Optimization Of Manufacturing Process Research Articles

Optimisation of powder extrusion moulding process for thick ceramic electrodes of LiCoO2 for enhanced energy-density lithium-ion batteries

Lithium-ion batteries are widely used in portable electronic devices because of their high energy and power density. To enhance the energy density of these batteries, one strategy involves increasing the active material loading by increasing the thickness of the electrodes. In previous studies, we demonstrated that powder extrusion moulding (PEM) is a scalable and efficient method for producing additive-free LTO and LFP ceramic electrodes, which exhibit remarkable electrochemical performance with thicknesses up to 500 μm. This study focuses on the preparation and optimisation of the entire manufacturing process for thick ceramic electrodes of LiCoO2 (LCO) with a high mass loading of 180 mg cm−2 using the PEM process. Different powder loadings of mixtures of LCO and a thermoplastic multi-component binder were explored to obtain a formulation suitable for rheology. The optimal formulation comprises 60 vol% of powder. After binder removal via a combination of solvent and thermal treatment, followed by air sintering, parts with varying porosities were obtained. Striking a balance between porosity and thermal degradation is crucial for achieving optimal electrochemical behaviour. Electrochemical evaluations of the electrodes sintered at 900 °C and 1000 °C revealed areal capacity values as high as 17 mAh cm−2 at C/25 and 7 mAh cm−2 at C/6.25, respectively. These values far exceed the 1−2 mAh cm−2 obtained from commercial LCO electrodes. These results indicate a strategy for the production of electrodes to fabricate lithium-ion batteries with a low ratio of inactive materials and, therefore, with higher energy densities.

Transforming Tablet Production in Indonesia with 66% Faster Manufacturing

This study addresses the high production costs of tablet products at PT. XYZ, a pharmaceutical company in Indonesia, due to long cycle and takt times. Using Causal Branching and KATA methods, data were collected over eight months through observations, interviews, and quantitative analysis. The study achieved a 43% reduction in reject foils from destripping, a 40% decrease in total cycle time, and a 66% reduction in takt time. These results highlight effective strategies for optimizing manufacturing processes and reducing costs in the pharmaceutical industry. Highlights: Cost Reduction: 43% reject foils, 40% cycle time reduction. Methodology: Used Causal Branching and KATA for problem-solving. Data Collection: Eight-month study with observations, interviews, quantitative analysis. Keywords: Tablet production, Cycle time reduction, Takt time optimization, Causal Branching, KATA method

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process

Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics. The densification process during CVI critically influences the final performance, quality, and consistency of these composite materials. Experimentally optimizing the CVI processes is challenging due to the long experimental time and large optimization space. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model’s reliability and robustness. Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and an incomplete description of the underlying physics.

Implications of Generative Al and Machine Learning on Automotive Industry Development & Reduction of Carbon Footprint: An Analysis of the U.S. Economy Perspective

The U.S. automotive industry is instrumental to the economy and contributes significantly to the American GDP, employment, and global trade. The principal objective of this research paper is to examine the implications of Generative AI and ML in advancing the automotive industry from the U.S. economic perspective. Generative AI is the latest frontier in artificial intelligence software development, where algorithmic generation can be achieved across various types of content: text, images, audio, and video. The generative AI in the Automotive market at the global level had witnessed boisterous growth and commanded a value of approximately $389.47 million by 2023. The analysis exposed that North American regions are dominating the market, which was attributed to high technological infrastructure, along with partnerships among automotive companies with research institutes and universities to foster AI innovations. Application analysis exposed that Advanced Driver Assistance Systems (ADAS) had the biggest market share, indicating a strong focus on developing and implementing Generative AI technologies to enhance driver safety and vehicle autonomy. Followed by, Connected Car Technologies, representing growing efforts towards implementing generative AI solutions that will improve vehicle connectivity, infotainment, and user experience. The impact of Generative AI and Machine Learning can be witnessed in terms of virtual prototyping, generative automotive designs, consolidation with the CAD system, supply chain optimization, Sensor Fusion and Perception Enhancement as well as automotive manufacturing process optimization.

