Hasil untuk "Production capacity. Manufacturing capacity"

T. Sturgeon

Bharat Behl, Yu Dong, Alokesh Pramanik et al.

Maraging steels encounter tremendous aerospace applications, such as in landing gears, rocket motor casing, pressure vessels, satellite launch vehicles, etc. Laser welding is considered one of the most effective manufacturing processes due to its minimal instances of wider heat-affected zones (HAZs), precipitate accumulation, and other benefits. However, it should also be noted that their severe effect is still evident in terms of the tensile strength and fatigue strength of laser-welded maraging steel. This paper provides a critical review of the evolution of microstructural features and mechanical properties of laser-welded maraging steel, including corresponding factors in terms of microstructures and the formation of reverted austenite, as well as precipitation hardening from various studies on maraging steels. We examined the influence of precipitation, reverted austenite, welding, and post-weld heat treatment on mechanical properties like hardness, tensile strength, yield strength, elongation, and fatigue strength of laser-welded maraging steel. It is worth mentioning that the laser welding process is generally insufficient for welding sheets with a thickness over 10 mm or those requiring multi-pass welding. The reheating process becomes unfavorable for maraging steel in the multi-pass welding process since it may induce localized heat treatment. Although hybrid welding may resolve an arising thickness issue, the reversion of austenite and complexity are still difficult to overcome due to the dual nature of welding processes, resulting from the use of both arc and laser. Furthermore, maraging steel produced via additive manufacturing tends to avoid austenite reversion with effective heat treatment prior to any welding process. Post-weld heat treatment and cryogenic treatment have been found to be favorable for desired reverted austenite formation. Finally, the proposed constructive framework specifically applies to the welding process of maraging steel, particularly for aerospace applications.

Alexandra Raluca Borşa (Bogdan), Adriana Păucean, Melinda Fogarasi et al.

The solid waste generated from processing rosehip fruits into jam is valuable due to its rich content in fibres, polyphenols, and carotenoids; it could be valorised as a functional ingredient in a powder form to enrich food products. This study aimed to test its potential as a value-added ingredient, especially to enrich waffle cones with fibres, polyphenols, and carotenoids. In this regard, four formulations of waffle cones were prepared by partially substituting wheat flour with rosehip waste powder at 0%, 10%, 15%, and 20%, reaching concentrations of 0%, 3.7%, 5.7%, and 7.5% of the total batter, respectively. These were assessed for their sensory, textural, and techno-functional properties; proximate composition (including crude fibre); energy value; pH; and colour, as well as the content of carotenoids and polyphenols. The contribution of rosehip powder to the production cost of these waffle cone formulations was also determined. The results showed that using rosehip waste powder as an ingredient reduced the waffle cones powder’s capacity to hold water (from 3.11 g/g to 2.64–3.08 g/g) and to swell (from 4.98 mL/g to 4.23–4.48 mL/g), while it increased their oil-holding capacity (from 0.93 g/g to 0.96–1.19 g/g) and the content in fibre (from 1.58% to 3.41–4.83%), polyphenols (from 400.70 µg/g to 1732.26–2715.69 µg/g), and carotenoids (from n.d. to 6.86–14.28 µg/g); however, the solubility (72.65–75.33%), hardness (2.31–2.83 N), and fracturability (6–8) were not significantly influenced. The sensory acceptability of enriched waffle cones (92–93%) was higher than that of control waffle cones (90%). The production cost of a waffle cone increased by EUR 0.004–0.009 when wheat flour was substituted by rosehip powder in concentrations of 10–20%. In conclusion, to enrich waffle cones with fibres, polyphenols, and carotenoids, at least 10% of wheat flour must be substituted with rosehip waste powder in their manufacturing recipe.

Guido Servetti, Federico Valente, Jérôme Laurent et al.

Powder bed fusion with a selective laser melting (SLM) process is a versatile technology that allows for the manufacturing of complex geometries and lightweight structures. A prototype of a redesigned refrigeration block is made with topology optimization, thereby demonstrating the capabilities and challenges of this approach in terms of design and manufacturing. The geometry obtained was more efficient in terms of thermal performance with respect to the original design, and the simulation of the printing process indicated ways to reduce distortions. Moreover, a demonstrator was printed and measured through X-ray computed tomography (XCT) scanning, showing that the approach used was effective in terms of process parameters, technology used, and materials. In fact, it was found to have a low level of porosity, and although there were some differences in the dimensional comparison, such differences were lower in the areas where greater accuracy was required. The manufacturability was possible because of the appropriate choice of process parameters and the combination of the additive with subtractive manufacturing techniques, such as CNC milling. Overall, the methodology used proved effective for the purpose of the component in terms of thermal efficiency and weight reduction.

