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Purpose: An open issue is the decentralization of standards to each process owner. Centralized standards are a common situation which puts a big amount of chore tasks against continual improvement. The Pipes and Puddles framework was born to solve this problem. Design/methodology/approach: Pipes and Puddles research has been conducted through goal-oriented business requirements engineering, and the model melts the Lean philosophy pillars, the PDCA Deming's cycle for change management, and shapes the three pipes underlying each business process by risk analysis. Findings: Putting a clear difference among reactive and preventive problem-solving is the first observable change when adopting this framework, which drives to keep cost of poor quality permanently close to zero. Another important consequence of adopting this framework is that each process owner is responsible for continually improving their processes. Research limitations/implications: This research is still limited to a few empirical cases of use; the diffusion of its benefits and guidelines to its real implementation is an opportunity for a massive adoption. Practical implications: Pipes and Puddles framework brings to management a roadmap to foster the role of process owner, providing a decentralized approach towards risk management, to react to nonconformities and to implement effective channels for continual improvement. Social implications: The virtual elimination of cost of poor quality and the empowerment of all employees by having specific roles and regular channels to effectively act as problem-solvers, hence, as agents of continual improvement. Originality/value: The vision of each process in the firm as the interconnection of three pipes (sub-processes) is a novelty that completely solves common issues for all processes that are transversal to all others (risk management, non-conformant products, customer claims, change management, etc.). [ABSTRACT FROM AUTHOR]

This article provides an overview of the arguments and counterarguments involved in identifying the state of an enterprise, assessing its functional efficiency, and optimizing its target functions while implementing management tasks. The decentralization of enterprises empowers them to adapt to specific territorial needs. However, it also introduces an element of instability that can be disconcerting. The primary objective of this research is to develop a scientific and methodological approach to modelling the management system of the development of manufacturing enterprises under decentralization. The systematization of literary sources and approaches to the problem of managing the development of manufacturing enterprises reveals that each level of analysis of the state of the enterprise and assessment of its development dynamics and problem situation corresponds to the aggregate set of assessments, which includes both individual indicators and their various groups. At its core lies the crucial recognition of production systems' (PS) state as a pivotal parameter influencing the very fabric of UTC development. The model employs a powerful vector functional approach, allowing for a multi-dimensional assessment of an enterprise's state. The model translates into a tangible metric, empowering informed decision-making and targeted interventions. The relevance of this scientific problem is that the management process of enterprise development under decentralization needs to be more structured and less accidental. There is a need to manage this process to survive and develop in an aggressive market environment. Optimal management of production systems as discrete matrix objects provides an opportunity to consider the functioning of each of them depending on the state of the others at time t of their production to time t+1, which is associated with their further development in a decentralized environment. The results of the research can be helpful for the development of systems for monitoring and strategic management of the development processes of manufacturing enterprises under decentralization. [ABSTRACT FROM AUTHOR]

Industry 4.0 involves major changes in manufacturing process management. Both the Internet of Things and cloud computing allow online interactions between third parties, such as providers, customers and suppliers, with the traceability system of a factory. Several blockchain-based approaches have been proposed to increase confidence in traceability data and reinforce trust. However, the transparency brought may be at the cost of risks to factory's confidential data exposure. This paper investigates the way these critical data, which are necessary to post-assembly audit, could be included into traceability data, and validated through the related transactions by the third parties, without compromising their confidentiality. Accordingly, this proposal includes the description of a blockchain-based traceability system and its implementation using the Multichain platform. In addition to its confidentiality-preserving feature, we discuss the way energy consumption and storage volume induced could be managed so as to favor its effective adoption by manufacturing factories. [ABSTRACT FROM AUTHOR]

The article examines the possibilities of evaluating the management of warehousing processes of a manufacturing company using the WERC efficiency model. It has been established that assessment models based on quantitatively determined indirect indicators, which can be calculated and interpreted in different ways, are mostly used in scientific works and practice. The assessment model used in the study includes more direct indicators, which by comparing them with the reference values of the best practice allow to better identify problems in the management of warehousing processes and monitor changes in this activity. [ABSTRACT FROM AUTHOR]