Utilization of steelmaking slag in cement clinker production: A review

The cement industry significantly contributes to global CO2 emissions, with a notable portion attributed to limestone calcination during cement clinker production. To promote carbon-neutral building practices, alternative approaches are being explored to replace raw materials in cement manufacturing and mitigate CO2 emissions. Steelmaking slag, enriched with noncarbonated CaO from limestone decarbonization during steel production, shows potential as a promising raw material for cement manufacture to reduce CO2. Despite their environmental benefits, most steelmaking slags are underutilized, with limited recognition of their CO2 mitigation potential. Moreover, challenges to using steelmaking slag as a raw material to manufacture cement clinker exist due to the mineral and chemical compositions of each slag type. This study explored potential pretreatment methods to enhance slag's performance as a cement raw material and the research on utilizing steelmaking slag, including blast furnace slag (BFS), Kanbara reactor (KR) slag, basic oxygen furnace (BOF) slag, electric arc furnace (EAF) slag, ladle furnace (LF) slag, and stainless slag, in cement clinker manufacture. Consequently, steelmaking slag could be used as a raw material to manufacture cement clinker to reduce CO2 emissions. However, additional research is needed, including slag pretreatment methods and cement clinker manufacturing process optimization.

Digital Twin in Process Planning of the Additive and Subtractive Process Chain for Laser Metal Deposition and Micro Milling of Stainless Steel

Abstract Additive and subtractive (Add/Sub) manufacturing processes are increasingly being combined to produce complex parts with unique geometries and properties. However, the design of such combined processes is often challenging as it requires a deep understanding of the interaction between the different processes. On the other hand, digital twin (DT) technology has become a powerful tool in recent years for optimizing manufacturing processes. This article explores the use of the digital twin technology for a holistic process planning of combined additive and subtractive processes. The article describes the integration of laser metal deposition (LMD) and micro-milling prediction models of resulting geometry (width and height), hardness, and surface roughness into the digital twin. This is then used for combined process planning to achieve different target values regarding resulting geometry and surface roughness. For the planning of this combined process chain, further criteria such as tool life, energy, and process time are considered in the optimization, showing the potential for sustainable and efficient production. Sensorless cutting force estimation is also used to detect small cutting forces, with the potential to use this as a soft sensor for roughness estimation. The measured width, height, and roughness as a result of the process parameters suggested by the optimization algorithms showed a mean absolute percentage error (MAPE) of 4, 17, and 16%, respectively.

A study on the effect of the geometric properties and surface defects on silicon chip flexibility for wearable electronics

A study on the effect of the geometric properties and surface defects on silicon chip flexibility for wearable electronics

Electrical, ferroelectric and electro-caloric properties of lead-free Ba0.85Ca0.15Ti0.95(Nb0.5Yb0.5)0.05O3 multifunctional ceramic

In this study, lead-free Ba0.85Ca0.15Ti0.95(Nb0.5Yb0.5)0.05O3 (BC15TYN5) ceramic has been successfully obtained through the conventional solid-state reaction. It has been found the multifunctionality of the BC15TYN5 ceramic. It exhibits ferroelectric properties, with a significant remanent polarization value of about Pr = 9.36 μC/cm2, and a recoverable energy storage density value of about Wrec = 42.21 mJ/cm3. Additionally, it displays a dielectric permittivity of about ε′r = 7230 around TC = 80 °C, and an electro-caloric strength value of about ξ = 0.12 K mm/kV. It's worth noting that these values surpass those typically found in BaTiO3 ceramics elaborated with similar experimental conditions. Electrical study (impedance, modulus, and conductivity characteristics) is essential for characterizing the BC15TYN5 material, determining its suitability for various technological applications, and optimizing manufacturing processes. The Cole-Cole plots exhibited two relaxations associated with the contribution of the grains and grain boundaries. The relaxation times for both grains and grain boundaries showed temperature-dependent variation following Arrhenius relation. The measured AC conductivity was evaluated by applying the Jonsher power law. Based on the thermal evolution of the exponent S, it was confirmed that the conduction mechanism follows the Correlated Barrier Hopping (CBH) model. These properties make BC15TYN5 ceramic suitable for applications such as dielectric capacitors, ferroelectric random-access memories, electrical energy storage units, and electro-caloric refrigeration systems.

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

AbstractThe issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

Improving Maraging Steel 350 Machinability via Wiper Insert-Enhanced Face Milling

Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing.

Optimization of electrode manufacturing processes from the perspective of mechanical properties

Abstract As the fundamental part of battery production, the electrode manufacturing processes have a key impact on the mechanical and electrochemical properties of batteries. A comprehensive study is designed in this paper to reveal the manufacturing effect from the perspective of mechanical properties. Initially, the electrodes samples are prepared after different manufacturing process, i.e., slurry mixing, coating, drying, calendering, slitting, punching, cutting, assembling, electrolyte filling and formation. The effects of these processes on the mechanical response and morphology of electrodes are investigated. The calendering process significantly enhances the strength of both anode and cathode while providing a more uniform distribution of particles on the electrode. Besides, according to literature studies, the slurry mixing process has a critical impact on electrode deformation and failure. Hence, the effects of compaction density ρc and binder content Bc are further discussed to improve the slurry mixing and calendering processes. The active layer will debond from the current collector during cathode failure process as ρc and Bc decreases. This study provides valuable suggestions for optimizing the mechanical response of electrodes under key electrode processes.

