Indiana University-Purdue University Indianapolis (IUPUI) / Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization. The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.

<p>The manufacturing industry widely employs sound monitoring inspired by the ability of operators that can detect problems based on the sounds that machines emit. This monitoring serves as an integral component for predictive maintenance and productivity estimation. To facilitate real-time monitoring, edge devices are employed to manage and collect sound data. A streamlined Convolutional Neural Network (CNN) model was proposed, designed to execute all necessary computations for predictions, taking into consideration the limited computational resources of edge devices. Comparative analysis with renowned CNN models, namely VGG16, VGG19, ResNet-50, and YAMNet, reveals that the proposed CNN model is highly effective in event prediction from sound classification. Remarkably, the proposed model only required 2% of the prediction time as compared to the slowest and most complex model, while preserving an overall prediction accuracy of 98.9%. To balance the minor accuracy trade-off due to the simplicity of the proposed CNN architecture, an algorithm based on the First-In, First-Out (FIFO) queue system was developed. This method led to a reduction in the prediction error rate by up to 25% within a certain interval between the queue elements, in contrast to systems that do not implement this algorithm. The input feature adopted was the normalized Log-Mel spectrum with a duration of one second. A grid search method was utilized for hyperparameter tuning, with the aim of identifying the optimal hyperparameter combination within the constraints of the simplified CNN model architecture. To substantiate the real-time monitoring performance and superiority of the proposed CNN model, the same workflow was applied to the grain leg and plasma cutting machine using sound data collected from each. The results affirmed that the combination of the proposed CNN model and the developed algorithm exhibited exceptional performance under real-world conditions. In conclusion, for real-time monitoring that employs edge devices, the usage of a simplified CNN model and a customized algorithm is advocated to ensure continuous real-time monitoring devoid of errors or network instability.  </p>

Product lifecycles are complex heterogeneous systems. Applying control methods to lifecycles requires significant human capital. Additionally, measuring lifecycles relies primarily on domain expertise and estimates. Presented in this dissertation is a way to semantically represent a product lifecycle as a cyber-physical system for enabling the application of control methods to the lifecycle. Control requires a model and no models exist currently that integrate each phase of lifecycles. The contribution is an integration framework that brings all phases and systems of a lifecycle together. First presented is a conceptual framework and technology innovation. Next, linking product lifecycle data dynamical is described and then how that linked data could be certified and traced for trustworthiness. After that, discussion is focused how the trusted linked data could be combined with machine learning to drive applications throughout the product lifecycle. Last, a case study is provided that integrates the framework and technology. Integrating all of this would enable efficient and effective measurements of the lifecycle to support prognostic and diagnostic control of that lifecycle and related decisions. / Ph. D. / The manufacturing sector is on a precipice to disruptive change that will signifcantly alter the way industrial organizations think, communicate, and interact. Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, inexpensive and easy to implement technologies to integrate the people, processes, and things across various enterprises are still not available to the entire value stream. Industry needs technologies that use cyber-physical infrastructures efectively and efciently to collect and analyze data and information across an enterprise instead of a single domain of expertise. Meeting key technical needs would save over $100 billion annually in emerging advanced manufacturing sectors in the US. By enabling a systems-thinking approach, signifcant economic opportunities can be achieved through an industrial shift from paper-based processes to a digitally enabled model-based enterprise via the digital thread. The novel contribution of this dissertation is a verifed and validated integration framework, using trusted linked-data, that brings all phases and systems of the product lifecycle together. A technology agnostic approach was pursued for dynamically generating links. A demonstration is presented as a reference implementation using currently available technology. Requirements, models, and policies were explored for enabling product-data trustworthiness. All methods were developed around open, consensus-based standards to increase the likelihood of scalability. The expected outcome of this work is efcient and efective measurements of the lifecycle to support data-driven methods, specifcally related to knowledge building, decision support, requirements management, and control of the entire product lifecycle.

