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Tool wear plays a decisive role in achieving the required surface quality and dimensional accuracy during the machining of Inconel 718-based products. The highly stochastic phenomenon of tool wear, particularly in later stages, results in difficulty in predicting the failure point of the tool. The present research work aims to study this late-stage wear of the tool by generating consistent wear conditions and thereby decoupling the late-stage wear from the wear history. To do so, a multi-axis grinding operation is employed to create artificial tool wear that replicates the topology of natural wear occurring in the process. In order to evaluate the imitating ability of the proposed methodology, microscopic images in different wear states of naturally and contrived worn tools were analyzed. The methodology was validated by comparing the resulting process forces measured during end milling with the natural and contrived worn tool for different path strategies. Finally, a qualitative FE-analysis was conducted, and specific force coefficients for worn tool segments were determined through simulation.

Electrically assisted heat treatment is the process of applying an electric current to a sample during heat treatment. Literature has generally shown there to be a difference in the resulting effects of direct current (DC) current and highly transient current (i.e. electropulsing). However, these differences are poorly characterized. In situ transmission electron microscopy (TEM) observation of an AA7075 sample while DC and pulsed current were passed through it was performed herein to explore the effects of an electric current on precipitate development. Numerical simulation results indicate that the thermal response of the samples was very rapid, causing the sample to reach steady-state temperatures almost instantly. There does not appear to be any significant difference between the results of pulsed current application and DC current. Additionally, the failure mechanism of an electrical biasing TEM sample is explored.

Modern manufacturing enterprises must be agile to cope with sudden demand changes arising from increased global competition, geopolitical factors, and unforeseen circumstances such as the Covid-19 pandemic. Small- and Medium-Sized Enterprises (SMEs) in the manufacturing sector lack agility due to lower penetration of Information Technology (IT) and Operational Technology (OT), the inability to employ highly skilled human capital, and the absence of a formal innovation ecosystem for new products or solutions. In recent years, Cloud-based Design and Manufacturing (CBDM) has emerged as an enabler for product realization by integrating various service-based models. However, the existing framework does not thoroughly support the innovation ecosystem from concept to product realization by formally addressing economic challenges and human skillset requirements. The present work considers the augmentation of the Design-as-a-Service (DaaS) model into the existing CBDM framework for enabling systematic product innovations. The DaaS model proposes to connect skilled human resources with enterprises interested in transforming an idea into a product or solution through the CBDM framework. The model presents an approach for integrating human resources with various CBDM elements and end-users through a service-based model. The challenges associated with successfully implementing the proposed model are also discussed. It is established that the DaaS has the potential for rapid and economical product discovery and can be readily accessible to SMEs or independent individuals.

The direct application of an electric current to a metallic workpiece during a manufacturing process, more commonly known as electrically-assisted manufacturing (EAM), produces many beneficial effects. During forming, significant flow stress reductions can be achieved. While there is much literature characterizing the effects of an electric current on the mechanical properties of materials during deformation, few feasible control systems for use in industrial processing have been developed. One such method is developed and tested herein using tensile testing in which electric current is triggered once a user-defined stress level is reached. The current then remains on until the stress reaches a desired minimum level. This cycle repeats until fracture occurs. The influence of several process parameters is discussed and an empirical formulation for determining the pulse frequency and the required energy per pulse is developed. This control method was found to be successful for forming metals while not exceeding a desired stress level. Furthermore, its application towards industrial use is also discussed.

The conventional stamping manufacturing process has certain limitations that need to be considered throughout the product design process, including the thickness of the blank, geometry of the product, and the drawing force. If the limitations are not considered during the design and manufacturing, they become defects such as wrinkles, excessive thinning, rupture, and spring back. The outcome of the defects is an increase in costs, rework, pre-processing of material (Heat Treatment), and the most important factor, time. To overcome defects, standard alternatives are changing the material composition, blank thickness, or the product design. This research aims to reduce the defects by keeping the design and the material the same as considered during the design phase. Electrically assisted manufacturing is used in the stamping process to eliminate defects. Electrically Assisted Manufacturing has been proven successful in increasing the workability of the workpiece. In this method, controlled electricity passes through the workpiece, blank holder, or the dies during the manufacturing process, which heat the blank. 5052-H32 Aluminium with a thickness of 0.5 mm was used for this study. Previous research indicates that this EAM technique can be used in forging, which is called Electrically Assisted Forging, to improve the formability of the workpiece. This research provides insights into the implementation of Electrically Assisted Forging in the stamping process. In the Electrically Assisted Stamping process, the heat produced due to electricity will temporarily change the material properties and increase its elasticity. Once the temporary elastic limit is achieved, the stamping process will begin. The current flow in pulses will continue until the stamping is completed. The method proposed in this paper considered three important parameters; the amplitude of the current, current holding time, and feed rate of the stamping machine. These parameters were used with different combinations during the testing. Using the data generated of drawing force from the Instron machine was used to plot different types of comparison graphs, which ultimately resulted in direct relation between current and drawing force.

