Manufacturing companies are faced with challenges associated with the fluctuating demand in consumer purchases. As a result, broader families of varyingly manufactured products are on the rise, which poses a significant workload problem for production engineers involved with process planning. The aim of this paper is therefore to introduce Feature-based Machining, as an endorsed CAM-based method to alleviate such workload involved when planning for machining processes. Feature-based Machining does this by eliminating conventional work steps associated with Computer-aided Manufacturing, such as defining cutting parameters, tool associations, feeds and speeds, tool-path generation, and more. By recognizing features over a given workpiece, several pre-taught operations can be automatically generated in response, following an established library of machining knowledge. The methodology of preparing Feature-based Machining is an extensive process that is to be detailed comprehensively in this project, so that future projects and industries may adopt it as a resolute method towards process planning. Moreover, relevant subjects such as sustainability, ethical concerns and innovation are also to be reflected on from the perspective of Feature-based Machining, to provide for fruitful discussion surrounding strategies and implementation.Furthermore, it is also relevant to discuss the intuitiveness and transparency of computer[1]aided software, as it relates to any issues or hurdles that may be encountered during the process of applying Feature-based Machining. Despite such challenges, this project has resulted in a visual framework of recognizing features and applying processes across a family of crankshaft workpieces, where the utility of Feature-based Machining has proven significantly promising for industries of the future, as a result. Future work is also encouraged to follow up on the significance of Virtual Commissioning to this part, as well as a thorough review of Feature-based Machining as a modern subject.

The application of robotic manipulators in machining systems has gained a great interest in manufacturing because of their lower prices, higher kinematic flexibility and larger workspace compared to conventional CNC machine tools. However, their performance is limited due to the much lower structural rigidity which makes them more susceptible to excessive and unstable vibrations, known as chatter, during the machining process. Highly effective chatter modeling and avoidance methods that have been developed for CNC machining in the past decades are now being used by the industry to design high-performance chatter-free machining operations. The available methods, however, face major difficulties when applied to robotic machining, mainly due to the high flexibility and pose-dependency of the vibration response in robots. High flexibility leads to high-amplitude vibrations which affect the process dynamics and excite structural nonlinearities. The existing approaches to modeling machining vibrations assume linearity of the structural dynamics of the robotic manipulator. This assumption, considering the inherent nonlinearities in the robot’s revolute joints, may cause considerable inaccuracies in predicting the stability of vibrations during the process. This thesis studies the high flexibility and nonlinearity of the robot’s structural dynamics and their effects on chatter vibrations. The research starts with investigating the effects of high flexibility of robot's structure in the process dyamics by considering the modulation of cutting forces by axial vibrations, which is normally ignored in CNC milling due to high rigidity of the machine in this direction. The results of chatter prediction considering this effect are shown and discussed. The rest of the thesis focuses on the structural nonlinearity. Firstly, an experimental study is presented to investigate the extent of nonlinearity in structural dynamics of the robot. The results confirm that structural nonlinearities in robotic machining systems can be effectively excited in the presence of high-amplitude vibrations due to milling forces, such that they cause remarkable differences in chatter prediction. The following step is modeling the structural nonlinearities. For this purpose, the variation of restoring forces with the dynamic response (displacement and velocity) are observed when the robot is subjected to harmonic excitation. Based on the experimental observations, the nonlinear effects are modeled by cubic stiffness and damping characteristics. Parameters of the nonlinear model are then identified using Higher-order Frequency Response Functions (HFRF) extracted from measurements. The identified model can predict the vibration behavior of the robotic machining system when subjected to periodic loads such as milling forces. The developed model of nonlinear structural dynamics is then coupled with the chatter model. Consequently, the system is described by nonlinear Delay Differential Equations (DDE) with periodic coefficients. Bifurcation diagrams for the forced vibrations in the described system are developed using the numerical continuation method. The effects of cutting parameters such as feedrate as well as the nonlinear parameters are studied. The thesis is concluded by proposing the use of in-process FRF in the linear model of chatter stability for quick prediction of stability limits. In this approach, the exact characteristics of the nonlinear mechanisms are not studied; instead, the measured FRF during the milling process are used, which are assumed to represent the nonlinear structural dynamics that are linearized about the applied operational conditions. Two methods of measuring in-process FRF are proposed and employed in the robotic milling system. The measured FRF are then used in the linear chatter model to develop the Stability Lobes Diagram (SLD) which shows the combination of cutting parameters that lead to stable or unstable vibrations. Experimental chatter tests show that better agreement with predictions can be achieved by using in-process FRF instead of FRF measured at the idle state of the system. The results of this thesis contribute to better characterization of vibrations in robotic machining with high-amplitude forces and selecting suitable strategies to enhance productivity of the operation. / Graduate