The world of industrial manufacturing has changed a lot during the past decades. It has gone from a labour-intensive process of manual control of machines to a fully connected and automated process. The next big leap in industrial manufacturing is known as industry 4.0 or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor. This change has proven challenging to implement into existing factories many with the intended lifespan of several decades. One of the single most important parameters to measure is the operating hours of each machine. This information can help companies better utilize their resources and save huge amounts of money.  The goal is to develop a solution which can track the operating hours of the machines using image analysis and the signal lights already mounted on the machines. Using methods commonly used for traffic light recognition in autonomous cars, a system with an accuracy of over 99% during the specified conditions, has been developed. It is believed that if more diverse video data becomes available a system, with high reliability that generalizes well, could be developed using similar methodology. / Industriell tillverkning har förändrats mycket under de senaste decennierna. Det har gått från en process som krävt mycket manuellt arbete till en process som är nästan helt uppkopplad och automatiserad. Nästa stora steg inom industriell tillverkning går under benämningen industri 4.0 eller smart tillverkning. Med industri 4.0 kommer en ökad integration mellan IT-system och fabriksgolvet. Denna förändring har visat sig vara särskilt svår att implementera i redan existerande fabriker som kan ha en förväntad livstid på flera årtionden. En av de viktigaste parametrarna att mäta inom industriell tillverkning är varje maskins operativa timmar. Denna information kan hjälpa företag att bättre utnyttja tillgängliga resurser och därigenom spara stora summor pengar. Målet är att utveckla en lösning som, med hjälp av bildanalys och de signalljus som maskinerna kommer utrustade med, kan mäta maskinernas operativa timmar. Med hjälp av metoder som vanligen används för trafikljusigenkänning i autonoma fordon har ett system med en träffsäkerhet på över 99% under de förutsättningar som presenteras i rapporten utvecklats. Om mer video med större variation blir tillgänglig är det mycket troligt att det går att utveckla ett system som har hög pålitlighet i de flesta produktionsmiljöer.

The U.N. projects the world population to reach nearly 10 billion people by 2050, which will cause demand for manufactured goods to reach unforeseen levels. In order for us to produce the goods to support an equitable future, the methods in which we manufacture those goods must radically change. The emerging Circular Economy (CE) concept for production systems has promised to drastically increase economic/business value by significantly reducing the world’s resource consumption and negative environmental impacts. However, CE is inherently limited because of its emphasis on recycling and reuse of materials. CE does not address the holistic changes needed across all of the fundamental elements of manufacturing: products, processes, and systems. Therefore, a paradigm shift is required for moving from sustainment to sustainability to “produce more with less” through smart, innovative and transformative convergent manufacturing approaches rooted in redesigning next generation manufacturing infrastructure. This PhD research proposes the Helical Economy (HE) concept as a novel extension to CE. The proposed HE concepts shift the CE’s status quo paradigm away from post-use recovery for recycling and reuse and towards redesigning manufacturing infrastructure at product, process, and system levels, while leveraging IoT-enabled data infrastructures and an upskilled workforce. This research starts with the conceptual overview and a framework for implementing HE in the discrete product manufacturing domain by establishing the future state vision of the Helical Economy Manufacturing Method (HEMM). The work then analyzes two components of the framework in detail: designing next-generation products and next-generation IoT-enabled data infrastructures. The major research problems that need to be solved in these subcomponents are identified in order to make near-term progress towards the HEMM. The work then proceeds with the development and discussion of initial methods for addressing these challenges. Each method is demonstrated using an illustrative industry example. Collectively, this initial work establishes the foundational body of knowledge for the HE and the HEMM, provides implementation methods at the product and IoT-enabled data infrastructure levels, and it shows a great potential for HE’s ability to create and maximize sustainable value, optimize resource consumption, and ensure continued technological progress with significant economic growth and innovation. This research work then presents an outlook on the future work needed, as well as calls for industry to support the continued refinement and development of the HEMM through relevant prototype development and subsequent applications.

