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Advanced Manufacturing and Machining Processes

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A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 50181

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Special Issue Editors

Dr. Alborz Shokrani

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Guest Editor

Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK
Interests: innovative machining processes; hybrid and assisted machining; difficult-to-machine materials; cooling and lubrication
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dirk Biermann

E-Mail Website
Guest Editor

Institute of Machining Technology, TU Dortmund University, Dortmund, Germany
Interests: all relevant machining processes; the application of methods from computer science in machining

Special Issue Information

Dear Colleagues,

Machining is one of the most used manufacturing processes for realizing precision and functional components used in various applications, such as in the aerospace, automotive, medical implants, space, and oil and gas industries.

Meeting the 21st-century requirements for green manufacturing together with the economical demands for higher productivity and lower costs can be challenging, especially when machining advanced alloys and composite materials with engineered thermomechanical properties. Numerous innovations have taken place in recent years in terms of (i) cutting tool design, materials, and coatings; (ii) applications of computational methods for machining, such as finite element analysis (FEA) and computational fluid dynamics (CFD); (iii) novel cooling and lubrication techniques; (iv) smart machining using sensor-based technologies and machine learning approach; and (v) hybrid and assisted machining, such as laser-assisted machining, ultrasonic assisted machining, etc. Geometrical accuracy and surface integrity are paramount in high-precision and high-performance manufacturing, and the challenges of finish machining of additively manufactured materials have also attracted the interest of many researchers in recent years.

The aim of this Special Issue is to collate the state of the art and showcase recent innovations in advanced machining processes. This Special Issue invites papers in the area of advanced manufacturing and machining processes with a specific focus on the abovementioned areas with the objective to achieve the following:

Provide an in-depth analysis of recent innovations in machining techniques, such as ultrasonic machining, novel cooling/heating/lubrication techniques, and novel tool designs;
Provide an insight into the mechanics of material cutting for advanced alloys and composites and their interactions with other parameters in a machining system, including the use of CFD and FEA;
Report on advances in sensor-based data collection and control and the application of machine learning in manufacturing and specifically machining processes;
Deliver detailed investigations on the impact of recent advances on the sustainability and environmental aspects of manufacturing processes.

Dr. Alborz Shokrani
Prof. Dr. Dirk Biermann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Advanced machining
Difficult-to-machine materials
Simulation
Environmentally conscious manufacturing

Published Papers (13 papers)

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Editorial

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3 pages, 176 KiB

Open AccessEditorial

Advanced Manufacturing and Machining Processes

by Alborz Shokrani and Dirk Biermann

J. Manuf. Mater. Process. 2020, 4(4), 102; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4040102 - 27 Oct 2020

Cited by 1 | Viewed by 2284

Abstract

Manufacturing is one of the major sections of the economy along with services, construction and agriculture [...] Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

Research

Jump to: Editorial

26 pages, 14821 KiB

Open AccessArticle

Real-Time Laser Tracker Compensation of Robotic Drilling and Machining

by Zheng Wang, Runan Zhang and Patrick Keogh

J. Manuf. Mater. Process. 2020, 4(3), 79; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4030079 - 06 Aug 2020

Cited by 33 | Viewed by 5905

Abstract

Due to their flexibility, low cost and large working volume, 6-axis articulated industrial robots are increasingly being used for drilling, trimming and machining operations, especially in aerospace manufacturing. However, producing high quality components has demonstrated to be difficult, as a result of the inherent problems of robots, including low structural stiffness and low positional accuracy. These limit robotic machining to non-critical components and parts with low accuracy and surface finish requirements. Studies have been carried out to improve robotic machine capability, specifically positioning accuracy and vibration reduction. This study includes the description of the hardware, software and methodologies developed to compensate robot path errors in real time using a single three-degrees-of-freedom (DOF) laser tracker, as well as the experimental results with and without compensation. Performance tests conducted include ballbar dynamic path accuracy test, a series of drilling case studies and a machining test. The results demonstrate major improvements in path accuracy, hole position accuracy and hole quality, as well as increases in accuracy of a machined aluminum part. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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14 pages, 3616 KiB