Remaining Useful Life (RUL) is critical to optimizing part life and reducing maintenance costs in a predictive maintenance strategy. Current methods of remaining useful life predictions are significantly dependent on operating conditions and time as input features. However, these features do not fully encompass the variability of real-world operating conditions and notably as the bearing nears failure. This work provides an improved failure representation by exploring the underlying data distribution parameters of a bearing failure dataset generated using the Purposeful Failure Methodology under varying operating conditions and then provides a comparison to the widely used NASA/IMS bearing run-to-failure dataset. Laboratory experiments utilized a bearing test stand to capture failure states for fatigue and contamination failure mode. The fatigue and contamination failure procession is compared to the failed bearings from the NASA Bearing dataset to examine similarities in the underlying data distribution between either dataset. A Weibull distribution is then fitted to both datasets. The resulting distributions exhibit similar trends, dependent on the damage stage. Based on the fitted parameters, a decreasing trend for the Weibull parameters was influenced by the changing speed in the engraving test case with similar trends to the NASA bearing dataset. The resulting understanding of the data distribution parameters will be used to improve the end of RUL calculation by describing the distribution fit that best determines the bearing life modification numbers.

Labeled training data are challenging to obtain in a manufacturing environment during production due to the time and cost constraints of the labelling process. Of the labeled training data that is collected, failure data comprises a small proportion or is non-existent in production datasets for condition monitoring. The small proportion can be related to failures occuring uxpectedly and parts are replaced quickly, meaning the failure state is rare and makes up a small portion of the run life and number of samples collected. The lack of labeled data and failure data leads to challenges in creating effective predictive systems, such as Digital Twins, to accurately determine equipment health state and remaining useful life. This work investigates training predictive algorithms using an augmented failure data set derived from laboratory systems with knowledge of real-world failures. Data are collected under different failure progressions and operating conditions to create variability for the variety of different production applications to apply these data augmentation methodologies. These same data are transformed by adding the variability measured through purposefully damaging the mechanical system to create the degraded and failed state data. This variability is extracted using a spectral augmentation technique on the surrogate system’s failure data under an artificial fatigue case. The fatigue case is created by incrementally damaging the bearing raceway and measuring the damaged surface area with respect to the total bearing raceway. The measured difference between these pre- and post-lab damage states is used as the damage state data set transformation function. The augmented and “true” data are then compared using class probability analysis and diagnosing particular failure instances. For future research, relatability analysis will be investigated to see how the effects change between bearings of different sizes.

This research aims to model manual assembly processes by parameterizing operator force readings, specifically for engine and coolant hose connections in an automotive manufacturing line. During automotive assembly, many processes are still performed manually by the human operator due to the complexity of automating the process or product with current technology. Processes include completing hose connections and subsequent “push–pull–push” verification testing. Manual work introduces an opportunity for human error because of natural variation when completing tasks; even a slight inconsistency in operation can lead to an incomplete or missed process. These incomplete processes can pass post-production checks, such as a pressure test, but later loosen and fail, causing rework or warranty issues. To minimize human error, operator force is parameterized to provide real-time feedback to the connection status. The operator force was chosen to classify connections and to verify testing quantitatively. The parametrization was completed by partitioning the shear and normal forces using custom fixtures, with shear being the primary force type required by the process. The varying finger and hand engagement for the different connector locations were factored into the parametrization to encompass a broader range of manually completed tasks. It was found that operator forces in finger engagement for manual assembly could be effectively represented by a limited set of measurable parameters.