This thesis presents a multi-physics-based approach to the design and analysis of smart cutting tools for emerging industrial requirements, within an innovative design process. The design process is in stages according to design specifications and requires analysis, conceptual design, detailed design, prototype production and service testing. The research presented in the thesis follows the design process but focuses on the detailed design of the smart turning tool, including mechanical design, electrical wiring and sensor circuitry, embedded algorithms development, and multi-physics-based simulation for the tool system integration, design analysis and optimisation. The thesis includes the introduction of the research background, a critical literature review of the research topic, a multi-physics-based design and analysis of the smart cutting tool, a mechanical structural detail design of the prototype smart turning tool, the electrical system design focusing on cutting force measurement and embedded wireless communication features, and the final experimental testing and calibration of the smart cutting tool. The contributions to knowledge are highlighted in the conclusions chapter towards the end of the thesis. The research proposes multi-physics-based design and analysis concepts for a smart turning tool, which can measure the cutting forces on a 0.1 N scale and can also be used to monitor the tool condition, particularly for ultraprecision and micro-machining purposes. The smart turning tool is a sensored tool, constructed with wireless and plug-and-produce features. The tool design modelling and simulation was undertaken within a multi-physics modelling and analysis environment-based on COMSOL. This integrates the piezoelectric physics with mechanical structural design and radio frequency electronic communications of cutting force signals. The multi-physics simulation method takes account of all design-mechanics-physics-electronics analysis and transformations simultaneously within one computational environment, including FEA analysis, modal analysis, structural deformation, lead piezoelectric effect and wireless data/signal simulation. With the multi-physics simulation developed, the integrated design of the smart turning tool and its performance can be physically analysed and optimised in a virtual environment. The tool design process follows the total design methodology, which can be strictly executed in several design stages. Both mechanical and electrical design of the smart cutting tool are embodied into the tool detail design. The tool mechanical structure is systematically built from the selection of the tool material, through the structure analysis and further progressed with static force – strain/stress transformation, equivalent force measurement and calibration. The electrical circuitry was systematically developed from developing the customised charge amplifier, detail design of the main circuitry and coding development procedure, preliminary PCB fabrication and multi-sensor port PCB development, as well as the real-time cutting force monitoring programming and interface coding. The experiment calibrations and cutting trials with the tool system are also designed in light of the total design methodology. The experiment procedure for using the smart turning tool is further presented in two different sections. The thesis concludes with a further discussion on the main research findings, which are further supported by the highlighted contributions to knowledge and recommendations for future work.

Purpose – The purpose is to explore how SMEs can initiate transformation towards AI. The purpose will be fulfilled through identifying which opportunities that exist for SMEs and which challenges they are facing. Additionally, we will identify which requirements are necessary to face these challenges. Method – A qualitative approach was most suitable for this study since the purpose is to explore how SMEs can initiate transformation towards AI. It was also suitable since we have collected data through interviews. We have done a single case study where we have studied one supply chain. Finally, we have had an inductive research approach which means that we have gathered a theoretical background which has founded the base and the background for our study, but our interviews have founded the result of the study and created a conclusion based on that. Findings –The result of the study provides identified opportunities with AI for SMEs, what challenges can occur, and which requirements that are needed to face the challenges. The opportunities are identified as forecasting, maintenance and repair, self-optimization, and tracing and tracking. The identified challenges when initiating transformation towards AI are cultural difficulties, lack of external communication, lack of internal communication, limited internal processes, and lack of resources. The requirements are identified as automation, data, strategy, and capabilities. The opportunities, challenges and requirements are summarized in a framework. Theoretical and Managerial Implications – We have contributed to literature by exploring how an SME, as a mass production company, can benefit from AI by identifying opportunities, challenges, and requirements. Additionally, the framework guides SMEs to prepare for opportunities with AI and ensure that they have all of the requirements. Further, by understanding which requirements that are necessary for a transformation and which challenges that can occur, managers can reduce risk of failing projects. Limitations and Future Research –The number of studied companies together with that the study is a single case study limits the generalizability. It creates a suggestion for future research where a wider set of data can be collected. Additionally, we have identified challenges that can occur, however how companies should face these challenges in the best way is a suggestion for future research.