Experimental investigation and optimization of the additive manufacturing process through AI-based hybrid statistical approaches

Experimental investigation and optimization of the additive manufacturing process through AI-based hybrid statistical approaches

Additive Manufacturing—Process Optimisation

The realm of Additive Manufacturing (AM), often referred to as 3D printing, encompasses a broad spectrum of applications and methodologies, each contributing distinctively to the progress of this dynamic field [...]

Review of path planning of welding robot based on spline curve

This paper assesses the efficacy of intelligent path planning for welding robots utilizing splines. Traditional path planning methods can result in inefficient and inaccurate welding operations. The study reviews current research and case studies to appraise the practical application of spline-based path planning across diverse industrial scenarios. It underscores the benefits of discovering the shortest path and reducing cycle time while acknowledging challenges such as calibration accuracy and sensitivity to sensor data noise. The introduction of artificial intelligence algorithms in automobile welding path planning enables a more precise replication of the body's design curve, ensuring the continuity and smoothness of the welding process. This, in turn, fosters further automation and optimization of the automotive welding manufacturing process. The current research concentrates on integrating intelligent optimization algorithms and spline curves to provide an efficient and intelligent method for welding path planning. Intelligent path planning based on spline curves demonstrates significant potential in enhancing welding efficiency, determining the shortest path, and holds promising applications in the broader research field of welding path planning.

Optimized manufacturing process of homogeneous microwave-sintered blocks of KLS-1 lunar regolith simulant

The use of materials from the Moon's surface based on In-Situ Resource Utilization (ISRU) is gaining considerable attention as a means of constructing infrastructure to sustain a human presence there. In particular, the sintering of lunar regolith appears a promising way to fabricate infrastructure on the lunar surface. This study presents a method to manufacture homogeneous blocks using a lunar regolith simulant. The tested simulant, KLS-1, was sintered under diverse conditions in a multi-mode microwave sintering furnace. Successful sintering requires the sample to be heated throughout, without generating a temperature gradient. This was achieved by testing various heating rates and sample sizes. X-ray computed tomography was used to assess the pore structure of sintered samples. While the optimized sintering conditions resulted in the formation of internal cracks by outgassing, preheating the KLS-1 in a vacuum oven at 250 °C effectively prevented the formation of internal cracks. The homogeneity of the blocks was confirmed by coring and measurements of true density, bulk density, porosity, and unconfined compressive strength. The robust reproducibility and similarity in physical and mechanical properties validate the viability of microwave sintering in manufacturing large and uniform blocks suitable for application as construction materials on the Moon.

Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications

The integration of robotics in manufacturing processes has undergone significant advancements, reshaping the landscape of industrial production and introducing transformative changes. This review explores the latest developments in robotics within the manufacturing sector, shedding light on the technological breakthroughs and their implications on the workforce. The recent surge in robotics adoption in manufacturing has been driven by the pursuit of increased efficiency, precision, and cost-effectiveness. From traditional robotic arms to cutting-edge collaborative robots (cobots) that work alongside human operators, the manufacturing industry has witnessed a paradigm shift. Advanced robotic systems equipped with artificial intelligence (AI) and machine learning algorithms enable adaptive and autonomous decision-making, further enhancing their capabilities. Automation in manufacturing, fueled by robotics, has yielded numerous benefits such as improved product quality, reduced production cycle times, and increased overall productivity. The review delves into case studies and real-world applications where robotics have proven instrumental in optimizing manufacturing processes across diverse industries. However, as the manufacturing landscape evolves, the implications for the workforce cannot be overlooked. The integration of robotics poses challenges and opportunities for human workers. While automation has the potential to eliminate routine and hazardous tasks, it also necessitates upskilling and reskilling of the workforce to operate, program, and maintain robotic systems. The review examines the socio-economic impacts of increased automation, discussing potential shifts in employment patterns and the need for a strategic approach to workforce development. This review provides a comprehensive analysis of the advances in robotics within manufacturing and their profound implications on the workforce. Striking a balance between automation and human collaboration is crucial for the future of manufacturing, emphasizing the importance of proactive strategies to harness the benefits of robotics while ensuring a resilient and adaptable workforce.