Open AccessArticle

Electrohydrodynamic Atomization for Minimum Quantity Lubrication (EHDA-MQL) in End Milling Ti6Al4V Titanium Alloy

by Andrea De Bartolomeis and Alborz Shokrani

J. Manuf. Mater. Process. 2020, 4(3), 70; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4030070 - 13 Jul 2020

Cited by 9 | Viewed by 4726

Abstract

Titanium alloy Ti6Al4V is a difficult-to-machine material which is extensively used in the aerospace and medical industries. Machining titanium is associated with a short tool life and low productivity. In this paper, a new cooling-lubrication system based on electrohydrodynamic atomization was designed, manufactured and tested and the relevant theory was developed. The major novelty of the system lies within the use of electrohydrodynamic atomization (EHDA) and a three-electrode setup for generating lubricant droplets. The system was tested and compared with that of flood, minimum quantity lubrication (MQL) and compressed air machining. The proposed system can extend the tool life by 6 and 22 times when compared with MQL and flood cooling, respectively. This is equivalent to more than 170 min tool life at 120 m/min cutting speed allowing for significant productivity gains in machining Ti6Al4V. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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12 pages, 11455 KiB

Open AccessArticle

Disturbance of the Regenerative Effect by Use of Milling Tools Modified with Asymmetric Dynamic Properties

by Jonas Baumann, Andreas Wirtz, Tobias Siebrecht and Dirk Biermann

J. Manuf. Mater. Process. 2020, 4(3), 67; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4030067 - 06 Jul 2020

Cited by 4 | Viewed by 3733

Abstract

Milling processes are often limited by self-excited vibrations of the tool or workpiece, generated by the regenerative effect, especially when using long cantilevered tools or machining thin-walled workpieces. The regenerative effect arises from a periodic modulation of the uncut chip thickness within the frequencies of the eigenmodes, which results in a critical excitation in the consecutive cuts or tooth engagements. This paper presents a new approach for disturbing the regenerative effect by using milling tools which are modified with asymmetric dynamic properties. A four-fluted milling tool was modified with parallel slots in the tool shank in order to establish asymmetric dynamic characteristics or different eigenfrequencies for consecutive tooth engagements, respectively. Measurements of the frequency response functions at the tool tip showed a decrease in the eigenfrequencies as well as an increase in the dynamic compliance in the direction of the grooves. Milling experiments with a constant width of cut and constantly increasing axial depth of cut indicated a significant increase in the stability limit for the specific preparations of up to 69%. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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18 pages, 6063 KiB

Open AccessFeature PaperArticle

An Iterative Size Effect Model of Surface Generation in Finish Machining

by Ian Brown and Julius Schoop

J. Manuf. Mater. Process. 2020, 4(3), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4030063 - 02 Jul 2020

Cited by 5 | Viewed by 2521

Abstract

In this work, a geometric model for surface generation of finish machining was developed in MATLAB, and subsequently verified by experimental surface roughness data gathered from turning tests in Ti-6Al4V. The present model predicts the behavior of surface roughness at multiple length scales, depending on feed, nose radius, tool edge radius, machine tool error, and material-dependent parameters—in particular, the minimum effective rake angle. Experimental tests were conducted on a commercial lathe with slightly modified conventional tooling to provide relevant results. Additionally, the model-predicted roughness was compared against pedigreed surface roughness data from previous efforts that included materials 51CrV4 and AL 1075. Previously obscure machine tool error effects have been identified and can be modeled within the proposed framework. Preliminary findings of the model’s relevance to subsurface properties have also been presented. The proposed model has been shown to accurately predict roughness values for both long and short surface roughness evaluation lengths, which implies its utility not only as a surface roughness prediction tool, but as a basis for understanding three-dimensional surface generation in ductile-machining materials, and the properties derived therefrom. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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13 pages, 3136 KiB

Open AccessFeature PaperArticle

Reconstruction of Process Forces in a Five-Axis Milling Center with a LSTM Neural Network in Comparison to a Model-Based Approach

by Berend Denkena, Benjamin Bergmann and Dennis Stoppel

J. Manuf. Mater. Process. 2020, 4(3), 62; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4030062 - 02 Jul 2020

Cited by 24 | Viewed by 2881

Abstract

Based on the drive signals of a milling center, process forces can be reconstructed. Therefore, a novel approach is presented to reconstruct the process forces with a long short-term memory neural network (LSTM) using drive signals as an input. The LSTM is evaluated and compared to a model-based approach. The latter compensates nonlinearities and disturbances such as friction and inertia. For training of the LSTM, multiple milling processes are considered to enhance the generalizability. Training data is generated by recording drive signals and process forces measured by a dynamometer. The LSTM is then evaluated using a test set, which comprises new process parameters. It is shown that the LSTM has a lower root mean square error (RMSE) in comparison to the model-based approach. Especially, when changing the feed motion direction during milling, the neural network clearly outperforms the model-based approach. Nevertheless, there are processes, where the LSTM induced oscillations, which do not correspond to the measured forces. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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22 pages, 4380 KiB