Electrically assisted manufacturing (EAM) is defined as the direct application of electricity to a workpiece in situ with a manufacturing process. This is commonly used in forming to reduce the flow stress and increase the ductility of metals. Under certain conditions, there seem to be effects of the electricity that occur in addition to the inherent resistive heating in metals. This electroplastic effect is often deduced by estimating temperatures through analytical or numerical simulations and comparing this to the temperatures required to effect thermal stress reductions observed in experimental tests. For tests which utilized pulsed or AC currents, an RMS current value may be used to simplify simulations since current transience can be averaged to a constant representative value. However, there is often no justification of this assumption and it is possible that assumption could lead to erroneous results. Various assumptions applied to EAM research are explicitly explored herein to determine their validity in thermal estimations. It was concluded that AC, square wave, and sawtooth currents at frequencies greater than 1 Hz, or pulses from power supplies with significant ripple, can be approximated with a DC current of similar RMS value to obtain similar thermal estimations. Simulation geometries should incorporate as much of the experimental setup as possible. An example from literature was used to test several other assumptions as well, including the use of analytical simulations, rather than numerical.

Unexpected downtime from equipment failure has increased due to a production line’s mechanization to meet production throughput requirements. Manufacturing equipment requires accurate prediction models for determining future failure probability in maintenance scheduling. This paper explores using generated failure data under contrived failure scenarios in training a model for a robot with different combinations of data features. Failure data are generated by inducing an anomalous state in the robot arm. The anomalous state is created by attaching weights at the robot end-effector. A random forest regression model diagnoses the anomalous state and determines the anomalous state progression after gathering data. Three different regression models were trained to test accuracy based on different feature selections. The random forest regression predicted 92% of the robot joint operations through five-fold cross-validation, an anomaly in a robot joint 99% of the time, and the correct anomaly state-based on the confusion matrix, 85% of the time. In future research, the anomalous state will represent more targeted component failures on the system through purposeful permanent damage of the robots’ components. Future datasets generated will train other machine health algorithms for estimating component and system damage.

Multimaterial joining has become necessary to continue to reduce vehicle mass in order to further improve fuel economy. Novel processes continue to be developed which are capable of joining various materials. Friction element welding (FEW) is one such process used to join aluminum and steel sheets. This process is well known for its rapid process times, typically less than two seconds, and its extraordinary robustness. FEW is also capable of joining aluminum sheets to the strongest steels used in automotive bodies. A derivative of the FEW process was explored, termed friction element riveting (FER). This process is used to perform aluminum to aluminum joining and shares many of the same benefits as FEW. For example, there remains no need for pre-hole drilling, a distinct advantage of the FEW process. Several strength metrics and the influence of process parameters are defined herein. It was determined that FER is a viable process for use in industrial applications. Through parameter optimization of the welding step, the joint resulted in a cross tension strength and transverse shear strength of 7.37±0.31 kN and 21.4±1.21 kN, respectively, which makes it a competitive technology for similar joining methods.

Multimaterial use in automotive body structures has become essential for continuing vehicle mass reduction. This has created challenges in joining of these materials. Friction element welding (FEW) is a joining process capable of joining aluminum to high strength steels. In this process, the element is driven through the aluminum sheet and friction welded to the steel, securing the aluminum under the head of the fastener. The flow of aluminum during the FEW process is a critical parameter. Poor aluminum flow conditions can result in the protrusions of aluminum chips from the underhead of the fastener. These chips can accelerate corrosion and generate contamination. The flow of aluminum material was observed experimentally and modeled in order to better understand the FEW process and guide parameter selection. Two aluminum alloys, 6061 and 7075, were selected for this study due to their differences in ductility and strength and for their widespread use in the automotive industry. Various experimental methods were explored for revealing the flow of aluminum during processing and validating simulations. The results of this testing reveal that there is minimal radial and vertical mixing within the aluminum substrate. It was also found that the 6061 material exhibits much greater upwards flow of aluminum, while the 7075 alloy experiences more outward flow.