The existing mathematical analysis approaches for the management of product manufacturing process cannot satisfy the global optimization and high feasibility required for product manufacturing. Neither do they reflect the actual production situation accurately and comprehensively. Therefore, this paper explores the simulation and modelling of management decisions in multi-variety small-batch product manufacturing process in discrete production environment. Firstly, the management knowledge in product manufacturing process was expressed mathematically, and the product manufacturing system was modelled in discrete production environment. Then, the flow of the interactive simulation model was explained, along with the realization steps of the model. Taking a real multi-variety small-batch production unit as the engineering background, an empirical analysis was carried out to detail the interactive simulation of the line change management for the production lines in the management system of the product manufacturing process, and to build a simulation model for the product manufacturing system. The simulation model was proved effective through simulations. (Received in April 2022, accepted in July 2022. This paper was with the authors 1 month for 2 revisions.) [ABSTRACT FROM AUTHOR]

Industrial manufacturing management can benefit from the use of modeling. For a correct representation of the manufacturing process and the subsequent management, the models must incorporate the effect of the uncertainty propagation throughout the stages considered. In this paper, the proposed methodology for uncertainty management uses a nonintrusive method that is based on building a deterministic physics-informed real-time model for the a posteriori computation of output uncertainties. This model is built using tensor factorization as the Model Order Reduction technique. It includes as model parameters: material properties, process operations, and those random and epistemic uncertainties of known variables. The resulting model is used off-line to identify sensitivities and therefore to unify uncertainty management across the material transformation process. This method is presented by its direct application to an automotive door seal manufactured by continuous co-extrusion of several rubbers and reinforcement (metal strip and glass fiber thread). [ABSTRACT FROM AUTHOR]

Process industries operate in commodity markets with low product variety, little product differentiation, and highly capital-intensive manufacturing. Because prices are volatile and demand is uncertain, manufacturing capabilities become the main strategic competitive lever. The purpose of this paper is to evaluate the interplay between market conditions, capital expenditure, manufacturing flexibility, and production capacity to effectively analyze the consequences of manufacturing investments in both the short and the long terms. In order to achieve this, we developed an econometric model linking market conditions and operational decisions. We tested the model on a sample of 480 firms from the mining sector and 1053 firms from the oil and gas sector. The results show that firms in process industries follow the market with their investments, which impacts their operations in the short term as well as their capacity growth in the long term. Additionally, from our findings, we propose a firm value driver model that managers could consider when deciding on capital expenditures. Finally, our results indicate that the managers of financially healthy companies should resist the stock market's short-term pressure to reduce fixed costs and instead play the long game. [ABSTRACT FROM AUTHOR]

In view of the production quality management of filter rods in the manufacturing and execution process of cigarette enterprises, this paper analyzes the necessity of implementing the manufacturing execution system (MES) in the production process of filter rods. In this paper, the filter rod quality system of cigarette enterprise based on MES is fully studied, and the constructive information management system demand analysis, cigarette quality control process, system function module design, implementation and test effect are given. This paper utilizes the Fuzzy analytic hierarchy process to find the optimal system for processing the manufacturing of cigarette. The implementation of MSE based filter rod quality information management system for a cigarette enterprise ensures the quality control in the cigarette production process. Through visualization, real-time and dynamic way, the information management of cigarette production is completed, which greatly improves the quality of cigarette enterprise manufacturing process. [ABSTRACT FROM AUTHOR]

The integrity of the cellulosic insulation in power transformers is considered one of the most relevant parameters that affects their performance and reliability. Electric faults, such as short circuits, have thermal and mechanical effects that degrade the paper and can eventually produce the end-of-life of the transformer. The evolution of the properties of the paper insulation of a commercial continuously transposed conductor due to thermal ageing was characterised through the degree of polymerisation and tensile testing. Failure initiation and propagation in the paper was analysed macroscopically and microscopically using scanning electron microscope. A finite element numerical mechanical model of the conductor was implemented to reproduce the experiments and to obtain the load level and strain state that produce failure at each ageing state, aiming at developing a failure model for the insulation. This model may contribute to an improvement in manufacturing processes and management of the electrical system. [ABSTRACT FROM AUTHOR]

