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There is no denying smart manufacturing is a critical step in responding to a new round of energy crises and promoting the high-quality development of the manufacturing industry, among which, the construction of intelligent production lines is the key link. In the practical production of complex aerospace components, production scheduling optimization plays an important role in achieving cost savings and energy reduction for a range of existing problems, such as cumbersome process design, difficult real-time scheduling adjustment, inefficient quality data testing, and complex interrelationships of state-type data. In this work, the optimization of scheduling objective, the selection of scheduling method, and the construction of scheduling management control system are the pointcuts to review the recent development of production scheduling optimization, systematically. The research on the more practical implications of multi-objective production scheduling optimization has shown that efficiency and energy consumption are the primary priorities of scheduling objectives. Scheduling rules and heuristic algorithms are the crucial research methods. Intelligent information technologies are an effective means to decrease the complexity of scheduling. Meanwhile, building intelligent management control systems for production scheduling is of great significance for the transformation and upgrading of production from digitalization to intelligence. [ABSTRACT FROM AUTHOR]

Tool wear will lead to the reduction of surface quality and machining accuracy. Therefore, tool condition monitoring is vital to the improvement of industrial production efficiency and quality. In this paper, first of all, the internal mechanism of the milling process and the performance characteristics of the milling signals were analyzed. It is found that there are different trends between the large and small fluctuations of milling signals in the process of tool wear increasing. This property can be characterized by various parameters of multifractal spectrum to establish the relationship between tool wear and multifractal parameters. By analyzing the changes of multifractal spectrum parameters, the tool wear monitoring can be realized. Then, the multifractal detrended fluctuation analysis (MFDFA) method is used to calculate the mean square error, generalized Hurst exponent, and multifractal spectrum parameters, which are the eigenvectors, and establish its relationship with tool wear. Finally, the tool condition diagnosis is conducted by a support vector machine (SVM). The results show that the tool condition monitoring method of MFDFA combined with SVM is proved to be effective and the multifractal parameters of MFDFA are very sensitive to tool wear. [ABSTRACT FROM AUTHOR]

The utilization rate of forging materials and the forging process parameters are affected by the shape and size of forgings. The measurement of the shape and size of forgings plays a key role in forging quality and efficiency of industrial production. At present, the laser measurement method is commonly used in forging measurement. The relatively large errors in the laser measurement are mainly caused by the dust at the measurement site, the angular deviation of the object and laser scanner, and the system error of the laser scanner. Therefore, a measurement method of outer diameter of ring forgings based on standard deviation algorithm is presented in this paper. Firstly, after resampling by the standard deviation algorithm, the more accurate data are obtained. Secondly, the good arc feature of the point cloud data is obtained by means of mean filter. Finally, the outer diameter of ring forgings is obtained by using ellipse fitting based on least squares principle. The feasibility of this method is verified by measuring two ring forgings with different outer diameters in the experiment. [ABSTRACT FROM AUTHOR]

In modern manufacturing industry, resource efficiency is negatively affected by the high fluctuations of demands within the global market. In this work, an intelligent cloud manufacturing platform is proposed to increase resource efficiency, productivity, and utilization rates in a smart manufacturing network by dynamically matching manufacturing services offers and requests through the broad sharing and on-demand delivery of distributed computational, software, digital, and physical manufacturing resources. The cloud platform is developed with particular reference to the sheet metal cutting sector and includes several modules. A database module is employed for users data input and storage. An intelligent assessment and optimization module performs the functional and geometrical assessments of the sheet metal cutting instances entered by customers and suppliers and makes use of a genetic algorithm to optimize the manufacturing solutions with specific attention to the surface utilization rate, as key performance index of resource efficiency. A decision-making module supports the supplier in the selection of the best production strategy and the customer in the evaluation and comparison of the best manufacturing solutions ranked according to the preferred criteria. To demonstrate the implementation of the proposed cloud manufacturing platform in a manufacturing network scenario, a case study including several customer and supplier instances is presented, showing the multiple manufacturing solutions proposed by the platform and the advantages in terms of industrial resource efficiency improvement. [ABSTRACT FROM AUTHOR]