The reduced cost of implementing pervasive industrial sensing networks enables universities to incorporate these tools in engineering curricula. They provide engineering students from increasingly computerized backgrounds, such as mechanical and automotive engineering, the opportunity to work alongside students from technical schools who bring different skill sets than what students may be used to, synthesize historical data, and drive the sensing system’s physical system design and implementation. This paper outlines this convergent curriculum’s initial implementation stage, including the wireless environmental sensing Internet of Things (IoT) network, focusing on laboratory environmental sensing. Students placing many sensors around the lab and on equipment generates a wealth of real-time and historical data for use in the classroom and provides them a tangible example of learning to measure the world around them. This setup parallels the current varied Industry 4.0 state of the manufacturing industry, where Big Data exists but is underutilized, and where additional sensors and intelligent machine data streams are added each year. Students in each class are given a defined portion of a broader roadmap to a fully instrumented and intelligent laboratory environment. In the first step, student-programmed environmental sensors were placed around the lab and provide temperature, humidity, pressure, and gas mixture measures every five minutes. Classroom use of the aggregated data includes visualizing the laboratory and essential equipment’s current status using a Microsoft PowerBI dashboard and historical data visualization and analysis through trend forecasting and outlier detection in Python JupyterLab notebooks. The IoT system’s installation also provided an infrastructure for further study of future student-designed IoT projects.

The manufacturing innovation that underlies advanced products comes about through rational, reasoned design, motivating the need for a manufacturing engineering curriculum within higher education that teaches methodologies for designing manufacturing processes. As an alternative to conventional manufacturing process courses, the authors propose learning outcomes and methods for teaching process design and innovation. Proposed learning outcomes for new process design courses include describing key relationships and directionality between product and process design functions, determining whether a component can be made with a process, selecting process sequences for products based on cost and/or environmental impact, specifying new process designs when needed, and choosing between product/process alternatives. Examples of instructional materials and approaches that are being developed to help meet these outcomes are discussed.

The electroplastic effect (EPE) encompasses the known reduction in flow stress and increase in ductility in a metal resulting from the application of an electric current concurrently with deformation, a phenomenon which cannot be accounted due solely to resistive heating. While several theories have been postulated, the mechanism responsible for the electroplastic effect is still in debate after nearly 60 years of scientific research. A significant amount of research has been conducted on the anomalous effects observed when performing research on the EPE. Particular behaviors which remain elusive are the transient effect of electric current application and the difference between constant and pulsed current applications. Throughout literature, the type of power supply used in electric current application appears to be ignorantly selected and output signals improperly or incompletely documented. It was found herein that the type of power supply used in electrically assisted tensile testing can have a significant effect on the resulting transient mechanical behavior for pulsed current testing within the parameters explored in this research.

Tool wear in machining is generally observed as early and late stage tool wear. During early stage tool wear, the tool is rapidly worn during a break-in period, followed by a stable region of tool wear. After machining more material, the tool reaches late stage tool wear. At this point, tool wear becomes unstable; tool failure occurs quickly or it may take some time. Therefore, late stage tool wear represents a bifurcation point, making it difficult to predict tool wear past this point. Tool wear is well known to be stochastically influenced. Due to this effect, it is difficult to perform studies on late stage tool wear since machining tools will be affected differently up to this point, introducing unknown variables. A novel method is presented in this research which utilized artificial wear to reach late stage tool wear. This method, termed contrived tool wear, may be capable of reducing the stochastic tool wear that occurs during early stage tool wear. As an initial investigation, machining tool inserts were worn by taking several passes over a grinding wheel with the tool rotating in reverse. Several parameters were tested in an attempt to match the natural worn state as close as possible. Subsequent to artificial wear, the inserts were used to machine IN718. The presented method of contrived wear was found to be a good approach, but could not sufficiently replicate the tool wear typically produced in IN718 machining. Future work should aim at implementing a multi-axis approach to enable grinding at various angles to the rake face of the insert.