Sheshadri Chatterjee, Ranjan Chaudhuri, Sachin S. Kamble et al.

Cutting-edge technologies like big data analytics (BDA), artificial intelligence (AI), quantum computing, blockchain, and digital twins have a profound impact on the sustainability of the production system. In addition, it is argued that turbulence in technology could negatively impact the adoption of these technologies and adversely impact the sustainability of the production system of the firm. The present study has demonstrated that the role of technological turbulence as a moderator could impact the relationships between the sustainability the of production system with its predictors. The study further analyses the mediating role of operational sustainability which could impact the firm performance. A theoretical model has been developed that is underpinned by dynamic capability view (DCV) theory and firm absorptive capacity theory. This model was verified by PLS-SEM with 412 responses from various manufacturing firms in India. There exists a positive and significant influence of AI and other cutting-edge technologies for keeping the production system sustainable.

Namrata Kharate, Prashant Anerao, Atul Kulkarni et al.

This study investigates the complex relationships between process parameters and material properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We examine the effects of key parameters, including biochar content (BC), layer thickness (LT), raster angle (RA), infill pattern (IP), and infill density (ID), on the tensile, flexural, and impact strengths of FDM-printed pure PLA and biochar-reinforced PLA composites. Mechanical testing was used to measure the ultimate tensile strength (UTS), flexural strength (FS), and impact strength (IS) of the 3D-printed samples. The extreme gradient boosting (XGB) algorithm was used to build a predictive model based on the data collected from mechanical testing. Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) techniques were implemented to understand the effects of the interactions of key parameters on mechanical properties such as UTS, FS, and IS. Prediction by XGB was accurate for UTS, FS, and IS, with R-squared values of 0.96, 0.95, and 0.85, respectively. The explanation showed that infill density has the most significant influence on UTS and FS, with SHAP values of +2.75 and +5.8, respectively. BC has the most significant influence on IS, with a SHAP value of +2.69. PDP reveals that using 0.3 mm LT and 30° RA enhances mechanical properties. This study contributes to the field of the application of artificial intelligence in additive manufacturing. A novel approach is presented in which machine learning and XAI techniques such as SHAP, LIME, and PDP are combined and used not only for optimization but also to provide valuable insights about the interaction of the process parameters with mechanical properties.

Konstantin V. Moiseenko, Olga A. Glazunova, Tatyana V. Fedorova

Recent consumer demand for non-dairy alternatives has forced many manufacturers to turn their attention to cereal-based non-alcoholic fermented products. In contrast to fermented dairy products, there is no defined and standardized starter culture for manufacturing cereal-based products. Since spontaneous fermentation is rarely suitable for large-scale commercial production, it is not surprising that manufacturers have started to adopt centuries-known dairy starters based on lactic acid bacteria (LABs) for the fermentation of cereals. However, little is known about the fermentation processes of cereals with these starters. In this study, we combined various analytical tools in order to understand how the most common starter cultures of LABs affect the most common types of cereals during fermentation. Specifically, 3% suspensions of rice, oat, and wheat flour were fermented by the pure cultures of 16 LAB strains belonging to five LAB species—<i>Lacticaseibacillus paracasei</i>, <i>Lactobacillus delbrueckii</i>, <i>Lactobacillus helveticus</i>, <i>Streptococcus thermophilus</i>, and <i>Lactococcus lactis</i>. The fermentation process was described in terms of culture growth and changes in the pH, reducing sugars, starch, free proteins, and free phenolic compounds. The organoleptic and rheological features of the obtained fermented products were characterized, and their functional properties, such as their antioxidant capacity and angiotensin-converting enzyme inhibitory activity, were determined.

Long Hu, Yiming Miao, Gaoxiang Wu et al.