Environmentally friendly products are increasing demand and the challenge is to combine them with optimal efficiency in industrial processes. In cold drawing, lubrication improves the surface and dimensional quality of the workpiece, reduce tool wear, work loads, and maintenance intervention. Ester-based, vegetable-derived lubricants have interesting properties, such as biodegradability and low toxicity, but their application in forming processes has not yet been investigated despite their use in machining operations. In this work, we evaluated the drawing strength , surface roughness, and microhardness of electrolytic copper in the dry (non-lubricated) condition and using three different types of lubrication: powdered graphite, molybdenum sulfide grease, and ester-based oil, a possible environmentally friendly lubrication substitute for petroleum-derived lubricants. The drawing strength proved to be 23.4% lower when using grease and ester-based oil compared to the dry process, and the grease and ester-based oil presented the same result regarding process performance and material characteristics. Thus, combined with the fact that the ester-based oil led to the lowest friction coefficient between cooper and tool steel among the tested lubricants, it was possible to demonstrate that it can be used as an alternative to conventional products in cold- drawing operations of the tested cooper alloy. [ABSTRACT FROM AUTHOR]

In this paper, we discuss how Cross Reality (XR) and the Industrial Internet of Things (IIoT) can support assembly tasks in hybrid human-machine manufacturing lines. We describe a Cross Reality system, designed to improve efficiency and ergonomics in industrial environments that require manual assembly operations. Our objective is to reduce the high costs of authoring assembly manuals and to improve the process of skills transfer, in particular, in assembly tasks that include workers with disabilities. The automation of short-lived assembly tasks, i.e., manufacturing of limited batches of customized products, does not yield significant returns considering the automation effort necessary and the production time frame. In the design of our XR system, we discuss how aspects of content creation can be automated for short-lived tasks and how seamless interoperability between devices facilitates skills transfer in human-machine hybrid environments. [ABSTRACT FROM AUTHOR]

The optimization of energy efficiency of industrial processes is a desirable objective. Roll forming is a continuous sheet metal forming process gradually producing profile-shaped parts. Although cold forming processes are usually comparatively energy-efficient, in roll forming, an efficiency of deformation of 25% and less has been observed in previous studies. At the same time, a significant energy-saving potential has been identified by means of the evaluation of drive torques and the readjustment of tool velocities. However, the efficiency of deformation is still small in comparison to other cold forming processes. In this study, a new decision rule for optimizing the tool velocity in roll forming is proposed and tested experimentally and numerically. The objective of this decision rule is, firstly, to avoid decelerating drive torques that counteract the forming process and need to be compensated by additional drive torques in other process steps. Secondly, the accelerating drive torques should not exceed a certain limit in order to ensure an effective process with respect to wear and surface quality of the products. The results show that a reduction of the energy demand of 66% in comparison with a conventionally operated roll forming process is possible resulting in an efficiency of deformation of 89%. Furthermore, the mechanism of the optimization is validated experimentally using a novel sensor concept, a sensorial parallel key. [ABSTRACT FROM AUTHOR]

Although an increasing number of researches regarding inter-organizational supply chain collaboration (SCC) were conducted, the resource sharing (RS) in SCC is still an under-explored subject. Many RS models are successfully applied in various industries for improving the efficiency of manufacturing. Meanwhile, due to the demand of higher flexibility in the garment production recently, RS has become a novel method in order to optimize the industrial organization. This study explored the feasibility of applying RS in the manufacturing stage of garment supply chain. A KPI based on extended ANP approach was designed to evaluate overall performance of different RS scenarios. Furthermore, a simulation-based heuristic was developed to generate and optimize RS scenario. Finally, experiments based on a discrete-event simulation model of garment production were conducted to examine the performance of all RS scenarios. By comparing the outputs of RS scenario and traditional scenario, the simulation shows that garment manufacturers could get great benefits by applying RS. We also found that the opener garment manufacturers towards RS, the more benefits they can obtain. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is widely used in mold manufacturing to form the intricate geometric features which are difficult to be produced by the conventional machining process. Owing to the use of a large number of electrodes, the cost and time consumption for electrode design and NC programming has become a bottleneck in boosting productivity of mold enterprises. Although computer-aided design (CAD)/computer-aided manufacturing (CAM) systems are widely used, because of the complex shapes and diversity of electrodes, manual operations are commonly employed to generate process plans and tool paths for electrode manufacturing, which is cumbersome and error-prone. In order to realize the intelligent manufacturing of electrodes in such a knowledge-intensive domain, this paper presents a feature-based integration approach of CAD/computer-aided process planning (CAPP)/CAM. A hierarchical taxonomy of electrode features is introduced and a hybrid feature recognition method is proposed to build multi-level feature tree. Then, feature knowledge and domain know-how based process planning and optimization are achieved followed by automatic tool path generation which aim to machining the electrode precisely and economically. In the framework of system integration, a structured product model is established to capture and encapsulate geometric entities, machining features, technical information, process plans, and measurement data of machining error to realize seamless flow of information among CAD/CAPP/CAM systems. The effectiveness and efficiency of the proposed approach are demonstrated by case studies and industry implementation. [ABSTRACT FROM AUTHOR]