Governmental regulations and increasing awareness of global greenhouse gas effects have caused a need to rapidly improve energy efficiency across all industries. The transportation industry is one such sector which utilizes a significant amount of energy, thus necessitating efforts to drastically improve the fuel economy of vehicles. A well-known method for achieving this is by vehicle lightweighting, which can be done through the use of multi-material structures. Inherent to the utilization of multiple materials is the joining process, which creates new challenges since vehicles and the supporting infrastructure have traditionally been based on mainly steel designs. Friction element welding (FEW) is a process which can effectively join aluminum sheets to steel. This process involves the formation of a friction weld between a fastening element and lower steel substrate, securing an upper aluminum sheet between the fastener head and the steel. Prior to forming the friction weld, the fastener penetrates the aluminum through frictional heat-based softening and axial load. In relatively soft alloys, the aluminum flow remains close to the fastener’s shaft. However, when penetrating harder alloys, the aluminum flows further away from the shaft, resulting in protrusion outside of the fastener’s head in the final joint. This external material can lead to accelerated corrosion. Laser power is herein explored as a method for rapidly and efficiently heating the workpiece material to preemptively soften it before joining using FEW, thereby improving the flow behavior. It was experimentally determined that laser heating is a viable augmentation of the FEW process for improving aluminum flow during penetration. Furthermore, this process does not significantly increase the processing time of FEW, as heating can be performed in parallel with other essential steps. It can also reduce the total energy consumption under certain parameters.

The overall quality and efficiency of a machined part relies heavily on the tool path that is used. A more recently developed toolpath method is known as trochoidal milling, which is also known by several other terms, such as adaptive milling, circular milling, or volume clearing. In order to follow the contours of the final geometry, this path can give rise to a significant number of direction changes, which result in highly variable force directions on the tool. Chatter, or self-excited vibration that occurs at the tool or workpiece, can therefore be mitigated or avoided since periodic resonance does not have time to increase the vibration’s amplitude. A randomized variation of the trochoidal path is tested in this research. Using this new proposed method, stochastic behavior of the toolpath is implemented. The toolpath consists entirely of circular arcs, which drive the tool in a pseudo-random fashion. The stability of such a path is examined in this work. A key parameter of this path is the allowable radius range of the circular arcs. It was found that the most efficient path utilized a median parameter value, illustrating an overall negative parabolic relationship between path efficiency and tool path radius. It was also discovered that smaller arcs reduced chatter. Future studies will explore the behaviors of this path for milling 3D surfaces.

The lightweighting of vehicle structures has become imperative in order to further improve efficiency within the transportation industry. One method of lightweighting is through the extensive use of multiple materials, offering the ability to selectively add strength where needed while eliminating the addition of extraneous mass. Though effective in design, multi-material automotive structures can be difficult to manufacture. One challenge which arises is the joining of these structures, which often cannot be achieved through traditional methods. Friction element welding (FEW) is a process capable of joining aluminum sheets to a steel substrate. In this process, the aluminum material flows around the fastener shaft by applying an axial load and high rotational speeds. However, when joining high-strength aluminum alloys, this flow becomes diminished due to these materials’ relatively low ductility, resulting in the formation of sharp edges which protrude from the head of the fastener that introduce irregularities and potential contamination. These protrusions, termed chips, accelerate corrosion and are a significant defect. In a previous study, thermal assistance was confirmed as a viable method of reducing chipping by improving the flow of aluminum. An application method for such thermal assistance is developed and tested herein, which utilizes a direct electrical current applied locally to the joint region. The temperature rise was simulated and determined experimentally using a prototype modified downholder. Electrical heating was found to be a viable method of thermal assistance only when forced cooling of the electrodes is used.

Within manufacturing, resistance spot welding (RSW) has been the traditional method of choice when joining steel-steel sheets. However, within the transportation industry, the use of lighter weight materials such as aluminum has become necessary in order to improve fuel economy. This has required the creation of new technologies and adaptations of traditional ones in order to successfully join these materials. One such adaptation, useful in joining aluminum-aluminum sheets, is friction element riveting (FER). This process is similar to the friction element welding process; however, two or more aluminum sheets are secured together between the element head and a relatively small steel sheet, which is termed lower element. Since this is a novel technology, the influence of different sized lower elements is unknown. A study is conducted which varies the diameter and thickness of the lower elements. A simulation was also created to estimate the thermal effects of these various geometries. Strength testing was used to determine the success of each parameter. It was discovered that the maximum joint strength occurs when using a lower element diameter of 25 mm and a thickness of 1.6 mm.