Abstract The Internet of Things (IoT) and Artificial Intelligence (AI) have been driving forces in propelling the technical innovation of intelligent manufacturing, promoting economic growth, and improving the quality of people’s lives. In an intelligent factory, introducing edge computing is conducive to expanding the computing resources, the network bandwidth, and the storage capacity of the cloud platform to the IoT edge, as well as realizing the resource scheduling and data uplink and downlink processing during the manufacturing and production processes. Moreover, the emotion recognition and interaction of the Affective Interaction Intelligence Robot (iRobot), with the IoT cloud platform as the infrastructure and AI technology as the core competitiveness, can better solve the psychological problems of the user. Accordingly, this has become a hot research topic in the field of intelligent manufacturing. In this paper, we describe an intelligent robot factory (iRobot-Factory), adopt a highly interconnected and deeply integrated intelligent production line, and introduce the overall structure, composition, characteristics, and advantages of such a factory in details from the two aspects of cognitive manufacturing and edge computing. Then, we describe the implementation of the volume production of iRobot using iRobot-Factory and look at the system performance experimental results and analysis of the iRobot-Factory and a traditional factory. The experimental results show that our scheme significantly improved both the chip assembly and the production efficiency, while the number of system instructions also decreased significantly. In addition, we discuss some open issues relating to cloud-end fusion, load balancing, and personalized robots to make reference to promoting the emotion recognition and interaction experience of users.

Hartono Widjaja

Salah satu contoh material plastik yaitu, ABS (Acrylonitrile Butadiene Styrene) yang termasuk ke dalam jenis plastik thermoplastik. Thermoplastik adalah bahan sintetik organik yang dapat melumer saat dipanaskan dan dapat dibentuk dibawah pengaruh tekanan. Pada buku “Molding Simulation : Theory and Practice terdapat tabel reasonable design values for the L/t ratio. Pada tabel tersebut disebutkan bahwa flow length atau panjang mampu alir material ABS dengan tebal produk 1mm adalah 100mm-200mm. Penelitian ini bertujuan mengetahui flow length material ABS pada tekanan injeksi terendah dan tertinggi dengan mould temperature 25 °C (mould temperature material plastik yang benar adalah 50 – 80 °C), mengetahui perbandingan antara hasil trial dengan tabel referensi, mengetahui pengaruh tekanan injeksi terhadap flow length material plastik ABS pada runner panjang dan runner pendek. Berdasarkan kondisi dan parameter yang digunakan pada penelitian ini, dihasilkan bahwa semakin besar tekanan injeksi (injection pressure), flow length yang dihasilkan akan semakin panjang dan suhu mould mempengaruhi flow length yang dihasilkan. Serta agar didapatkan flow length atau mampu alir sesuai dengan tabel reasonable design values for the L/t ratio, maka lebar cetakan produk / specimen yang tepat yaitu untuk runner panjang adalah 7,941 mm dan runner pendek adalah 12,687 mm.    