Open AccessFeature PaperArticle

Characterization and Modeling of Surface Roughness and Burr Formation in Slot Milling of Polycarbonate

by David Adeniji, Julius Schoop, Shehan Gunawardena, Craig Hanson and Muhammad Jahan

J. Manuf. Mater. Process. 2020, 4(2), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4020059 - 23 Jun 2020

Cited by 14 | Viewed by 3311

Abstract

Thermoplastic materials hold great promise for next-generation engineered and sustainable plastics and composites. However, due to their thermoplastic nature and viscoplastic material response, it is difficult to predict the properties of surfaces generated by machining. This is especially problematic in micro-channel machining, where burr formation and excessive surface roughness lead to poor component-surface integrity. This study attempts to model the influence of size effects, which occur due to the finite sharpness of any cutting tool, on surface finish and burr formation during micro-milling of an important thermoplastic material, polycarbonate. Experimental results show that the depth of cut does not affect either surface finish or burr formation. A proposed new sideflow model shows the dominant effect of cutting-edge radius and feed rate on surface finish, while tool edge roughness, coating and feed rate have the most pronounced influence on burr formation. Overall, a good agreement between the experimental data and the proposed size effect model for the machining of thermoplastic material was found. Based on these results, tool geometry and process parameters may be optimized for improved surface integrity of machined thermoplastic components. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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10 pages, 1585 KiB

Open AccessArticle

Green Ceramic Machining: Influence of the Cutting Speed and the Binder Percentage on the Y-TZP Behavior

by Anthonin Demarbaix, François Ducobu, Nicolas Preux, Fabrice Petit and Edouard Rivière-Lorphèvre

J. Manuf. Mater. Process. 2020, 4(2), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4020050 - 21 May 2020

Cited by 12 | Viewed by 2906

Abstract

The demand for inert bioceramics is always increasing in the dental field. Yttrium oxide tetragonal zirconia polycrystals (Y-TZP) are oxide ceramics which are currently used because of their interesting mechanical properties due to a toughening transformation. Industrially speaking, machining of the ceramic before sintering (green body) is very common because it allows a better productivity and it reduces crack probability during the sintering process. The goal of this paper is to determine the behavior of green ceramic during the machining operation. This study is carried out on several blanks with different binder percentages. The specific cutting energy (SCE) and the surface quality (R_a and R_z) are determined for several cutting speeds. The SCE follows a logarithmic evolution when the cutting speed increases. Despite this increase, the R_a are relatively stable whatever the cutting speed and the binder percentage. At a low cutting speed, a higher R_z value is observed caused by pullout of material. The increase of cutting speed allows to stabilize the R_z value whatever the binder percentage. This study shows that the green ceramic has a pseudo-plastic behavior whose machinability depends mainly on the interaction between the material and the cutting edge of the tool, so unlike pre-sintered ceramic or metallic part cutting speed has a low influence on the quality of the machined part. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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18 pages, 5405 KiB

Open AccessArticle

A Novel Experimental Test Bench to Investigate the Effects of Cutting Fluids on the Frictional Conditions in Metal Cutting

by Thomas Lakner and Marvin Hardt

J. Manuf. Mater. Process. 2020, 4(2), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4020045 - 14 May 2020

Cited by 12 | Viewed by 3155

Abstract

The tribological effect of cutting fluids in the machining processes to reduce the friction in the cutting zone is still widely unknown. Most test benches and procedures do not represent the contact conditions of machining processes adequately, especially for interrupted contacts. This results in a lack of knowledge of the tribological behavior in machining processes. To close this knowledge gap, a novel experimental test bench to investigate the effects of cutting fluids on the frictional conditions in metal cutting under high-pressure cutting fluid supply was developed and utilized within this work. The results show that there is a difference between the frictional forces in interrupted contact compared to continuous contact. Furthermore, the cutting fluid parameters of supply pressure, volumetric flow rate, and impact point of the cutting fluid jet influence the frictional forces, the intensities of which depend on the workpiece material. In conclusion, the novel test bench allows examining the frictional behavior in interrupted cuts with an unprecedented precision, which contributes to a knowledge-based design of the cutting fluid supply for cutting tools. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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15 pages, 2587 KiB