Often today’s designers are relegated to degrade their own output to realize mass production on existing manufacturing capital equipment. These considerations are typically encoded in the design process through “Design for Manufacturing” where constraints on the production processes available and limits to each manufacturing approach are ideally considered early in design. This Design for Manufacturing guise inhibits true designer creativity and the possibility to realize truly revolutionary products. In this paper, the authors formalize their previous introduction of the concept of Manufacturing for Design (MFD), a framework whereby product design creativity is used instead as a motive force of innovation to rethink manufacturing process approaches and assist in facilitating real innovation in manufacturing. This is not proposed as a replacement for DFM, but an extension that can help “question” the manufacturing-based requirements during the development process. It is clear that existing machines cannot be abandoned, but one can instead consider an intermediate augmentation step to feasibly enhance existing capital, to realize new designs in new materials, or to achieve new functional requirements and desires. The blending of MFD and DFM strategies (a proposed MFD|DFM approach) can lead to feasible evolution of manufacturing, and ultimately disruptive process innovation, defined as a rethinking of manufacturing rather than just improvement on existing solutions. Herein is reported the integration of the MFD|DFM concept to two separate education programs, one undergraduate and one graduate. These programs are independent but share resources together with those of an Advanced Manufacturing technical college program to educate students across disciplines and curricula in the dichotomy of Design and Manufacturing, and how the concepts can be properly considered together.

Increasing customer demand for individualized and cost-effective products within shorter production times is reshaping the manufacturing and production environment. Human workers and machines must be able to react to changes with increased flexibility and efficiency. To meet these needs, the tools and products of modern assembly have continually updated and changed, but much work remains to incorporate the natural intelligence of an assembly worker more deeply into future assembly system information flow, both to and from the worker (feedback loop). This work presents a pilot lab evaluation of varied real-time feedback mechanisms for human workers on manual assembly processes to understand better how the method of the information feedback loop to the assembly associate affects their assembly time, variance, and accuracy as well as their perceived acceptance of each mechanism for information feedback. Lego building block models were used as the assembly product to build while wearing a wireless feedback mechanism device. The device incorporated LED lights, vibration, a text screen, and an image screen to provide feedback to the worker. All feedback was provided by an administrator who was able to send commands to the respective feedback methods as needed. Early conclusions in the pilot show a difference in the assembly time depending on both the feedback mechanism used and the complexity of the assembled model. Future work will include expanding the number of participants per test case and increasing the number and types of feedback provided to include non-wearable types such as stationary monitors and sound.

Component failure analysis is sometimes difficult to directly detect due to the complexity of an operating system configuration. Raw time series data is not enough in some cases to understand the type of fault or how it is progressing. The conversion of data from the time domain to the frequency domain assists researchers in making a more discernible difference for detecting failures, but depending on the manufacturing equipment type and complexity, there is still a possibility for inaccurate results. This research explores a method of classifying rolling bearing faults utilizing the total energy gathered from the Power Spectral Density (PSD) of a Fast Fourier Transform (FFT). Using a spectrogram over an entire process cycle, the PSD is swept through time and the total energy is computed and plotted over the periodic machine cycle. Comparing with a baseline set of data, classification patterns emerge, giving an indication of the type of fault, when a fault begins and how the fault progresses. There is a separable difference in each type of fault and a measurable change in the distribution of accumulated damage over time. A roller bearing is used as a validating component, due to the known types of faults and their classifications. Traditional methods are used for comparison and the method verified using experimental and industrial applications. Future application is justified for more complex and not so well-understood systems.

Increasing the productivity and efficiency of milling practices is of high importance with the rapidly changing global economy. To this end researchers have turned to alternative milling toolpaths, such as trochoidal milling, which has been shown to increase tool life with a corresponding reduction in machining time for some applications. To better understand the trochoidal milling process and optimize it for manufacturing scenarios, the modeling of cutting forces must be investigated; semi-mechanistic methods are the focus of this work. The basis for this type of force modeling lies in uncut chip thickness modeling combined with cutting force coefficients and edge force coefficients. With a novel uncut chip thickness model proposed by the authors in a previous work, this investigation looks to understand the dependence of the model coefficients as they relate to trochoidal path parameters along with machining outputs such as maximum cutting force and tool wear. Furthermore, the machining parameters are investigated as to how they relate to the improvement of tool life and cutting force utilizing the Taguchi method, where optimal parameters are found for minimum tool wear and cutting forces. The effects of the trochoidal path on the subsurface of the machined samples as they relate to the machining affected zone, are also investigated in both the radial and axial directions. It is found that tool wear increases the depth of the machining affected zone as does increasing chip thickness.