Warpage is one of the most frequent issues facing the molding industry, especially for different wall thickness products. The aim of this study is to generate an optimization method in order to reduce warpage of a protector cover as much as possible. Topology optimization and deflection analysis were carried out to obtain a stiff design that resists warpage. The topology optimization results suggest a weak area/void in the product design. Then, a careful consideration in patterning the rib location and modification wall thickness based on topology data was introduced until it obtained an optimum wall deflection. Finally, the warpage conditions were found to optimize approximately 79.52% (left side), 76.12% (middle area), and 69.06% (right side) with respect to the reference model. [ABSTRACT FROM AUTHOR]

Recently, the new generation of information technology as cloud computing, big data, IoT, and AI has led to profound changes in manufacturing. Many new technologies and ideas have emerged in the field of system development, manufacturing process, industrial form, and business model, such as cloud manufacturing (CM). The primary feature of CM is to realize the integration and widely sharing of manufacturing services. Therefore, optimization of manufacturing services is vital for a CM system. Based on wide investigation, this paper summarizes the research progress of CM service optimization in recent years from the aspects of optimization strategy, optimization model, and optimization algorithm. And problems and development trends such as the research of task decomposition, service discovery and optimization integration, algorithm with big data, and optimized object are proposed. [ABSTRACT FROM AUTHOR]

In the last decade, aerial delivery system has been considered as a promising response to increasing traffic jams and incremental demand for transportation. In this study, a distance-constrained mobile hierarchical facility location problem is used in order to find the optimal number and locations of launch and recharge stations with the objective of minimizing the total costs of the system. System costs include establishment cost for launching and recharge stations, drone procurement, and drone usage costs. It is supposed that the demand occurs according to Poisson distribution, distributed uniformly along the network edges and is satisfied by the closest open facility. Since the flying duration of a drone is limited to its endurance, it may visit one or more recharge stations to reach to the demand point. This route is calculated by the shortest path algorithm, and the Euclidean distance is considered between nodes and facilities. It is proved that facility location problems are NP-hard on a general graph. Accordingly, heuristic algorithms are proposed as solution method. To illustrate the applicability of the algorithms, a case study is presented and the results are discussed. [ABSTRACT FROM AUTHOR]

Among all the main influential factors on the distortion of thin-walled parts, residual stress plays a key role before or after the machining process. It is worth to control and propose an approach to predict the precision of workpiece after the processing has been completed. In this study, through comprehensive multi-factor optimization analysis and investigation, a thin-walled workpiece with aerospace frame is taken as the research object, followed by the proposition of a method of predicting the processing accuracy of the thin-walled part during the milling affected by the residual stress. Using the proposed approach and considering of the residual stress as the optimization goal, the optimized milling path should be arranged from the inside to the outside in the case of a single characteristic. By contrast, for the machining with different characteristics, the best processing sequence should be assigned reversely, from the outside to the inside. After using the prediction method and verification with a target thin-walled part, this study concludes that the smaller magnitude of surface initial and machined residual stress will reduce distortion of the thin-walled part. In this way, the processing accuracy and stability of thin-walled parts can be both improved and raised to a higher level. [ABSTRACT FROM AUTHOR]