Introduction The paradigm shift in manufacturing that Industry 4.0 brings forth with new advanced technologies and the rapid growth of sensing and controlling technologies enable further visualization and optimization that can contribute to achievingimproved decision-making in manufacturing. A significant new capability is the ability to construct a Digital Twinthat connects the physical and virtual space. However, there are still confusion and obscurity regarding what Digital Twinis and how it can becreated and then used to create value for the company. Therefor the purpose of the thesis is to examine how manufacturing companies can utilize the implementation of Digital Twinand assess Digital Twinin a shop-floor. ➢RQ1: How can DT be beneficial to increase business value in a manufacturing company? ➢RQ2: What changes need to be done in the shop-floor to implement Digital Twin? Methodology A literature review was conducted to provide previous researchand contextwithin the area of Digital Twin. A multiple-case studywas performed at three case companies to gain meaningful insight from a real-world perspective, semi-structured interviews, dialogs, and observations were conductedat the case companies. The analysis was then performed by examining similarities, and dissimilarities between theoretical and empirical data, as well as opportunities in theoretical findings that correspond with challenges in empirical findings. Frame of Reference The literature review increased the authors’ understanding of the research topic and gave context to the concept of Digital Twin. The review is mainly focused on the Digital Twintechnologyand how it is constructed, as well as the applicationsareas. Empirical Findings The empirical findings provide an overview of boththe current and future state of the case companies in relation to organizational, operational, and technological factors. Additionally, it provides a deeper understanding of how shop-floor management is designed at one of the case companies. Analysis The combination of the Frame of Reference and Empirical Findings contributewith important insight on the potential benefits that can be created through the utilizationof Digital Twin, as well as what is requiredin the shop-floor to enable implementation ofDigital Twin. Conclusions The value that can be created utilizing Digital Twinis outlinedand a clearer definition is proposed to avoid misunderstandings and confusion. Requirements that need to be achieved for a successful implementation arecovered as well. A future recommendation is measuring resources and effort in relation to the created value of a Digital Twin.

Smart manufacturing promotes the demand of a new interface for communication with virtual entities such as big data analysis model, digital twin, and autonomous software programs. Although ideal smart manufacturing pursues full automation by self-adoption and self-decision of autonomy, converging human intervention and collaboration with the autonomy have shown significant improvements on productivity and quality, and it expects more advancements on current manufacturing trend. This study aims how to combine human and autonomy based on current technical advancement of smart manufacturing. In detail, creating networks between the entities, developing a new interface for human-autonomy collaboration, and demonstrating the effectiveness of the collaboration are main research topics.<br>

In applications spanning across aerospace, marine, automotive, energy, and space travel domains, composite materials have become ubiquitous because of their superior stiffness-to-weight ratios as well as corrosion and fatigue resistance. However, from a manufacturing perspective, these advanced materials have introduced new challenges that demand the development of new tools. Due to the complex anisotropic and nonlinear material properties, composite materials are more difficult to model than conventional materials such as metals and plastics. Furthermore, there exist ultra-high precision requirements in safety critical applications that are yet to be reliably met in production. Towards developing new tools addressing these challenges, this dissertation aims to (i) build high-fidelity numerical simulations of composite assembly processes, (ii) bridge these simulations to machine learning tools, and (iii) apply data-driven solutions to process control problems while identifying and overcoming their shortcomings. This is accomplished in case studies that model the fixturing, shape control, and fastening of composite fuselage components. Therein, simulation environments are created that interact with novel implementations of modified proximal policy optimization, based on a newly developed reinforcement learning algorithm. The resulting reinforcement learning agents are able to successfully address the underlying optimization problems that underpin the process and quality requirements. / Doctor of Philosophy / Within the manufacturing domain, there has been a concerted effort to transition towards Industry 4.0. To a large degree, this term refers Klaus Schwab's vision presented at the World Economic Forum in 2015, in which he outlined fundamental systemic changes that would incorporate ubiquitous computing, artificial intelligence (AI), big data, and the internet-of-things (IoT) into all aspects of productive activities within the economy. Schwab argues that rapid change will be driven by fusing these new technologies in existing and emerging applications. However, this process has only just begun and there still exist many challenges to realize the promise of Industry 4.0. One such challenge is to create computer models that are not only useful during early design stages of a product, but that are connected to its manufacturing processes, thereby guiding and informing decisions in real-time. This dissertation explores such scenarios in the context of composite structure assembly in aerospace manufacturing. It aims to link computer simulations that characterize the assembly of product components with their physical counterparts, and provides data-driven solutions to control problems that cannot typically be solved without tedious trial-and-error approaches or expert knowledge.