Digitization of manufacturing allows modeling and diagnosis of equipment failure, but training such systems is difficult without observations of failed states. This paper presents a method whereby failure data, from the baseline to the destruction case, is methodically generated to train machine health models. Creating failure classifiers and predictive models of manufacturing equipment suffer from two problems: training-data accuracy and data representation of lifetime machine failure modes of both components and systems. The Purposeful Failure Method aims to generate defect training data at both the component and system level, from initial defect to final destruction. Bearings are a validation case and are monitored from a healthy baseline state to a maximum-damage state by applying artificial damage. Bearings are used for validating the method, due to prior knowledge of failure progression, as well as their prevalent use. The generated data were analyzed to see if they matched expected failure phenomena. Preliminary results with the Purposeful Failure Method show promise for generating failure data for machine health models; continued work on validating the method on other components is justified.

Electrically assisted manufacturing is the direct application of an electric current or field to a workpiece during a manufacturing operation. In addition to resistive heating, various anomalous effects have been observed experimentally. Since its conception in the 1950s, scientists continue to debate the existence of these so called electroplastic effects (EPEs) due to conflicted results shown throughout literature. A popular theory of electroplasticity is the electron wind, which postulates that there is a transfer of momentum between electrons and dislocations, which assists their motion during deformation. Though refuted both mathematically and experimentally in other types of tests, the electron wind theory, and therefore the existence of electroplasticity, is interestingly supported by the existence of polarity effects in wire drawing. A detailed review of the literature that has shown polarity effects in wire drawing is conducted. While the authors of these publications failed to fully disclose all test parameters, requiring several assumptions to be made, it appears that no mathematical/logical trends could be established. It is hypothesized herein that the velocity of the wire in a wire drawing application can influence the drift velocity of electrons, thereby increasing or decreasing current flow explicitly through the moving section of the wire. In order to test this hypothesis, a fixture was constructed which is capable of passing a current through a moving wire at common wire drawing speeds. Modern sensing equipment was used to measure various electrical parameters during testing. The wire speed effect hypothesis was refuted by experimental testing. While the results of experimental testing thus far indicate the existence of electroplasticity, further testing that includes drawing and force measurements must be conducted in order to fully conclude its existence in the wire drawing application.

Increasing awareness of global warming and strict government regulations have required the automotive industry to pursue lightweighting as an avenue towards increased vehicle efficiency. Lightweight designs typically rely heavily on multi-material use, which enables selective strengthening of critical areas without additional, unnecessary mass. Joining these materials during manufacturing has proven to be a challenging endeavor. Friction element welding (FEW) is one process that is capable of joining aluminum to steel. This two-sided joining technique utilizes a fastener to secure the aluminum sheet by creating a friction weld with the steel sheet. While this process is extremely robust for most materials, the FEW process can result in the extrusion of material from underneath the head of the fastener, termed chipping, which leads to corrosion and aesthetic issues. This behavior is typically seen in high strength aluminum alloys, such as 7075. A solution to chipping is implemented herein, which utilizes a modified downholder to conductively heat the aluminum sheet prior to the FEW process. This heating method was explored experimentally and through various numerical analyses. This method was found to be a viable option for relieving chipping. While the process time was only increased by a maximum of 2.5 seconds, faster, more localized heating should be targeted for future work.

The use of multiple material in the structural components of a vehicle allows for significant weight reduction. Friction element welding (FEW) is a novel method that allows the joining of two or more dissimilar material sheets. A limitation of this process is the chip formation in high strength aluminum alloys, which is observed as the protrusion of thin aluminum segments from under the head of the fastener. Chipping can degrade the joint’s strength over time due to accelerated crevice corrosion. A novel method is proposed to eliminate chip formation using thermal assistance. A grading scheme is developed to quantify the severity of chip formation. The effect of thermal assistance on chipping is analyzed. An investigation is also carried out to validate that the thermal assistance does not negatively affect the process time, energy, and joint strength. Thermal assistance is proposed to be a novel method of overcoming this limitation to allow more widespread use of the FEW process for higher-strength aluminum alloys. Future work will include the development of feasible, rapid methods of heating and measurement of energy utilization for implementation in the industrial environment.