In extrusion industry, optimization of die design plays a critical role in contributing to the quality of the extruded profile as well as tooling life. This study addresses the fact that most dies today are typically designed following a trial and error approach based on empirical knowledge of the designer, which inevitably results in increasing scrap rate and cost. The purpose is to understand the effects of geometry on the performance of a porthole die in order to improve the life of tooling and quality of the final extruded shape using finite element based simulations. In this work, a finite element (FE) model is developed to simulate the extrusion of a commercial grade aluminum alloy to produce a condenser tube used for cooling systems. Using the Update Lagrange FE analysis first, the tooling components of the modular die are assumed to be rigid in order to achieve the right friction coefficients to validate the experimental data collected during trail runs of the modular die. Next, DOE is used to identify the relative influence of the three main parameters that control the geometry of the mandrel. For each parametric set, a steady-state FE analysis is run to predict the maximum weld pressure between the mandrel port webs. The insight gained from these simulations is used to optimize the mandrel geometry. [ABSTRACT FROM AUTHOR]

'Productivity is never an accident, it is always the result of a commitment to excellence, intelligent planning, and focused approach,' the phrase by Paul J. Meyer, an American businessman, has everything explained within it. Various quality improvement tools and techniques along with their integration have been attempted in the past for enhancing productivity levels in large-scale organizations across the globe. Similarly, new unification of these techniques can bring positive results even in a small- and medium-sized enterprise (SME). Authors, in this case study, use the synergy of two approaches, namely, 'Shainin system' and 'fuzzy analytical hierarchy process (AHP)' to enhance the productivity of a system. Shainin system stands close to a set of instruments that are clear to understand and easy to be applied, whereas AHP technique has a proven potential in decision-making and evaluation. Results, after successful implementation, indicate a monetary saving of $100,000, which is substantial for a SME. Emphasis is put on a coherent step-wise implementation of both the strategies and linking them together to inculcate the best potential outcomes. [ABSTRACT FROM AUTHOR]

Dynamically changing machining conditions and uncertain manufacturing resource availability are forcing manufacturing enterprises to search advanced process planning in order to increase productivity and ensure product quality. As growing quantities of the three-dimensional process models are gradually applied, reusing the embedded manufacturing information in process models with less time and lower cost attracts a lot of attention. In this paper, a new flexible method is presented to reuse the existing process information based on retrieval of the similar machining feature. First, the three-level organization model is introduced to represent the process information; the machining feature which is seen as the parent layer carries the corresponding manufacturing information. To ensure accurately that the process information are obtained, the associated mechanism between the machining feature and process information is created. Second, an eight-node representation scheme is designed to represent the similar machining feature having same variations in topology and geometry. For accelerating similar feature retrieval, the extension-attributed adjacency graph and the topological relationship of the machining feature faces are built. Finally, some aircraft structural parts are utilized in the developed prototype module to verify the effectiveness of the proposed method. This method can be used as the basis for accumulation of the process information; it can promote the development and application of the intelligent process planning. [ABSTRACT FROM AUTHOR]

In this paper, a single-item capacitated lot-sizing problem in a flow-shop system with energy consideration is addressed. The planning horizon is split into T periods where each one is characterized by a duration, an electricity cost, a maximum peak power and a demand. This problem is NP-hard, since its simple version is known to be NP-hard. Therefore, to deal with the complexity and to find good quality solutions in a reasonable time, a fix-and-relax heuristic and a genetic algorithm are developed. Computational experiments are performed on different instances to show the efficiency of these proposed heuristics. To evaluate their performances, problems of different scales have been studied and analyzed. [ABSTRACT FROM AUTHOR]

As an efficient process, narrow gap gas metal arc welding (NG-GMAW) was employed to join thick plates of 5083 Al-alloy. In this paper, arc characteristics in NG-GMAW of 5083 Al-alloy and their influence on the weld bead geometry were studied with synchronous acquisition system for various groove types, welding modes, welding current levels, and swing frequencies. The arc profiles were measured and appear to be entirely different compared with that in bead-on-plate GMAW process, meanwhile the arc size changes with varied welding mode, welding current, and arc swing frequency. Arc characteristics highly affect the weld bead geometrical parameters such as bead width, welding penetration, sidewall penetration, and weld surface concavity. Linear regressions were made between the arc characteristics and the weld bead geometrical parameters both in swing and non-swing arc process. It showed that a negative linear correlation exists between welding penetration and arc length, sidewall penetration, and arc warpage, while a positive linear correlation exists between weld surface concavity and arc warpage in non-swing arc process. In contrast, a non-linear correlation exists among them and the variation tendency remains the same in swing arc process. [ABSTRACT FROM AUTHOR]