With the progress of German Industry 4.0 and China's Manufacturing 2025 era, the construction of industrial Internet has been closely integrated with the digitization and intelligent management of manufacturing process, industrial intelligentization has become the theme of manufacturing industry. As a practice base of engineering education, the engineering training center had been also upgrading to digitization and intelligent transformation. This paper aimed at the digital construction, intelligent management and service of engineering training center, a frame work of intelligent engineering training center was constructed based on engineering training practice teaching platform, and the construction of industrial internet system and intelligent management and service platform of engineering training center were explored. The construction of intelligent engineering training center focuses on the two platforms of modern engineering education and innovation and entrepreneurship education, and the teaching system of modern engineering education has been gradually supplemented and improved with the innovation and entrepreneurship practice in the engineering background. Finally, it can provide intelligent services for engineering education, innovation and entrepreneurship education through the construction of intelligent engineering training center. [ABSTRACT FROM AUTHOR]

Since the sixties, the industry has begun an accelerated process of automation, which has led to the need for analysis, definition, modeling and management of the workflows for the manufacturing processes. A flexible manufacturing system (FMS) is an automatized system which includes automatic equipments for quick and correct operations, handling of products and materials and who includes computerized control for activities. Due to the complexity of these systems, the necesity to verify their correctness theoretically by mathematical methods before being implemented in practiceis obvious.A workflow net (WF net) is a mathematical model, based on Petri nets, for workflow modeling. This paper wants to emphasize the advantages of using WF nets for describing FMSsand to show how this can be realized. [ABSTRACT FROM AUTHOR]

In an advanced manufacturing environment, the analysis of profile data collected from the process equipment is a critical issue in improving process efficiency. In particular, multi-profile monitoring is essential for process control because an advanced manufacturing process consists of numerous pieces of equipment and their related sensors. The main goal of this study is to build a monitoring chart using a Profile Integrated Measure (PIM) from multi-profile data in order to observe an overall condition of various points in the process. To deploy the proposed algorithm, multi-profile data needed to be preprocessed and applied to the prediction model. The PIM is calculated from the prediction model and reflects the relationships between the multi-profile data property, which has normal/abnormal states. The proposed algorithm constructs a model using the PIM of a normal state and identifies the performance of the model. Experiments with the simulation datasets modified from the manufacturing process validate the effectiveness and applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fusion framework is an extension to the regularized particle filtering technique, used to facilitate updating the state prediction with a numerical inference model, when measurement models alone are not satisfactory. The results show significant improvement in tool wear state estimation, reducing the prediction errors by almost half, compared to the prediction model and sensor-based monitoring method used independently. [ABSTRACT FROM AUTHOR]

Consumption of ultrapure water (UPW) during semiconductor manufacturing processes is a very important topic. A new water-dispensing arm design uses a small volume of UPW to remove slurry residue and defects inside pad grooves. This innovative water conditioning arm, in contrast to the conventional atomizer and diamond disc, not only reduces UPW consumption by 87% but also reduces scratch defects caused by diamond abrasive particles. The small water volume arm conditioning system injects UPW with diameters at micro levels. Experimental results show that oxide film removal amount under continuous polishing conditions is maintained at 716 A even after 20 runs. The removal amount and profile not only is maintained but improved by over 2% in comparison with the conventional atomizer under no-dressing conditions. Finally, using a water pump to increase the water pressure up to 50 km/cm2 and treating for 5 s resulted in only 0.5-mm dishing for the IC1010 polish pad. [ABSTRACT FROM AUTHOR]

Abstract The present work presents an in-depth evaluation of continuously collected data during a twin-screw granulation and drying process performed on a continuous manufacturing line. During operation, the continuous line logs 49 univariate process variables, hence generating a large amount of data. Three identical 5-h continuous manufacturing runs were performed. Multivariate data analysis tools, more specifically latent variable modeling tools such as principal component analysis, were used to extract information from the generated data sets unveiling process trends and drifts. Furthermore, a statistical process monitoring strategy is presented. The approach is based on the application of multivariate statistical process monitoring to model the variables that remain around a steady state. [ABSTRACT FROM AUTHOR]

Modeling and simulation (M&S) is a promising solution for manufacturers to verify and resolve errors in advance. Recently, M&S using high-performance computing has attracted attention as a technology trend for its efficiency in terms of the time and the cost. However, a huge amount of computing resources is still required to perform an M&S analysis workflow within a short time. For the resolution of this issue, we organize a heterogeneous cluster environment with individual nodes of the diverse performance and OS. An open source-based task scheduler named Son of Grid Engine is used for managing the heterogeneous resources for the experiment. To verify the efficiency and validity of the organization scheme, we have developed an environment for analyzing products and monitoring work through CalculiX, an open source-based finite element analysis solver. [ABSTRACT FROM AUTHOR]