Esfandiar Jahangard, Alireza Jahangard, Negar Ebrahimi

In recent years, with the increased availability of data and statistics, particularly multi-country input-output tables and firm-level microdata, along with advances in the data processing capacity of personal computers for managing these vast datasets, as well as information and communication infrastructure, the efficient shared use of databases for foreign trade analysis has become possible. The goal of this paper is to implement the gross export decomposition method by Borin and Mancini (2023), using a source-based approach and the perspective of the exporting country, as a foundational analysis for decomposing value-added in the gross exports of Iran’s economic activities. The contribution of this paper to the economic literature on Iran can be summarized in the following three aspects: First, it utilizes data from the 2016 inter-country input-output database, including data on Iran, for empirical documentation. Second, it focuses on the most recent theoretical framework presented by Borin and Mancini (2023), with a source-based approach and country perspective, to decompose the value-added in the exports of Iran’s economic activities. Third, it offers a structural interpretation of the value-added decomposition of Iran's exports for the year 2016, which can be useful for researchers and policymakers in understanding the global value chains of Iran’s economic activities. The results show that Iran plays a small and fragile role in the global economy. Introduction The global economy has become increasingly interconnected, necessitating comprehensive tools to understand complex trade relationships. Traditional trade statistics, which rely on gross export values, often obscure the actual value added by countries. The emergence of inter-country input-output tables allows for a detailed examination of value-added flows within international trade networks. These tables track the production processes across different countries, shedding light on how value is added at each stage of production. This paper builds on the gross export decomposition framework developed by Borin and Mancini (2023), which enables a nuanced analysis of value-added in exports. By applying this framework to Iran's economic activities, we can gain a clearer picture of how different sectors contribute to the country's export economy. This approach provides a more accurate reflection of Iran's role in global value chains (GVCs), moving beyond traditional metrics that may understate or overstate its economic contributions. The study utilizes the Inter-Country Input-Output(ICIO) database for the year 2016 which includes Iran, which provides comprehensive data on trade and production relationships among various countries. The ICIO database is particularly suited for this analysis as it captures the interconnected nature of global trade and production networks. The Borin and Mancini (2023) methodology involves decomposing gross exports into three main components: The domestic value-added (DVA), that is value-added exported in final or intermediate goods. This is part of the Domestic Content – the part of exports that originated in the country – and is also a measure of GDP in gross exports or in intermediates absorbed by direct importers. The foreign value-added (FVA) that is value-added contained in intermediate inputs imported from abroad, exported in the form of final or intermediate goods. This is part of the Foreign Content – the part of gross exports that originated abroad. The returned value-added is domestic VA in intermediates exported. By applying this decomposition method, we can analyze the contribution of various sectors to Iran's export economy. This analysis involves several steps: Data Preparation: Extracting relevant data from the ICIO database for Iran and its trading partners. Decomposition Calculation: Applying the Borin and Mancini (2023) method to decompose Iran's gross exports into DVA, FVA, and RDVA. Sectoral Analysis: Examining the results to identify key sectors contributing to Iran's value-added exports. Results and Discussion The results reveal significant insights into the structure of Iran's export economy. In 2016, Iran's gross exports were composed predominantly of Domestic Value Added (DVA), reflecting the substantial contribution of domestic industries to the country's exports. The analysis shows that the oil and gas sector plays a crucial role in generating DVA, given Iran's abundant natural resources. However, the study also highlights the presence of Foreign Value Added (FVA) in Iran's exports. This indicates that foreign inputs are integrated into Iran's production processes, demonstrating the interconnectedness of Iran's economy with global supply chains. For instance, machinery and equipment imported from other countries are essential for Iran's manufacturing sector, contributing to the FVA in its exports. The Returned Domestic Value-Added component, although smaller, provides interesting insights into the circular nature of some value-added flows. This component illustrates how certain domestic value-added returns to Iran after being processed abroad. For example, raw materials exported from Iran may be processed into intermediate goods in other countries and then re-imported for further manufacturing. The application of the Borin and Mancini (2023) value-added decomposition method provides a detailed and nuanced understanding of Iran's export economy. By distinguishing between Domestic Value Added (DVA), Foreign Value Added (FVA), and Returned Domestic Value Added (REF), this analysis offers a comprehensive view of how different sectors contribute to Iran's gross exports. Conclusion The study reveals that while Iran's export economy is heavily reliant on domestic industries, it is also deeply from oil and mining interconnected with global supply chains. Furthermore, the Returned Domestic Value-Added component highlights the circular nature of some value-added flows, illustrating the complexity of global trade relationships. For policymakers and researchers, these insights are invaluable. Understanding the composition of Iran's export economy can inform strategies to enhance domestic industries' competitiveness and better integrate into global value chains. Additionally, recognizing the role of foreign inputs in domestic production can guide policies aimed at improving the efficiency and resilience of supply chains. In summary, the value-added decomposition method employed in this study offers a robust framework for analyzing Iran's export economy. It provides a clearer picture of how domestic and foreign industries interact within global trade networks, offering valuable insights for enhancing Iran's economic performance in the context of global value chains.

Vadim R. Gasiyarov, Andrey A. Radionov, Boris M. Loginov et al.

Creating digital twins of industrial equipment requires the development of adequate virtual models, and the calculation of their parameters is a complex scientific and practical problem. To configure and digitally commission automated drives, two-mass electromechanical system models are used. A promising area in which to implement such models is the development of digital shadows, namely drive position observers. Connecting virtual models for online data exchange predetermines the tightening of requirements for their parameter calculation accuracy. Therefore, developing accessible techniques for calculating electromechanical system coordinates is an urgent problem. These parameters are most accurately defined by experiments. The contribution of this paper is the proposition of a method for defining the two-mass system model parameters using the oscillograms obtained in the operating and emergency modes. The method is developed for the horizontal stand drives of a plate mill 5000 and is supported by numerical examples. The technique is universal and comprises calculating the rotating mass inertia torques, elastic stiffness and oscillation damping coefficients, and the time constants of the motor air gap torque control loop. The obtained results have been applied to the development of the elastic torque observer of the rolling stand’s electromechanical system. A satisfactory coordinate recovery accuracy has been approved for both open and closed angular gaps in mechanical joints. Recommendations are given for the use of the method in developing process parameter control algorithms based on automated drive position observers. This contributes to the development of the theory and practice of building digital control systems and the implementation of the Industry 4.0 concept in industrial companies.

