Active fixturing: literature review and future research directions

Fixtures are used to fixate, position and support workpieces and represent a crucial tool in manufacturing. Their performance determines the result of the whole manufacturing process of a product. There is a vast amount of research done on automatic fixture layout synthesis and optimisation and fixture design verification. Most of this work considers fixture mechanics to be static and the fixture elements to be passive. However, a new generation of fixtures has emerged that has actuated fixture elements for active control of the part–fixture system during manufacturing operations to increase the end product quality. This paper analyses the latest studies in the field of active fixture design and its relationship with flexible and reconfigurable fixturing systems. First, a brief introduction is given on the importance of research of fixturing systems. Secondly, the basics of workholding and fixture design are visited, after which the state-of-the-art in active fixturing and related concepts is presented. Fourthly, part–fixture dynamics and design strategies which take these into account are discussed. Fifthly, the control strategies used in active fixturing systems are examined. Finally, some final conclusions and prospective future research directions are presented

Robots in machining

Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Research and development of a reconfigurable robotic end-effector for machining and part handling.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

ON THE STABILITY OF VARIABLE HELIX MILLING TOOLS

One of the main aims of the manufacturing industry has been to maximise the material removal rate of machining processes. However, this goal can be restricted by the appearance of regenerative chatter vibrations. In milling, one approach for regenerative chatter suppression is the implementation of variable-helix cutters. However, these tools can lead to isolated unstable regions in the stability diagram. Currently, variable-helix unstable islands have not been extensively researched in the literature. Therefore, the current thesis focuses on studying and experimentally validating these islands. For the validation, an experimental setup that scaled not only the structural dynamics but also the cutting force coefficients was proposed. Therefore, it was possible to attain larger axial depths of cut while assuming linear dynamics. The variable-helix process stability was modelled using the semi-discretization method and the multi-frequency approach. It was found that the variable helix tools can further stabilise a larger width of cut due to the distributed time delays that are a product of the tool geometry. Subsequently, a numerical study about the impact of structural damping on the variable-helix stability diagram revealed a strong relationship between the damping level and instability islands. The findings were validated by performing trials on the experimental setup, modified with constrained layer damping to recreate the simulated conditions. Additionally, a convergence analysis using the semi-discretization method (SDM) and the multi-frequency approach (MFA) revealed that these islands are sensitive to model convergence aspects. The analysis shows that the MFA provided converged solutions with a steep convergence rate, while the SDM struggled to converge. In this work, it is demonstrated that variable-helix instability islands only emerge at relatively high levels of structural damping and that they are particularly susceptible to model convergence effects. Meanwhile, the model predictions are compared to and validated against detailed experimental data that uses a specially designed configuration to minimise experimental error. To the authors' knowledge, this provides the first experimentally validated study of unstable islands in variable helix milling, while also demonstrating the importance of accurate damping estimates and convergence studies within the stability predictions

Development of Mobile Machining Cell

This report covers some initial aspects of development of the mobile InnoMill machining cell. The new machining paradigm where the machine is mounted on the workpiece is compared to the old paradigm where the workpiece is mounted inside the machine, and differences are discussed. Parametric studies of the workpiece case study of the InnoMill project, the Vestas V112-3.0MW wind turbine hub, are performed to supply insight regarding load capacity etc. for the machine designers. The hub finite element model is validated using experimental results from Operational Modal Analysis performed on the hub. Furthermore, the InnoMill concept is described, and work regarding the 6 degree of freedom parallel kinematic manipulator which is present in the concept is performed. A numerical procedure accounting for base deflections due to static loading is proposed and implemented. Additionally, a six degree of freedom spring-mass model vibrational response is compared to vibrational response obtained from experiments on the 6 degree of freedom parallel kinematic manipulator at Aarhus University. The model, which is based on assumptions commonly found in literature, is rejected. Finally, an outlook for the remaining part of the PhD project is presented