Highly accelerated life test (HALT) is a test methodology to evaluate reliability of mechanical and electromechanical devices. HALT is often used on devices that must be guaranteed for high reliability over a long time span. HALT simulates the life cycle of the device, usually until it experiments a failure. HALT tests are used to assess reliability of devices at the end of the production cycle, but are also used to improve the design and manufacturing process, allowing to find and correct potential problems when changes to the production process are less costly. HALT tests are usually difficult and time consuming, and there is a strong need for their automation. This paper proposes a methodology to design software and hardware for HALT automated tests. The goals pursued are to standardize the test process, to reduce the need for manual commands at the minimum and to simplify the data gathering process. The methodology proposed starts from domain requirement analysis and is conceived to be as general as possible, with the goal to make it easily extensible and adaptable to multiple testing domains. Finally, the paper reports on a case study describing a HALT test device designed according to the proposed methodology and currently in use to test electromechanical actuators. [ABSTRACT FROM AUTHOR]

The failure mode and effects analysis (FMEA) is a mode that adopts systematic methods to determine the possible problems in products or processes, and analysis of the impacts of these problems on products or processes can facilitate the adoption of improvement and prevention measures. Nevertheless, FMEA focuses on factor problems, and can only solve problems according to the importance of the factor. If several failure mode factors take effect or interact simultaneously, it will be difficult to evaluate the analysis. Taking the examples of Chinese photovoltaic cell manufacturing companies, this study integrated three research methods and proposed a new FMEA-IPA-DEMATEL analysis model. First, FMEA was applied to identify the factors to be improved. Second, IPA was employed to discuss the improvement of the importance and performance of the factors. Third, DEMATEL was used to explore the causal relation and interaction among the factors. Finally, this study helped decision-makers identify the core problems to be improved and gave suggestions on the problem-solving sequence. The FMEA-IPA-DEMATEL model combined the strengths of the three methods, and uses the minimum resources to prioritize the projects to be improved, in order to enhance management performance. [ABSTRACT FROM AUTHOR]

From product maintenance and service point of view, high-value sophisticated computer numerical control (CNC) machine tools in modern manufacturing factories play important roles: they are manufacturing equipment, and on the other hand, they are also products supplied by equipment manufacturers. There is a trend that manufacturers are extending their responsibilities to the products use phase to meet customers' requirements for lifetime support and service. To ensure the effective performance and efficient maintenance of high-value machine tools, information and knowledge from their lifecycle should be collected and reused. However, in the research area of product service systems and related computerised maintenance systems, there is a lack of research work on how to integrate knowledge from different stakeholders into the maintenance and service planning process, which is important for modern digital manufacturing systems to reduce machine tools' downtime and improve their working performance. This project proposed a collaborative maintenance planning framework to connect different stakeholders and integrate their knowledge into the maintenance and service process. The potential of advanced content management systems (CMSs), which are widely used non-engineering sectors such as finance, business, publishing and government organisations, has been explored and tested for applications in the manufacturing engineering domain. The research realised that CMSs have several advantages compared with traditional engineering information systems, especially in managing dynamic and unstructured knowledge. A prototype maintenance and service planning system has been developed and evaluated using a real CNC machine tool. [ABSTRACT FROM AUTHOR]

In modern process management, the life cycle concept gains more and more importance. It focusses on the total costs of the process from invest to operation and finally retirement. Also for analytical procedures an increasing interest for this concept exists in the recent years. The life cycle of an analytical method consists of design, development, validation (including instrumental qualification, continuous method performance verification and method transfer) and finally retirement of the method. It appears, that also regulatory bodies have increased their awareness on life cycle management for analytical methods. Thus, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), as well as the United States Pharmacopeial Forum discuss the enrollment of new guidelines that include life cycle management of analytical methods. The US Pharmacopeia (USP) Validation and Verification expert panel already proposed a new General Chapter 〈1220〉 “The Analytical Procedure Lifecycle” for integration into USP. Furthermore, also in the non-regulated environment a growing interest on life cycle management is seen. Quality-by-design based method development results in increased method robustness. Thereby a decreased effort is needed for method performance verification, and post-approval changes as well as minimized risk of method related out-of-specification results. This strongly contributes to reduced costs of the method during its life cycle. [ABSTRACT FROM AUTHOR]