High Power Impulse Magnetron Sputtering (HiPIMS) comes as a new and promising PVD method for the development of high-performance coatings for cutting applications. This technique utilizes high energy and ionization which can produce a denser and stronger ceramic in comparison to traditional deposition techniques. Important coating characteristics that arise from this method such as enhanced hardness, adhesion, and less defects, can be applied when machining hard-to-cut materials. In this study, investigation of tool life and wear mechanisms, mechanical and physical properties of AlTiN coatings deposited on carbide tools by HiPIMS and Cathodic Arc Evaporation (CAE) were analyzed when machining Inconel 718 and Stainless Steel 304. Experimental turning tests were performed to evaluate tool life, and the wear mechanisms were analyzed by optical and scanning electron microscopy. Nanohardness, scratch test, fracture toughness and other methods were carried out to evaluate the coating properties. Impedance experiments were performed to determine the coating porosity and resistance to corrosion. The results showed that HiPIMS coating presented higher hardness, toughness to fracture and adhesion to the substrate in comparison to CAE coatings. The HiPIMS coated tool substantially improved tool life when machining Inconel. The dominant wear mechanism found was abrasion, which is induced by the presence of hard carbides. The main wear patterns observed were flank, notch, and crater wear. The tool performance of HiPIMS was found to have enhanced mechanical properties, lower porosity, and form a larger amount of tribo-oxides when machining, in comparison to CAE. / Thesis / Master of Applied Science (MASc)

The material removal process uses cutting tools in order to produce the desired shape of the workpiece. Tool wear has been a problem for cutting tools, since cutting tools wear and break. Research has been accomplished in the tool wear field for tool life and more recently tool wear. The computer generation has created a method to simulate the material removal process. These computer simulations model the cutting tool reaction with the workpiece. Many of the simulation models use finite element analysis to calculate the reaction of the cutting tool. Different finite element models are being used throughout the world for research. This thesis used an updated Lagrangian model in conjunction with Archard's law to predict the wear of the cutting tool. This research used experimental data to correlate with simulation data to see whether or not Archard's law was a good approximation for tool wear. The research used different side rake angles and cutting surface speed to test the simulation. Shear angle, contact length, cutting ratio, and force are used to provide output values to compare the experimental and computer simulation data. The comparative results showed good trends between the experimental and computer simulation data in every comparison. The results also showed a good approximation for the force and contact length values. Archard's law can be used to model wear on cutting tools with further research.

Within manufacturing of metallic components, machining plays an important role and is of vital significance to ensure process reliability. From a cutting tool design perspective,  tool macro geometry  design  based on physics based  numerical modelling  is highly needed  that can predict chip morphology.  The chip morphology describes the chip shape geometry and the chip curl geometry. The prediction of chip flow and chip shape is vital in predicting chip breakage, ensuring good chip evacuation and lower surface roughness.  To this end, a platform where such a  numerical model’s chip morphology prediction  can be compared with experimental investigation is needed and is the focus of this work. The studied cutting processes are orthogonal cutting process and nose turning process. Numerical models that simulate the chip formation process are employed to predict the chip morphology and are accompanied by machining experiments. Computed tomography is used  to scan the chips obtained from machining experiments and its ability to capture the variation in  chip morphology  is evaluated.  For nose turning process,  chip  curl parameters during the cutting process are to be calculated. Kharkevich model is utilized in this regard to calculate the  ‘chip in process’ chip curl parameters. High speed videography is used to measure the chip side flow angle during the cutting process experiments and are directly compared to physics based model predictions. The results show that the methodology developed provides  the framework where advances in numerical models can be evaluated reliably from a chip morphology prediction capability view point for nose turning process. The numerical modeling results show that the chip morphology variation for varying cutting conditions is predicted qualitatively. The results of quantitative evaluation of chip morphology prediction shows that the error in prediction is too large to be used for predictive modelling purposes.