Open AccessArticle

Development of a Sustainability Assessment Algorithm and Its Validation Using Case Studies on Cryogenic Machining

by Prathamesh Bhat, Chetan Agrawal and Navneet Khanna

J. Manuf. Mater. Process. 2020, 4(2), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4020042 - 30 Apr 2020

Cited by 16 | Viewed by 3824

Abstract

This work presents a comprehensive structure for evaluating the sustainability of machining processes. Industries can contribute towards developing a sustainable future by using algorithms that evaluate the sustainability of their processes. Inspired by the literature, the proposed model involves a set of metrics that are critical in evaluating the impact of a process on society, environment, and economy. The flexibility of this model allows decision-makers to use the available responses to identify the most favorable process. The entropy weight method was suggested for objectively calculating the weights of each indicator. A multi-criteria decision-making method i.e., Technique for Order Preference based on Similarity to Ideal Solution (TOPSIS), was used to rank processes in the decreasing order of their sustainability. The proposed algorithm was successfully validated with case studies from the published literature. A MATLAB code was also created so that industries may expeditiously apply this method to evaluate the sustainability of machining processes. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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15 pages, 4352 KiB

Open AccessArticle

First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors

by Siamak Mirifar, Mohammadali Kadivar and Bahman Azarhoushang

J. Manuf. Mater. Process. 2020, 4(2), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4020035 - 25 Apr 2020

Cited by 22 | Viewed by 4294

Abstract

The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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12 pages, 5248 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Conduction-Based Thermally Assisted Micromilling Process for Cutting Difficult-to-Machine Materials

by Timo Platt, Alexander Meijer and Dirk Biermann

J. Manuf. Mater. Process. 2020, 4(2), 34; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp4020034 - 24 Apr 2020

Cited by 10 | Viewed by 6206

Abstract

The increasing demand for complex and wear-resistant forming tools made of difficult-to-machine materials requires efficient manufacturing processes. In terms of high-strength materials; highly suitable processes such as micromilling are limited in their potential due to the increased tool loads and the resulting tool wear. This promotes hybrid manufacturing processes that offer approaches to increase the performance. In this paper; conduction-based thermally assisted micromilling using a prototype device to homogeneously heat the entire workpiece is investigated. By varying the workpiece temperature by 20 °C < T_W < 500 °C; a highly durable high-speed steel (HSS) AISI M3:2 (63 HRC) and a hot-work steel (HWS) AISI H11 (53 HRC) were machined using PVD-TiAlN coated micro-end milling tools (d = 1 mm). The influence of the workpiece temperature on central process conditions; such as tool wear and achievable surface quality; are determined. As expected; the temporary thermal softening of the materials leads to a reduction in the cutting forces and; thus; in the resulting tool wear for specific configurations of the thermal assistance. While only minor effects are detected regarding the surface topography; a significant reduction in the burr height is achieved. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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13 pages, 3041 KiB

Open AccessArticle

Micromagnetic Analysis of Thermally Induced Influences on Surface Integrity Using the Burning Limit Approach

by Jonas Heinzel, Daniel Sackmann and Bernhard Karpuschewski

J. Manuf. Mater. Process. 2019, 3(4), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/jmmp3040093 - 12 Nov 2019

Cited by 8 | Viewed by 2923

Abstract

Particularly for highly stressed components, it is important to have precise knowledge of the surface and subsurface properties and, thus, of the functional properties after final grinding at the end of a complex process chain in order to avoid rejected parts. Therefore, non-destructive testing methods have been the subject of research for several years. The Barkhausen noise analysis, as a micromagnetic measuring method, has the potential to characterize the subsurface area up to an analyzing depth δ non-destructively with micromagnetic parameters. In addition to micromagnetic multiparameter approaches, which allow post-process mode clear statements about the subsurface area state, the present research work deals with the concept of a connection of a single Barkhausen noise parameter with grinding process parameters. In combination with the analytical approach of Malkin for the thermal surface and subsurface area influence, which is based on the process parameters of grinding processes, a distinction between good and rejected ground parts can be achieved. The results show that, by post-process measurements of the Barkhausen noise on case-hardened workpieces made of steel 18CrNiMo7-6 (No. 1.6587, AISI 4820) and machined by a cylindrical grinding process, incipient changes in the residual stress state up to industrial-relevant limits, which distinguish between good and rejected parts, is possible. In the future, a combination of the Malkin grinding burning limit and sufficient condition monitoring based on in-process measurements of Barkhausen noise will be investigated. The application limits of the analytical approach of Malkin as well as the measurement of the Barkhausen noise in-process have to be determined. Full article

(This article belongs to the Special Issue Advanced Manufacturing and Machining Processes)

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