Methods for the organization of safe manufacturing processes and for the control of technological risk in Russia and abroad are considered. The structure, problems, and types of estimation (levels) of risk and the advantages of the method of Risk Based Inspection (RBI), a method for optimizing programs of inspection and maintenance, are set forth. [ABSTRACT FROM AUTHOR]

Biomass has long been considered one of the most promising feedstock as an alternative primary source to substitute traditional fuels in the transport sectors. However, both biomass intrinsic variability and the fact that several conversion technologies have not reached full maturity make the economic assessment of the production system performance rather difficult. This paper proposes a quantitative approach for the strategic design and optimisation of biomass-based supply chains under uncertainty on technology conversion efficiency. The methodology is based on regret theory and allows quantifying both risk and regret with respect to benchmark economic outputs. A Mixed Integer Linear Programming is employed to represent and optimise the profitability of a multi-echelon, multi-period and spatially explicit biomass-based supply chain for bioethanol and bioelectricity production where several conversion technologies are simultaneously taken into account. The modelling framework includes biomass cultivation, transport, conversion, distribution and final usage in alternative fuel vehicles (running either on bioethanol or bioelectricity). Results demonstrate how the methodology can help policy-makers and investors assessing technological options according to their risk aversion attitude. [ABSTRACT FROM AUTHOR]

The protein A capture step is the main cost-driver in downstream processing, with high attrition costs especially when using protein A resin not until end of resin lifetime. Here we describe a feasibility study, transferring a batch downstream process to a hybrid process, aimed at replacing batch protein A capture chromatography with a continuous capture step, while leaving the polishing steps unchanged to minimize required process adaptations compared to a batch process. 35 g of antibody were purified using the hybrid approach, resulting in comparable product quality and step yield compared to the batch process. Productivity for the protein A step could be increased up to 420%, reducing buffer amounts by 30–40% and showing robustness for at least 48 h continuous run time. Additionally, to enable its potential application in a clinical trial manufacturing environment cost of goods were compared for the protein A step between hybrid process and batch process, showing a 300% cost reduction, depending on processed volumes and batch cycles. [ABSTRACT FROM AUTHOR]

In this study, a low-cost infrared sensing system based on the analysis of the surface temperature distribution is proposed for monitoring the perturbations occurring during the aluminum alloy metal inert gas (MIG) welding process. A galvanometer scanner is employed in this real-time infrared sensing system to continually reflect the infrared energy to the point infrared sensor. By controlling the scanning mirror of the galvanometer scanner rotating in a high speed, the infrared energy at different points of the welding seam and the heat-affected zone on the surface of the plate will be continually captured by the point infrared sensor. Different conditions (changes in the welding speed, welding current, and joint gap width) of the welding process have been simulated to perturb the welding process. Three representative geometric defects such as undercut, humping, and lack of fusion were produced to validate our infrared sensing system. Experimental results showed that the sensing system is useful for monitoring perturbations that arise during the welding process and identifying welding defects. [ABSTRACT FROM AUTHOR]

A systematic study about the intrinsic resistance of 29 strains (26 autochthonous and 3 commercial ones), belonging to Leuconostoc genus, against diverse stress factors (thermal, acidic, alkaline, osmotic and oxidative) commonly present at industrial or conservation processes were evaluated. Exhaustive result processing was made by applying one-way ANOVA, Student's test ( t ), multivariate analysis by Principal Component Analysis (PCA) and Matrix Hierarchical Cluster Analysis. In addition, heat adaptation on 4 strains carefully selected based on previous data analysis was assayed. The strains revealed wide diversity of resistance to stress factors and, in general, a clear relationship between resistance and Leuconostoc species was established. In this sense, the highest resistance was shown by Leuconostoc lactis followed by Leuconostoc mesenteroides strains, while Leuconostoc pseudomesenteroides and Leuconostoc citreum strains revealed the lowest resistance to the stress factors applied. Heat adaptation improved thermal cell survival and resulted in a cross–resistance against the acidic factor. However, all adapted cells showed diminished their oxidative resistance. According to our knowledge, this is the first study regarding response of Leuconostoc strains against technological stress factors and could establish the basis for the selection of “more robust” strains and propose the possibility of improving their performance during industrial processes. [ABSTRACT FROM AUTHOR]