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

An investigation on micro cutting mechanics: Modelling, simulations and experimental case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Micro cutting is becoming increasingly important since miniature and micro components/products have become more and more demanded in precision engineering applications and consumer goods in a daily life. Meanwhile, it has not been thoroughly investigated yet. Scientific understanding of the fundamentals in micro cutting mechanics and physics is vital for micro manufacturing of micro or miniature components and products. Consequently, the scientific investigation on micro cutting mechanics is critically needed, particularly on its key fundamental aspects on which a systematic approach and key enabling technologies are developed for micro manufacturing. Therefore, three key fundamental aspects of micro cutting mechanics have been identified for this PhD project and a comprehensive systematic research has been performed through both theoretical and experiment-based investigations. The three aspects of micro cutting mechanics mainly include dynamic stiffness investigation, innovative micro cutting force modelling, and the study on micro cutting heat, temperature and their partitioned distribution. All experiment-based investigations are undertaken on a diamond turning machine test rig supported with a fast tool servo (FTS) using different reconfigured experimental setups. The finite element (FE)-based analysis is conducted to further support the in-depth analysis on the micro cutting phenomena especially the modelling and simulation of micro cutting force and temperature. Accordingly, both micro cutting force modelling and micro cutting temperature are investigated using modelling and simulation supported by well-designed experimental cutting trials and validations.The investigation on dynamic stiffness in the micro cutting system is focused on its effects on the micro cutting process and its control strategies. The burrs formation and machining accuracy are explored in relation with control of the dynamic stiffness. Furthermore, the control algorithm for dynamic stiffness is developed accordingly in order to minimise burrs formation and stabilize the micro cutting accuracy.The micro cutting force modelling is performed based on specific cutting force, i.e. modelling the cutting force at the unit cutting length or area as coined as the amplitude aspect of the proposed cutting force modelling. The cutting force against a dynamically varied cutting time interval is proposed as the spatial aspect of the cutting force formulation. The amplitude aspect can provide the insight into the micro cutting phenomena particularly in relation with the chip formation and size-effects. The spatial aspect, using a on the wavelet transform (WT) technique and standard deviation analysis can render the dynamic behaviour of the micro cutting force, particularly representing the dynamic effects of the cutting process and its correlation with tool wear.The micro cutting temperature is investigated to formulate the scientific understanding of cutting temperature, heat and their partitioned distribution particularly at the tool-workpiece-chip interface zone in ultraprecision and micro cutting using a diamond cutting tool. The contribution to knowledge at this aspect is to represent the partitioned cutting heat in the micro cutting process and their different behaviours compared to the conventional metal cutting. The scientific approach to modelling micro cutting application (MMCA), i.e. based on modelling-simulation combined with experimental validation, is further evaluated and validated to illustrate the overall benefits of this research investigation through micro cutting of single crystal silicon (for ultraprecision machining of large-sized infrared devices). This approach is established in light of combining all the three aspects of the above investigation on micro cutting mechanics. The research results show the approach can lead to industrial scale advantages for ultraprecision and micro cutting but driven by the scientific understanding of micro manufacturing technology. The systematic investigation on dynamic stiffness control, micro cutting force modelling, micro cutting heat and temperature and their integrated approach can contribute well to the future micro cutting applications

Indirect monitoring of surface quality based on the integration of support vector machine and 3D I-kaz techniques in the machining process

Improved machining process quality can contribute to sustainable manufacturing in terms of economic, environmental, and social sustainability. Reducing waste, increasing efficiency, and improving product quality can also help manufacturers to reduce costs and increase productivity rate. Machining is one of the common methods in industry and plays a central role in modern manufacturing. For many years, researchers have been studying monitoring methods to produce the best surface quality. The measurement involves three distinct techniques, which are categorised into quantitative and visualisation methods. Monitoring methods can be classified as either direct or indirect methods. The common method of measuring machining quality undergoes manufacturing bottlenecks, as it is constrained by human inspection and expensive equipment. A slow process leads to higher labour costs and a high risk of equipment damage to the workpiece. The present study aims to bridge this gap by leveraging the capabilities of 3D I-kaz and medium Gaussian SVM models to improve accuracy and classification rates for determining surface quality. The specific objectives are to analyse the impact of machining parameters on statistical analysis, classify acceleration signals for surface roughness identification using SVM, integrate SVM with 3D I-kaz to improve surface quality identification and validate its effectiveness through experiments. The quantification of signal processing for ductile iron, FCD450 material on cutting parameters: rotation speed with 1000–3026 rev/mm, feed rate of 120–720 mm/min, axial of 0.75–3.5 mm, and radial depth of cut (RDOC) is studied and validated through experiments under dry and minimum quantity lubrication (MQL) conditions. Surface roughness was measured to verify the acceleration signal, while Pearson’s correlation coefficient was used to evaluate the correlation strength between the acceleration signal and surface roughness. The calculated coefficient, r-value, was found to be 0.6543, which indicates a positive but nonlinear correlation between the acceleration signal and surface roughness. The kurtosis value measured from acceleration signals and surface roughness information was then used to classify the machining condition and identification of the surface quality. In the first experiment, the model displayed an accuracy of 84.87% and 84.57% in terms of F1 values. It was observed that by adjusting the hyperparameter, the model’s accuracy was augmented to 85.53% and its F1 score was enhanced to 84.93%. Additionally, the model was applied in the second experiment, resulting in an accuracy of 84.0%. Before the classification of machined surface condition, the condition is identified through the support vector machine (SVM) technique, and it was demonstrated that the condition could be demarcated into five different levels of surface quality. From the experimental test data, acceleration and average roughness (Ra)-based indicators are identified for correlation analysis. A relation is developed, which enables the prediction or identification of surface quality directly based on the selected based indicators (3D I-kaz coefficient) without having to inspect the milling process for surface roughness. It was demonstrated that the integration of the 3D I-kaz and SVM model resulted in an accuracy and F1 score of 96.0% and 96.3% respectively, suggesting that the quantification data is viable for surface quality identification. A monitoring experiment was conducted in this study to validate the identification of surface quality through the instantaneous surface roughness level obtained from the experiment. In conclusion, indirect monitoring of surface quality using vibration signals can quickly identify the surface quality using SVM and 3D I-kaz analyses, thus reducing the time and cost associated with manual inspection and allowing for its use in many other machining processes