Cleaner production and sustainability are of crucial importance in the field of machining processes where great amount of energy is being consumed. This paper outlines the application of gray relational theory in order to optimize the machining parameters for Ti6Al4V in high-speed dry electrical discharge milling (EDM) using a tubular graphite electrode. The objective of optimization is to achieve simultaneously the minimum power consumption and the maximum material removal rate (MRR). The maximum MRR 5634 mm/min was obtained with a power consumption of 7.61 kW. MRR stayed in the similar level, and power consumption was reduced by 17.82 % compared with that of the parameters in our previous study. Current is the main effect factor on MRR and power consumption. The corresponding single pulsed experiments were conducted to deeply explore the reasons for the influence of air pressure and electrode rotation speed on the MRR and power consumption. [ABSTRACT FROM AUTHOR]

This paper proposes a new control chart, denoted by $$ {\overset{-}{X}}^{att} $$ , for evaluating the stability of a process mean. This chart is based on attribute inspection rather than physical measurements (taken with an instrument such as a caliper or precise balance) of the quality characteristics of interest of the sampled items. Based on a go-no-go gauge device (which generates five categorizations), the average of a quality characteristic of interest is controlled. In equally spaced times, samples of n items are collected, and the averages are estimated by means of $$ {\overset{-}{X}}^{att} $$ based solely on the obtained categorization to decide whether the process is in control. Once the distribution of $$ {\overset{-}{X}}^{att} $$ is calculated, the decision problem is defined in terms of a mathematical programming formulation to find the dimensions to be used in the go-no-go gauge and to find the control limits that minimize the average run length (ARL) for the out-of-control situation and that are constrained to a prefixed ARL for the under-control situation. As shown by extensive computational experiments, the newly introduced $$ {\overset{-}{X}}^{att} $$ control chart outperforms the conventional $$ \overset{-}{X} $$ control chart for small shifts in the means and is still competitive otherwise. Because the new $$ {\overset{-}{X}}^{att} $$ control chart uses attributes, it can be considered a viable alternative to the conventional $$ \overset{-}{X} $$ control chart. [ABSTRACT FROM AUTHOR]

This paper presents the optimization of cutting forces, average surface roughness, cutting temperature, and chip reduction coefficient in turning of Ti-6Al-4V alloy under dry and high pressure coolant (HPC) that is applied at the rake and flank surfaces simultaneously. The experimental design plan was conducted by the full factorial parameter orientation. The optimization has been conducted in two ways: firstly, by using signal-to-noise ratio-based Taguchi method as mono-objective optimization; secondly, by using gray relational analysis integrated with Taguchi method as multi-objective optimization. In either method, the cutting speed, feed rate, and cutting condition were considered as the inputs to the optimization. The mono-objective optimization concluded that the 156 m/min cutting speed and 0.12 mm/rev feed rate when run under HPC optimized the cutting forces and roughness, and when operated under dry optimized chip reduction coefficient, the cutting temperature was minimized at 78 m/min and 0.12 mm/rev feed rate. The multi-objective optimization concluded that Ti alloy turning system is optimized at 156 m/min cutting speed and 0.12 mm/rev feed rate under HPC. [ABSTRACT FROM AUTHOR]

Improvements to efficiency and productivity have attracted growing interest from companies, and modularity is considered a strategic alternative to achieve these goals. However, empirical evidence to support the expected benefits of modularity is scarce in the literature. Therefore, it is necessary to evaluate the impact of modularity on the efficiency of, for example, an area or production system. The present work takes a case study approach to analyze the effects of the implementation of product modularity on the production process efficiency of a bus manufacturer. The effects of modularity are evaluated longitudinally using a combination of data envelopment analysis (DEA), analysis of variance (ANOVA), and causal impact assessment in time series (CausalImpact). The results show that the effects of modularity on efficiency are positive and significant. In addition, there are significant differences between modularized and non-modularized products. Finally, the research establishes causality between modularity and production process efficiency improvements in the examined company. [ABSTRACT FROM AUTHOR]