The Upper Cretaceous Himenoura Group in the Amakusa- Kamishima Island area, southwest Japan is subdivided into the Hinoshima and Amura Formations. In order to determine the numerical depositional age of the formations, zircon U- Pb ages were investigated using laser ablation inductively coupled plasma mass spectrometry ( LA- ICP- MS) for acidic tuff samples from the lower part of the Hinoshima Formation and the upper part of the Amura Formation. Although the two samples contain some accidental zircons, the samples have a definite youngest age cluster and their weighted mean ages are 85.4 ± 1.3 and 81.5 ± 1.1 Ma, respectively (errors are 95 % confidence interval). These age data indicate that the Himenoura Group in the Amakusa- Kamishima Island area was deposited mainly in the early Santonian to early Campanian which is consistent with biostratigraphic ages. Additionally, zircon age distributions of the two tuff samples from the upper part of the Hinoshima Formation do not show a distinct youngest peak of eruption age but characteristics of detrital zircons suggestive of maximum depositional age of the host sediments. These results demonstrate that the mean age of the youngest zircon age cluster of a tuff sample does not always indicate depositional age of the tuff, and statistical evaluation of age data is effective to determine depositional age of a tuff bed using zircon U- Pb ages. [ABSTRACT FROM AUTHOR]

Over the last decade, Wireless Sensor Networks (WSN) went from being a promising technology to the main enabler of countless Internet of Things applications in all types of areas. In industry, WSNs are now used for monitoring and controlling industrial processes, with the benefits of low installation costs, self-organization, self-configuration, and added functionality. Nevertheless, despite the fact that base WSN technologies are quite stable and subject to standardization, they have kept one of their main characteristics: fault-proneness. As a result, in recent years considerable effort has been made in order to provide mechanisms that increase the availability, reliability and maintainability of this type of networks. In this context, a whole range of techniques such as fault detection, fault identification and fault diagnosis used in other research fields are now being applied to WSNs. Unfortunately, this has not led to a consistent, comprehensive WSN fault taxonomy that can be used to characterize and/or classify faults. Neglecting the importance of WSN fault characterization (e.g., when using supervised algorithms for anomaly detection) may lead to bad classifiers and, consequently, bad fault handling procedures and/or tools. In this paper, we start by reviewing base fault management concepts and techniques that can be applied to WSNs. We then proceed to propose and present a comprehensive WSN fault taxonomy that can be used not only in general purpose WSNs but also in Industrial WSNs. Finally, the proposed taxonomy is validated by applying it to an extensive set of faults described in the literature. [ABSTRACT FROM AUTHOR]

Process planning is one of the most difficult tasks in product development caused by the large number of technical, technological, economic, environmental and other criteria. Accordingly, the selection of manufacturing processes is a complex multi-criteria decision making problem since it considers a number of possible alternative manufacturing processes in addition to a large number of specified criteria. This paper represents the computer-aided methodology for the multi-criteria evaluation and selection of manufacturing processes at the stage of conceptual process planning. The developed methodology is primarily focused on the mapping of product design and manufacturing requirements. Manufacturing processes that fail to meet the given conditions on the basis of 10 criteria such as materials, production volume, productivity, dimensional accuracy, surface finish, etc., are eliminated according to the developed rules. Then, the multi-criteria evaluation and ranking of manufacturing processes is performed based on 5 criteria: manufacturing cycle time, process flexibility, material utilization, quality and operating costs. Based on this methodology, a system is developed for the multi-criteria selection of manufacturing processes, whose implementation is presented in the case of the hip joint endoprosthesis. [ABSTRACT FROM AUTHOR]

Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating. [ABSTRACT FROM AUTHOR]

The authors discuss the control and measure used in molecular management as a key to the sustainocene, an era of global sustainability. Topics mentioned include the demand for industrial processes to use environmental monitoring technologies, the advancement in sensor and analysis technology, and the Process Analytical Technology (PAT) approved by the U.S. Food and Drug Administration (FDA).