What determines the performance of digital transformation in manufacturing enterprises? A study on the linkage effects based on fs/QCA method

The achievement of high-quality development in the manufacturing industry can be effectively realized through the process of digital transformation. The enhancement of digital transformation performance in manufacturing enterprises has emerged as a prominent topic of interest for both corporate entities and the academic community. Drawing upon the theoretical framework of the TOE model, this article presents a comprehensive analytical framework aimed at understanding the performance of digital transformation in manufacturing enterprises. By assessing a sample of 180 manufacturing enterprises currently undergoing digital transformation, this study employs the fuzzy-set qualitative comparative analysis (fs/QCA) methodology to study the interrelated effects and strategic choices pertaining to technology, organizational structure, and environmental conditions, all of which contribute to the improvement of digital transformation performance in manufacturing enterprises. The findings of the research indicate the following: (1) The attainment of high performance in digital transformation in manufacturing enterprises does not solely rely on a single condition, whether it pertains to the optimization of manufacturing processes or the development of new products. (2) High performance in digital transformation is the result of multiple interacting factors. Various causal configurations, such as “organization-environment oriented”, “all-factor driven”, and “technology-environment oriented”, which have the characteristics of “multiple concurrency” and “different paths leading to the same goal.” (3) In comparison, achieving high performance in manufacturing process optimization requires a higher degree of complexity than attaining high performance in new product development. Additionally, it is challenging to achieve high performance in new product development with a sole reliance on a single technological foundation and external environmental support. These research conclusions contribute to the existing body of knowledge on digital transformation performance, enhancing our understanding of the complex factors that underlie high performance in digital transformation in manufacturing enterprises. Importantly, they hold practical significance in improving the performance of digital transformation in the manufacturing sector.

STATISTICAL TECHNIQUES IN PRECISION METROLOGY, APPLICATIONS AND BEST PRACTICES

Statistical techniques play a pivotal role in precision metrology, ensuring accurate measurements and reliable data analysis in various industries. This review delves into the applications and best practices of statistical techniques in precision metrology, highlighting their significance in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. In precision metrology, statistical methods are employed to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. One of the fundamental applications of statistical techniques is in experimental design, where methodologies such as Design of Experiments (DOE) aid in optimizing measurement processes by identifying influential factors and their interactions. Moreover, statistical process control (SPC) techniques like control charts are widely utilized in monitoring and maintaining measurement processes within specified limits, thereby ensuring consistency and reliability in metrology operations. These techniques enable early detection of deviations or anomalies, facilitating timely corrective actions to prevent the production of non-conforming products. Furthermore, the application of statistical analysis in uncertainty evaluation is crucial for quantifying the reliability of measurement results. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty, considering various sources of error and their contributions to the overall uncertainty budget. In addition to applications, adopting best practices in statistical techniques is essential for ensuring the effectiveness and efficiency of precision metrology processes. This includes proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and interpretation of results with caution. Overall, this review underscores the importance of statistical techniques in precision metrology, emphasizing their role in improving measurement accuracy, reducing variability, and enhancing overall quality in manufacturing processes. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.
 Keywords: Statistics, Precision Metrology, Best Practices, Application, Review.

Exploring the Role of Queueing Theory in Manufacturing: An Analytical Study

Manufacturing system is a combination of humans, machinery and equipment that are bound by a common material and information flow. Queueing theory plays a pivotal role in optimizing manufacturing processes by analysing and modelling the flow of entities, such as materials or tasks, within production systems. This article delves into the significance of queueing theory in manufacturing, highlighting its applications in enhancing efficiency, reducing wait times, and minimizing resource wastage. By examining factors like arrival rates, service times, and system capacities, queueing theory enables the identification of bottlenecks and the design of strategies for smoother operations. This work provides a valuable insights of queueing theory, guidance for decision-making, streamline workflows, and ultimately improves the overall manufacturing performance.

Real-Time Cone-Growth Model for Determination of Pharmaceutical Powder Flow Properties.

The flow properties of pellets or granules are crucial for further processing drug dosage forms. Optimal compression or filling of multiparticulate dosage forms into capsules is influenced by forces between discrete particles, which could be partially characterized by flow properties. Several techniques have been developed to examine flowability, including static and dynamic methods applying empirical studies and up-to-date chaos theory; however, the newest methods seem only to be powerful with the supplementation of empirical principles. Our experiments try to refine both the technique of analysis and the methods, by finding new, alternative ways. Our approach to the flowability measurements was to set up a dynamic time-dependent model that combined empirical observations and chaos theory on a geometrical basis, thus finding new characteristics regarding the flow properties of pellets and granules that could be relevant for drug developers. Our findings indicate that sphericity and particle size are the most significant factors influencing the flowability of pharmaceutical multiparticular preparations. Furthermore, this study confirms that integrating chaos theory and empirical observations in a time-dependent dynamic model provides a comprehensive understanding of particle flow behavior, pivotal for optimizing manufacturing processes.