I dagens samhälle befinner sig tillverkningstekniksindustrin i en övergångsfas på väg in i en digital era. Till skillnad från Industry 3.0 som involverar automatisering av enstaka maskiner och processer omfattar Industry 4.0 digitalisering och uppkoppling av alla aktiviteter i sin värdekedja. Mängden av tillgängliga data ökar hela tiden. Målet med detta examensarbete var att ta reda på vad digitalisering kan tillföra till Scanias motortillverkning med fokus på förbättring av nuvarande KPI:er (Key Performance Indicator). För att säkerställa att det ovanstående målet uppfylls formulerades nedanstående frågeställningar: • Hur kan Scania Motortillverkning förbättra KPI i bearbetningsprocesser genom insamling och analys av data från maskiner? o Vilka faktorer påverkar Scanias nuvarande KPI? o Vilka av dem har potential att utvecklas vidare eller styras genom införande av digitalisering? o Vilka maskindata är nödvändiga att samlas in för att öka KPI:er och på vilket sätt kan data samlas in? För att besvara frågorna och identifiera vilka data som kan tillföra nytta, användes främst kvalitativa metoder. Data samlades in från olika aspekter av genom praktik, observationer, intervjuer och studiebesök. För att kunna dra generella slutsatser och för att bekräfta hypoteser från de kvalitativa studierna utfördes även en kvantitativ studie som utförs på vevstakeline, genom sortering och analys av de befintliga insamlade data i dagens produktion. Efter en litteraturstudie kombinerad med intern informationssamling, kunde fokusområdet avgränsas ytterligare till OPE (Overall Production Effectiveness) som är väsentlig för produktiviteten och ofta inte når målvärdet i nuläget. Genom en analys av PUS (Produktionsuppföljningssystem) kunde faktorer som har en negativ inverkan på OPE tas fram. Genom intervjuer med olika funktioner inom Scania kunde författarna identifiera många konkreta parametrar, vilka kan fördelas till tre kategorier, Maskindata, Verktygdata och Processdata. En teknisk skanning av verktygs- och maskintillverkare genomfördes också för att kunna redogöra för vilka data som är möjliga att samla in med befintliga insamlingsmetoder.  Resultatet från detta examensarbetet bildar en utgångspunkt för att kunna digitalisera Scanias motorbearbetning, digitaliseringen kräver en del nya investeringar som författarna anser komma att löna sig på många plan. / The manufacturing industry is undergoing a transformation into a digital era. Industry 4.0 comprises digitization and connection of all aspects of its value chain, which results in more data being available and collected. The purpose of this degree project is to investigate the benefits that digitalization can bring to Scania’s engine manufacturing, with focus on improving the current KPIs (Key Performance Indicator). The following questions were formulated to ensure that this purpose could be reached: • How can Scania’s engine manufacturing improve KPIs in machining processes by collecting and analyzing data from machines?o What factors affect Scania’s current KPIs? o Which of the factors has the potential to develop further or control through the implementation of digitization? o Which machine data needs to be collected in order to improve KPIs and how can this data be collected? To answer these questions and investigate which data can be of use, a primarily qualitative method was initiated during the project. Information was collected from various aspects involved in production. This was done through observations, interviews and study visits. A quantitative study has also been performed to improve the generalizability of the thesis, by sorting and analyzing the existing collected data in the production line of the connecting rod. By combining literature studies with internal information gathering, the authors could further define the focus area to OPE (Overall Production Effectiveness), which is significant for productivity, and often does not reach the target value for Scania. Through an analysis of the data gathering from PUS (Production Follow-up System), factors that had a negative impact on OPE could be identified. The authors could also produce many concrete parameters by utilizing results from interviews of various functions within Scania. The parameters can be divided into three categories, Machine data, Tool data and Process data. A technical scan of tool and machine supplier has also been conducted to specify what data is possible to collect with the existing technology. The result of this degree project creates a starting point for digitalization of Scania’s engine manufacturing, which also results in potential new investments. According to the estimation of the authors, these investments will prove to be profitable on many levels over time.