An examination was made of the free thermoforming of products and a method for monitoring this process. The advantages of the proposed monitoring method are considered. [ABSTRACT FROM AUTHOR]

Three-level versions of Multilevel Simultaneous Component Analysis (MLSCA) and Multilevel Partial Least Squares (MLPLS) were developed, which are capable of separating between-plant, between-run and within-run process variation, and modeling these three levels in a multivariate way. In comparison to the two-level versions they allow to discriminate between overall differences between plants and the variation between runs within a plant. It was shown that the three-level version of MLSCA has clear added value for the analysis of process runs from different plants. In MLPLS other projections of the multivariate data onto latent variables and different views of the data are obtained when relevant Y information is available. This has clear added value for obtaining insight into the relation between process data and Y. A special use of MLPLS is to diagnose aberrations in first principles models. In batch process monitoring MLSCA at three levels allows simultaneous multivariate modelling of batch data from different manufacturing plants. By filtering out the between-plant and between-run sources of variation, and using only within-run variation, monitoring models can be improved. Using within-run data, it is possible to build monitoring models across manufacturing units and reduce the number of nuisance alarms, while improving abnormal situation detection and diagnosis. Model transfer is only possible if static between-plant differences exist, but not if there are dynamic differences. [ABSTRACT FROM AUTHOR]

Femtosecond laser micro-drilling has progressed remarkably in recent years to become an essential tool for micro-hole drilling in many applications where the extreme high accuracy machining is required. This paper addresses the problem of finding optimal inspection policy in a femtosecond laser micro-machining process for micro-hole array products to improve successful rate of machined parts and hence to minimise the unit manufacturing cost. A solution algorithm is proposed based on particle swarm optimisation (PSO), and the simulation is used to provide better insight into the optimal solution. The numerical experiments and verification experiments which are conducted with different schemes illustrate the applicability and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Capacity planning helps to synchronize demands and production volumes, and assists in early preparedness for dealing with future production imbalances. A tool room of armored manufacturing organization was selected for this work study. Initially, a master production schedule and rough cut capacity plans are prepared based on the delivery commitments of the organization. Time and motion study of the tool room for 28 weeks was performed to determine its current production capacity. It was observed that imbalance between demands and production exists. An iterative system improvement strategy was proposed for exploring the potential of tool room. Lean concepts and methodologies were the essence of the proposed system improvement strategy. It was observed that WIP had been reduced by 18 %, production volumes had improved by 24 % and tardiness reduced by 28 %. This strengthened our belief that lean philosophy is equally applicable within the capacity planning domain. The capacity planning approach presented in this work study could also be generalized for other domains. This approach believes in improving the potential through elimination of wastes, process improvement, defect prevention and total preventative maintenance. This approach improves the system optimally by utilizing existing resources available on the shop floor. This research will be further expanded towards the major production units of this organization. We are currently working on capacity planning for rebuilding the activities of T-80 UD battlefield tanks. [ABSTRACT FROM AUTHOR]

The evaluation of mechanical properties of hazelnuts has been developed over the past years mainly to optimize industrial processes. The aim of this study is to reproduce the compressive behavior of hazelnut kernel obtained by experimental and numerical activities; the contribution of pellicle influence to the mechanical behavior is also analyzed. The experimental activity is aimed to measure the mechanical properties of hazelnut kernel and to obtain a model calibration based on experimental data analyzed by statistical approach. The finite element models of hazelnut kernels are implemented and a set of numerical compression tests are simulated; the comparison of experimental and numerical responses is shown. [ABSTRACT FROM AUTHOR]

A number of environmental forces such as increasing value chain network complexity, decreasing product life-cycle cost, and time-to-market requirements or increasing product complexity act upon manufacturing organizations, enhancing the acute need for organizational routines that foster efficient and effective communication between processes. Such organizational routines erode quickly in the absence of common standards for knowledge sharing, that is why successful manufacturing systems benefit from interprocess standardization. The purpose of this paper is to offer a standardization model of interprocess communication that increases manufacturing operational performance (MOP). First, we propose a novel holistic model that makes standardized interprocess communication possible in manufacturing organizations. Second, we propose a model for quantifying the implications of standardizing interprocess communication upon MOP. Finally, as a matter of application, we show the results of its successful implementation in one Japanese manufacturing organization. [ABSTRACT FROM PUBLISHER]