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Automation feasibility assessment tool
No full textIndustrial automation has become an inseparable part of the current production plants. Since robotic systems are the game-changers of modern production plants, employing them efficiently helps the production owners to stay competitive in the growing market. Cognitively demanding processes such as meat processing steps have seen fewer advancements in automation and robotics. In addition, automation in this sector is usually considered for particular cases by experiments and without a systematic approach. In this Engineering Doctorate Thesis, a decision-making tool is designed to identify low-hanging fruit cases for automation and reflect on the automatability of the production processes at a meat production plant named Ekro. Despite a few efforts towards automation, most of the critical processes at Ekro are done manually. Due to the variety of processes, the automatability is assessed through the stages of the designed tool addressing multiple design challenges. The developed tool, "Automation Feasibility Assessment ” or “AFA”, includes analysis of different layers of production, from workstations to subtasks of a process. For this reason, a rough assessment called “scope determination” is initially done to prefilter processes before a detailed analysis. Then, the filtered processes are analyzed elaborately based on the process's goals and sub-goals. Detailed analysis of the processes called “Task Analysis” helps to determine the complexity of the manual operations. The output of the task analysis block is used to create the critical characteristics for automation. Capabilities and constraints of technologies directly affect the feasibility of automation. Therefore, it is necessary to assess the current technology's capabilities and constraints from different aspects. The main contribution of this EngD project is presented as the “Solution Space” which takes inputs from the task analysis, incorporates processes and tasks, and offers recommendations and insights based on existing technology and capabilities. This would mark out the low-hanging fruit cases of automation and demonstrate the complex tasks in a process from the automation perspective. Solution availability is the main criterion for introducing a task or process as a suitable candidate for automation. The output of this project offers insights into the automatability of processes at Ekro which is based on the different levels of detail. Additionally, it generates information regarding automation complexity, considering both the present process and potential future modifications. The validity of the designed decision-making tool is checked by holding trial runs and assessing the achieved insights. The practicality of this tool as well as its compatibility with the user’s knowledge is also a validation criterion
Future-Centric Design Thinking:a Human-Centred process for addressig and empathising with trends in mobility at Apollo Tyres
No full textInformation processing with silicon-based nonlinear computing units
No full textThis thesis investigates the potential of in-materia computing, specifically through reconfigurable nonlinear processing units (RNPUs), to address the escalating demands for energy-efficient, high-performance computational systems that surpass the limitations of conventional CMOS-based digital circuits. As digital computing struggles to meet the processing speed, computational power, and energy efficiency required for modern applications, this work explores brain-inspired computing paradigms and their integration with existing digital platforms. A comprehensive review in Chapter 2 evaluates brain-inspired systems, identifying research gaps and proposing normalized energy efficiency metrics for fair comparisons across platforms. Chapter 3 introduces RNPUs, presenting novel dynamic characterizations that reveal their time-dependent computational properties, expanding beyond static behaviors reported in prior literature. Chapter 4 demonstrates the application of dynamic RNPU circuits for energy-efficient acoustic time-domain feature extraction, achieving superior performance compared to digital implementations on speech recognition benchmarks. By integrating RNPU-extracted features with an analogue in-memory computing (AIMC) chip, this work mitigates the von Neumann bottleneck, enhancing system compactness and efficiency. Chapter 5 proposes a hardware accelerator architecture leveraging RNPUs for both temporal (audio) and static (image) classification tasks, achieving over 9× energy savings compared to a 45-nm CMOS digital design, supported by a software distiller for automated training and deployment. Finally, Chapter 6 explores RNPU-based Kolmogorov-Arnold networks as an alternative to multi-layer perceptrons, demonstrating comparable performance with reduced computational costs and enhanced interpretability. Despite the volatility of current RNPUs, this thesis highlights their superior energy efficiency and nonlinear processing capabilities, paving the way for future scalable, low-power computing systems with integrated local memory for real-world applications
Future cure technology:Influence of polymer network structure of the tread and sidewall compounds on the tyre performance
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