Optimization Of Manufacturing Process Research Articles

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

Digital Twin in Process Planning of the Additive and Subtractive Process Chain for LMD 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 paper explores the use of the digital twin technology for a holistic process planning of combined additive and subtractive processes. The paper 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.

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 [...]

Creasing and folding of paper-based sandwich material–Phenomena and modelling

Creasing and folding are fundamental steps in many manufacturing processes of multi-material paperboard packaging. The complex structure of these materials, which comprise layers of cellulose fibres, aluminium, and polyethylene, coupled with the growing complexity of packaging designs, make these process operations essential to ensure the required structural integrity for packaging as well as their functionality in daily life. This paper introduces an approach for modelling damage in paper-based sandwich materials by integrating fibre-based and cohesive numerical modelling techniques. The results prove the effectiveness of the proposed methodology, opening new possibilities for process design and optimization in packaging manufacturing.

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.

Liquid lithium corrosion of SiC/SiC composites

Silicon carbide fibre reinforced silicon carbide matrix composite (SiC/SiC) is a key structural material for fusion reactors to allow high temperature operation of liquid lithium breeder blankets. This research investigated the corrosion behaviour of two SiC/SiC composites (using Tyranno SA3 and SA4 fibres) immersed in static liquid lithium at 600 °C for 100 h. Utilizing immersion tests and advanced microstructural analysis, the study reveals considerable corrosion in SiC/SiC composites, particularly in local areas enriched with residual carbon or oxygen. The interface carbon layer, while enhancing mechanical properties, induces preferential sites for corrosion cracking, diminishing the material's corrosion resistance. This study offers critical insights into the corrosive interaction between SiC/SiC composites and liquid lithium, highlighting the need for manufacturing process optimization and exploration of alternative interface layers, protective coatings, or avoiding contact between SiC/SiC and molten lithium in breeder blanket system.

Locoregional delivery of IL-13Rα2-targeting CAR-T cells in recurrent high-grade glioma: a phase 1 trial

Chimeric antigen receptor T cell (CAR-T) therapy is an emerging strategy to improve treatment outcomes for recurrent high-grade glioma, a cancer that responds poorly to current therapies. Here we report a completed phase I trial evaluating IL-13Rα2-targeted CAR-T cells in 65 patients with recurrent high-grade glioma, the majority being recurrent glioblastoma (rGBM). Primary objectives were safety and feasibility, maximum tolerated dose/maximum feasible dose and a recommended phase 2 dose plan. Secondary objectives included overall survival, disease response, cytokine dynamics and tumor immune contexture biomarkers. This trial evolved to evaluate three routes of locoregional T cell administration (intratumoral (ICT), intraventricular (ICV) and dual ICT/ICV) and two manufacturing platforms, culminating in arm 5, which utilized dual ICT/ICV delivery and an optimized manufacturing process. Locoregional CAR-T cell administration was feasible and well tolerated, and as there were no dose-limiting toxicities across all arms, a maximum tolerated dose was not determined. Probable treatment-related grade 3+ toxicities were one grade 3 encephalopathy and one grade 3 ataxia. A clinical maximum feasible dose of 200 × 106 CAR-T cells per infusion cycle was achieved for arm 5; however, other arms either did not test or achieve this dose due to manufacturing feasibility. A recommended phase 2 dose will be refined in future studies based on data from this trial. Stable disease or better was achieved in 50% (29/58) of patients, with two partial responses, one complete response and a second complete response after additional CAR-T cycles off protocol. For rGBM, median overall survival for all patients was 7.7 months and for arm 5 was 10.2 months. Central nervous system increases in inflammatory cytokines, including IFNγ, CXCL9 and CXCL10, were associated with CAR-T cell administration and bioactivity. Pretreatment intratumoral CD3 T cell levels were positively associated with survival. These findings demonstrate that locoregional IL-13Rα2-targeted CAR-T therapy is safe with promising clinical activity in a subset of patients. ClinicalTrials.gov Identifier: NCT02208362.

Manufacturing Process Optimization in the Process Industry

This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.

Eliminating Non-Value-Added Activities and Optimizing Manufacturing Processes Using Process Mining: A Stock of Challenges for Family SMEs

Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding leadership type, human resource management practices, innovation orientation, change management, information and communication technology deployment, process maturity, and resource availability. These differences present challenges when leading any change. Process mining (PM) tools can optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”. The present study investigates how a PM project is implemented in an FSME operating in the agri-food sector, focusing on challenges faced in every project phase to extract the most appropriate process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle methodology combined with quality and lean management tools such as the fishbone diagram, Pareto diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing process for an FSME operating in the agri-food sector. To achieve theoretical production capacity (TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle methodology allowed for the identification of five critical causes. An action plan to eliminate causes was proposed to the FSME managers.

Enhancing interlaminar bonding quality estimation in laser-assisted fiber placement of CF/PEKK composites: A correction factor approach for improved prediction of intimate contact

This study introduces an innovative Modified Mantell and Springer (MMS) model to more accurately assess degree of intimate contact with improved accuracy for laser-assisted fiber placement (LAFP), which also significantly enhances the subsequent degree of healing estimation and enables the presentation of an improved degree of bonding. Process parameters such as placement speed and consolidation forces, and fiber orientation are identified as key determinants of bonding quality. Lower placement speeds and higher consolidation forces are linked to increased healing and intimate contact between layers, crucial for achieving desired degree of bonding. The MMS model proves effective in capturing the influence of fiber orientation, revealing that [0°/0°] orientation exhibits superior bonding strength. Validation through T-peel tests provides tangible evidence of the model's precision, aligning fracture surface observations with model predictions. This research contributes to a comprehensive understanding and prediction of composite bonding quality, offering valuable insights for optimizing manufacturing processes and enhancing the mechanical performance of composites manufactured with LAFP.

Comparative analysis and experimental exploration of the milling process in the machining of Inconel 825 material under MQL

ABSTRACT With the flood cooling technique approach, it is impossible to fulfill the concerned environmental regulations and increase productivity while machining material that is infamously tough to cut. To address the conscious environmental regulation in the metal cutting industry, an effect must be made to reduce the industry process impact on environmental pollution along with manufacturing process optimization. Therefore, an eco-friendly MQL technique was investigated associated with machinability characteristics and compared results with dry condition while end milling of Inconel 825 material. The results revealed that MQL significantly reduced cutting temperature (CT) and surface roughness (Ra) to maximum levels of 70% and 43%, respectively. Further, optimum conditions were found using Taguchi method under MQL cooling. Additionally, using 3D surface plots, the interaction impact of process parameter on “Ra” has been investigated. For end milling Inconel 825 material, the MQL machining method has been proven as the most feasible alternative over dry condition.