Kaylla Lage Jordão, Tiago José Menezes Gonçalves, Daniela da Gama e Silva Volpe Moreira de Moraes

Este trabalho propõe um modelo multicriterial de avaliação de desempenho para apoiar o processo de gestão em instituições públicas. O modelo foi aplicado ao setor de obras, manutenção e serviços de uma instituição federal. A Metodologia Multicritério de Apoio à Decisão - Construtivista (MCDA-C) foi o instrumento de intervenção adotado para a obtenção e análise dos dados por ser capaz de tratar diferentes tipos de informações e por elicitar os objetivos de decisão conforme os valores dos decisores, possibilitando que eles reflitam sobre suas prioridades e preferências. Como resultados, o modelo possibilitou (i) a medição do desempenho do setor a partir dos aspectos considerados relevantes pelo decisor; (ii) a análise da evolução no desempenho de cada indicador e de suas tendências; (iii) o entendimento dos principais problemas de desempenho do setor e dos pontos passíveis de melhoria; (iv) e a realização de recomendações para resolver os problemas de desempenho identificados.

Xiaobo Shen, Boqiang Lin

Abstract The Chinese strategic plan “Made in China (2025)” aims at improving the independent innovation capacity of the manufacturing industry. It also commits to constructing green manufacturing systems through cleaner production and green technologies. The Chinese government provides research and development (R&D) preferential policies to promote the realization of these aims. Using the two-stage least square (2SLS) method, this paper tests the impact of R&D capital and its spillover on the energy intensity of the Chinese manufacturing industry. It also evaluates the effectiveness of R&D policy in promoting R&D investment at the industry level. The findings show that R&D capital is positively linked to the reduction of energy intensity, and heterogeneity exists in the relationship between energy intensity and R&D capital across industries. This study finds that in general, R&D subsidies and tax relief do not significantly promote R&D investment, but R&D subsidies generate a significantly positive effect on the R&D intensity in the high-tech industries.

F. Fang

This article presents the three paradigms of manufacturing advancement: Manufacturing I, craft-based manufacturing by hand, as in the Stone, Bronze, and Iron Ages, in which manufacturing precision was at the millimeter scale; Manufacturing II, precision-controllable manufacturing using machinery whereby the scales of material removal, migration, and addition were reduced from millimeters to micrometers and even nanometers; and Manufacturing III, manufacturing objectives and processes are directly focused on atoms, spanning the macro through the micro- to the nanoscale, whereby manufacturing is based on removal, migration, and addition at the atomic scale, namely, atomic and close-to-atomic scale manufacturing (ACSM). A typical characteristic of ACSM is that energy directly impacts the atom to be removed, migrated, and added. ACSM, as the next generation of manufacturing technology, will be employed to build atomic-scale features for required functions and performance with the capacity of mass production. It will be the leading development trend in manufacturing technology and will play a significant role in the manufacture of high-end components and future products.

E. Järvenpää, N. Siltala, Otto Hylli et al.

Today’s highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model’s content and structure.

Vichathorn Piyathanavong, J. Garza‐Reyes, Prof Vikas Kumar et al.

Abstract Evidence suggests that manufacturing companies have tried to address the current environmental challenges derived from their operations by implementing various operational environmental sustainability approaches, including green manufacturing (GM), cleaner production (CP), green lean (GL), green supply chain management (GSCM), reverse logistics (RLs) and circular economy (CE). However, although their adoption is well documented in developed nations and few other countries, very little has been done to understand such phenomenon in a rapid developing country such as Thailand. This paper aims at filling this gap by providing light into some fundamental issues regarding the implementation of these approaches in the manufacturing sector of Thailand. A survey-based exploratory research was carried out based on 287 Thai manufacturing companies. The data was analysed using a combination of descriptive and inferential statics. The study revealed that a large amount of investment capacity, and proper training & knowledge are needed to fully implement the studied operational approaches. This resulted in some of the weakest elements of Thai manufacturing firms and hence the main barriers to their implementation. The study also showed that Thai manufacturing firms consider the impact on the environment and benefits from adopting these operational approaches as company's policy and own initiative, environmental awareness, and cost saving from conservation of energy as the main reasons for adopting the studied operational approaches. Finally, the findings also indicate that Thai manufacturing firms tend to implement them because of internal factors and that they lack of motivation from external factors and involvement from other stakeholders. The paper extends the current limited knowledge on the deployment of operational environmental sustainability approaches in Asia, and its results can be beneficial for organisations that aim at effectively adopting them to improve their operation's sustainability.

