Advanced Numerical Modeling in Manufacturing Processes

In manufacturing applications, a large number of data can be collected by experimental studies and/or sensors. This collected data is vital to improving process efficiency, scheduling maintenance activities, and predicting target variables. This dissertation explores a wide range of numerical modeling techniques that use data for manufacturing applications. Ignorance of uncertainty and the physical principle of a system are shortcomings of the existing methods. Besides, different methods are proposed to overcome the shortcomings by incorporating uncertainty and physics-based knowledge. In the first part of this dissertation, artificial neural networks (ANNs) are applied to develop a functional relationship between input and target variables and process parameter optimization. The second part evaluates the robust response surface optimization (RRSO) to quantify different sources of uncertainty in numerical analysis. Additionally, a framework based on the Bayesian network (BN) approach is proposed to support decision-making. Due to various uncertainties, estimating interval and probability distribution are often more helpful than deterministic point value estimation. Thus, the Monte Carlo (MC) dropout-based interval prediction technique is explored in the third part of this dissertation. A conservative interval prediction technique for the linear and polynomial regression model is also developed using linear optimization. Applications of different data-driven methods in manufacturing are useful to analyze situations, gain insights, and make essential decisions. But, the prediction by data-driven methods may be physically inconsistent. Thus, in the fourth part of this dissertation, a physics-informed machine learning (PIML) technique is proposed to incorporate physics-based knowledge with collected data for improving prediction accuracy and generating physically consistent outcomes. Each numerical analysis section is presented with case studies that involve conventional or additive manufacturing applications. Based on various case studies carried out, it can be concluded that advanced numerical modeling methods are essential to be incorporated in manufacturing applications to gain advantages in the era of Industry 4.0 and Industry 5.0. Although the case study for the advanced numerical modeling proposed in this dissertation is only presented in manufacturing-related applications, the methods presented in this dissertation is not exhaustive to manufacturing application and can also be expanded to other data-driven engineering and system applications

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions

Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. It implies that the proposed data-driven framework can lead to similar manufacturing decisions with reduced costs and time

The impact of data-driven technologies on supply chain design

Recent supply chain disruptions following Covid-19 and international crises have led to changing paradigms in supply chain design. Likewise, data-driven technologies housed under the term Industry 4.0 have an increasing impact on how supply chains are orchestrated and shaped. This paper gives an overview to several examples of recent and expectable trends in supply chain design. Advanced manufacturing technologies, data-driven technologies in logistics and supply chain management, electrification of vehicles, as well as microchips and semiconductor manufacturing are described as representative drivers of new forms of supply chain design. In this context, a special emphasis is devoted to European initiatives such as the European Chips Act or the European Battery Alliance. Examples such as manufacturing ecosystems or platform based manufacturing are given as well as locally independent supply chains that provide potentials for supply resilience and sustainability. The paper concludes with a research agenda that includes seven areas for future research, including changes in supply chain structure, changes in inter-firm interaction, integration of small and medium-sized enterprises, changing roles of humans and new forms of business models and collaboration. In this context, the interrelations between technologies (product and production level) as well as the research avenues must be emphasized

'Modern Capitalism' in the 1970s and 1980s

John Cornwall built his analysis of Modern Capitalism on a combination of two strands of thought; the Schumpeter-Svennilson view of capitalist development as a process of qualitative change driven by innovation and diffusion of technology, and the Kaldorian idea of static and dynamic economies of scale in manufacturing as the driving force behind economic progress in the industrialized world. Combining these (and other) insights into a coherent perspective on modern economic growth was an important achievement in itself. He also provided convincing evidence from a group of industrialized countries in the fifties and sixties that supported his interpretation of the events. What we have done in this paper is to update and extend his empirical analysis using a larger sample of countries and more recent data. We have found that the Schumpeter-Svennilson perspective of growth as a process of qualitative (and structural) change, and the emphasis on the importance of skills and flexibility, has a lot to commend it. On the second set of ideas the evidence is more ambiguous. At least for many of the technologically and economically most advanced countries, manufacturing does not seem to be the ‘engine of growth’ assumed by Kaldor and Cornwall.

Towards data-driven additive manufacturing processes

Additive Manufacturing (AM), or 3D printing, is a potential game-changer in medical and aerospatial sectors, among others. AM enables rapid prototyping (allowing development/manufacturing of advanced components in a matter of days), weight reduction, mass customization, and on-demand manufacturing to reduce inventory costs. At present, though, AM has been showcased in many pilot studies but has not reached broad industrial application. Online monitoring and data-driven decision-making are needed to go beyond existing offline and manual approaches. We aim at advancing the state-of-the-art by introducing the STRATA framework. While providing APIs tailored to AM printing processes, STRATA leverages common processing paradigms such as stream processing and key-value stores, enabling both scalable analysis and portability. As we show with a real-world use case, STRATA can support online analysis with sub-second latency for custom data pipelines monitoring several processes in parallel

Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Additive Manufacturing

The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven in-situ quality monitoring based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning model to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in an additive manufacturing process, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture

Do Markets Value Advanced Service Development?

Purpose: Markets have a proven propensity for valuing research and development (R&D) intensity of manufacturing firms. This paper investigates whether coupling R&D intensity with advanced services (ADS) yields even higher market performance effect. Design/Methodology/Approach: The longitudinal financial and annual report data covered a period from 1994 to 2020 (n = 164, N = 2 844). Panel regression (fixed effects estimator) was used to investigate the relationships between market performance (regressand), R&D intensity (regressor) and annual report-level discourse related to ADS (moderator). Findings: The findings confirm that markets do in fact value R&D intensity of manufacturers more if the manufacturer publicizes ADS. However, in alignment with extant research the direct relationship between market performance and ADS discourse proved to be negative and significant. Originality/Value: The current study shows that ADS publicizing adds to the R&D-driven market value of manufacturing firms. Thus, the study contributes to the literature on financial consequences of servitization. However, it also highlights the challenging nature of ADS strategies.©2022 The Advanced Services Group.fi=vertaisarvioitu|en=peerReviewed