, and feed rate. This paper is focused on detecting sparks in photographs of the ECDM process captured using a high-speed camera. A novel approach of using a tri-planar reflective surface for capturing the location of sparks in 3D space using a 2D camera output is attempted. Traditional spark detection methods use neural network classifiers that need labeled data for training. This labeled data often comes from human intervention and contains inherent biases that could lead to misclassification. In this paper, an unsupervised spark detection methodology is demonstrated, which eliminates the need for human intervention and relies on the number of neighboring pixels detected in regions of interest (ROIs). The feasibility of using adaptive background modeling to classify thousands of images and identify the ones with sparks is demonstrated in this work. The masking technique combining effects of erosion followed by dilation is used to determine the exact boundaries of the spark contours in every image. Centroids for each of these contours are then transformed from the skewed coordinate system as observed in camera images, to a three-dimensional orthogonal coordinates system centered around the tool. The same procedure is repeated for various voltages to benchmark the distribution of sparks around a tool tip in an ECDM process.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 435-441"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}