D. Strong, M. Kay, B. Conner et al.

Abstract The ever-growing applications of Additive Manufacturing (AM) in the production of low volume- high value metal parts can be attributed to improving AM processing capabilities and complex design freedom. However, secondary post-processing using traditional processes such as machining, grinding, heat treatment and hot isostatic pressing, i.e., Hybrid Manufacturing, is required to achieve Geometric Dimensioning and Tolerancing (GD&T), surface finish and desired mechanical properties. It is often challenging for most traditional manufacturers to participate in the rapidly evolving supply chain of direct digital manufacturing (DDM) through in-house investments in cost prohibitive metal AM. This research investigates a system of strategically-located AM hubs which can integrate hybrid-AM with the capabilities and excess capacity in multiple traditional manufacturing facilities. Using North American Industry Classification System (NAICS) data for machine shops in the U.S., an uncapacitated facility location model is used to determine the optimal locations for AM hub centers based on: (1) geographical data, (2) demand and (3) cost of hybrid-AM processing. Results from this study have identified: (a) candidate US counties to build AM hubs, (b) total cost (fixed, operational and transportation) and (c) capacity utilization of the AM hubs. It was found that uncapacitated facility location models identified demand centroid as the optimal location and was affected only by AM utilization rate whereas a constrained p-median model identified 22 AM hub locations as the initial sites for AM hubs which grows to 44 AM hubs as demand increases. It was also found that transportation cost was not a significant factor in the hybrid-AM supply chain. Findings from this study will help both AM companies and traditional manufacturers to determine location in the U.S and key factors to advance the metal hybrid-AM supply chain.

Zhaojun Xu, Zhong Zheng, Xiaoqiang Gao

Heidi Taboada, Yasser A. Davizón, José F. Espíritu et al.

Dynamic supply chains (SC) are important to reduce inventory, enable the flow of materials, maximize profits, and minimize costs. This research work presents a capacity–inventory management model via system dynamics for a dynamic supply SC, applying model-based optimal control techniques. In the context of high-volume manufacturing (HVM) that present low variability and predictable demand, for mathematical modeling purposes, a set of coupled first-order ordinary differential equations, with an analogy from the mixing problem, is presented, which relates capacity and inventory levels, taking into account a production rate at each node of interaction. The application of ordinary differential equations via the mixing problem (or compartmental analysis) is important based on the idea of a balance between the influx and outflux of raw material along the supply chain. A proper literature review on optimal control for supply chains is analyzed. The mathematical model introduced is presented in a linear time-invariant (LTI) state-space formulation. Stability analysis for the dynamic serial SC is presented, and a sensitivity analysis is also conducted for the capacity and production rate parameters considering the effects of variations in parameters along the SC. An energy-based optimal control is also developed with proper simulations.

Michael Gomez, Tony Schmitz

This paper describes the dynamic stability evaluation of a constrained-motion dynamometer (CMD) with passive damping. The CMD’s flexure-based design offers an alternative to traditional piezoelectric cutting force dynamometers, which can exhibit adverse effects of the complex structural dynamics on the measurement accuracy. In contrast, the CMD system’s structural dynamics are nominally single degree of freedom and are conveniently altered by material selection, flexure element geometry, and element arrangement. In this research, a passive damping approach is applied to increase the viscous damping ratio and, subsequently, the stability limit. Cutting tests were completed and the in situ CMD displacement and velocity signals were sampled at the spindle rotating frequency. The periodic sampling approach was used to determine if the milling response was synchronous with the spindle rotation (stable) or not (chatter) by constructing Poincaré maps for both experiment and prediction (time-domain simulation). It was found that the viscous damping coefficient was increased by 130% and the critical stability limit was increased from 4.3 mm (no damping) to 15.4 mm (with damping).