To remain competitive in today’s demanding economy, there is an increasing demand for improved productivity and scrap reduction in manufacturing. Traditional manufacturing metal removal processes such as turning and boring are still one of the most used techniques for fabricating metal products. Although the essential metal removal process is the same, new advances in technology have led to improvements in the monitoring of the process allowing for reduction of power consumption, tool wear, and total cost of production. Replacing used CNC lathes from the 1980’s in a manufacturing facility may prove costly, thus finding a method to modernize the lathes is vital. This research focuses on Phase I and II of a three phase research project where the final goal is to optimize the simultaneous turning and boring operation of a CNC Lathe. From the optimization results it will be possible to build an adaptive controller that will produce parts rapidly while minimizing tool wear and machinist interaction with the lathe. Phase I of the project was geared towards selecting the sensors that were to be used to monitor the operation and designing a program with an architecture that would allow for simultaneous data collection from the selected sensors at high sampling rates. Signals monitored during the operation included force, temperature, vibration, sound, acoustic emissions, power, and metalworking fluid flow rates. Phase II of this research is focused on using the Response Surface Method to build empirical models for various responses and to optimize the simultaneous cutting process. The simultaneous turning and boring process was defined by the four factors of spindle speed, feed rate, outer diameter depth of cut, and inner diameter depth of cut. A total of four sets of experiments were performed. The first set of experiments screened the experimental region to iii determine if the cutting parameters were feasible. The next three set s of designs of experiments used Central Composite Designs to build empirical models of each desired response in terms of the four factors and to optimize the process. Each design of experiments was compared with one another to validate that the results achieved were accurate within the experimental region. By using the Response Surface Method optimal machining parameter settings were achieved. The algorithm used to search for optimal process parameter settings was the desirability function. By applying the results from this research to the manufacturing facility, they will achieve reduction in power consumption, reduction in production time, and decrease in the total cost of each part.

Liquid spray is essential to industries requiring processes such as spray coating, spray drying, spray pyrolysis, or spray cooling. This thesis reports the design, fabrication, and characterization of a thin film deposition system which utilizes a linear multiplexed electrospray (LINES) atomizer. First, a thorough review of the advantages and limitations of prior multiplexed electrospray systems leads to discussion of the design rationale for this work. Next, the line of charge model was extended to prescribe the operating conditions for the experiments and to estimate the spray profile. The spray profile was then simulated using a Lagrangian model and solved using a desktop supercomputer based on Graphics Processing Units (GPUs). The simulation was extended to estimate the droplet number density flux during deposition. Pure ethanol was electrosprayed in the cone-jet mode from a 51-nozzle aluminum LINES atomizer with less than 3% relative standard deviation in the D10 average droplet diameter as characterized using Phase Doppler Interferometry (PDI). Finally a 25-nozzle LINES was integrated into a thin film deposition system with a heated, motion controlled stage, to deposit TiO2 thin films onto silicon wafers from an ethanol based nanoparticle suspension. The resulting deposition pattern was analyzed using SEM, optical profilometry, and macro photography and compared with the numerical simulation results. The LINES tool developed here is a step forward to enabling the power of electrospray for industrial manufacturing applications in clean energy, health care, and electronics

Silicon carbide has many properties that make it a promising and desirable material for diverse applications. One such application for silicon carbide wafers is as a transparent cryogenic probe card. This thesis briefly describes the design of a probe card based on a silicon carbide wafer substrate. It includes a description of electroplating fundamentals and demonstrates the feasibility of electroplating copper onto a wafer for the formation of bond pads between the substrate and external PCB ring. The process for electroplating copper with good adhesion and quality based on metal alloy formation, current control, and materials selection is outlined. Results of this process are also presented. This work also demonstrates the ability to machine silicon carbide using electrical discharge machining, abrasive water jet machining, and diamond bit milling, proving diamond grinding to be the most versatile of the described methods for machining intricate patterns into the wafers.

There is a continuous need within most manufacturing environments for more flexible production equipment, particularly where customer satisfaction and responsiveness promote quality improvement. In this paper, an automated agile manufacturing system that uses high-speed computer numerically controlled (CNC) machines to make automotive cylinder heads is proposed and evaluated by means of discrete event simulation using the ARENA simulator. Two alternative agile system configurations are constructed and simulated to achieve the production target. The simulation shows some significant benefits in using the agile system and demonstrates that high-speed CNC equipment is a viable option for cylinder head manufacture at a production volume of 550 000 units per annum. It is shown that the agile system can provide more flexibility and half the throughput time of the transfer line.