Any organization can be defined as a system whose control is governed by the flow of information across all systems to which it belongs. Information is defined as an infinite-dimensional entity that is constantly moving and being assessed through mechanisms and control systems in order to achieve a goal. This article presents an approach for design of parameters based on information content. The development of a product is evaluated from the initial concept to the optimal design of its production parameters. In this approach, the values of a set of design parameters related to functional requirements are considered. The behavior of these functional requirements is modeled by a Gaussian distribution, which represents the design distribution. Once the product is produced in mass, a Gaussian distribution also models the behavior of the functional requirements and represents the process distribution. The intersection between distributions can be seen as a common range that describes the compliance with the functional requirements. As the process distribution approaches the most to the design distribution, the best functional requirements are met. Best levels of the parameters of the case study were defined based on this approach, which enables to reduce the variation on the response variable. [ABSTRACT FROM AUTHOR]

Existing decision-making methods for selection of feature operation chains normally only consider the processing requirements and neglect the impacts of the selected feature operation chain on the production line of the enterprise. As a result, the newly inserted manufacturing task may lead to a decrease in the manufacturing stability, which increases the malfunction probability of some machines and reduces production efficiency simultaneously. To solve this problem, a decision-making method for feature operation chain selection considering processing requirements and manufacturing stability is presented. The proposed method explores the factors affecting selection of a feature operation chain and develops a fuzzy comprehensive evaluation model of feature operation chain selection using hole feature as an example, thus realizing the selection of possible feature operation chains based on processing requirements. The method further applies a complex network theory to build an enterprise manufacturing network model, analyzes the manufacturing network stability, and then evaluates potential feature operation chains using a network stability index. Experimental results show that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

The ability of managing a production system is an essential factor for maintaining the position in the market. Companies are constantly looking for optimizing their process in order to follow this dynamic competition. In this sense, different tools and methodologies are applied in order to support the decision-making, based on algorithm solutions and objective results. Regarding that, this paper presents an analysis of the best alternatives to eliminate productive process bottlenecks of martensitic stainless steel (AISI-420) of a metal-mechanics industry from South of Brazil by applying the analytic hierarchy process (AHP). The results indicate the best alternative to extinguish the production process bottleneck as the replacement of the material of the polishing rolls, from sisal to cotton, which represents 27.1 % of preference regarding all the alternatives. Following it is the increase of speed of the conveyors and the verification and substitution of industrial squirts, all of them with relative high impact on the solution of the problem. Based on the changes made, it is possible to notice great reduction in losses and reprocess, besides the improvements pointed out by the inspection and quality sector. Considering all the improvements, the results show 74 % of efficiency when compared to the previous situation. [ABSTRACT FROM AUTHOR]

Monitoring and early detection of chatter are the key techniques to avoid the harmful effects caused by chatter in manufacturing process. The key for early chatter detection is to capture the feature signatures. A convenient and reliable technique is presented in this study to detect chatter in gear grinding process based on servo feed motor current and wavelet packet transform. Wavelet packet transform was used to monitor the energy change in the frequency domain and to identify the feature frequency band with respect to chatter, the result of which was confirmed by the impact hammer test. Standard deviation and energy ratio of the feature frequency band signal were chosen as the indexes of chatter monitoring. Combining these two chatter features, the state of the grinding process could be classified and chatter could be detected reliably in industrial application with proper thresholds. Acceleration signals of the machine tool were used as a reference to compare with the results from current signals. In every stage of the grinding process, the feature frequency band signals of current and vibration signal have shown very coincident variation trend. Both theoretical analysis and experimental results manifested the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Post-stretching elongation is a crucial process parameter in stretch bending process because of its significant influence on the forming effects. In this paper, the displacement loading method was presented to control the movement of the jaw in order to avoid the instability of tensile force in mechanical loading method. Typical aluminum hollow profile used in high-speed trains was selected as the research object. Stretch bending process with five levels of post-stretching elongations was carried out by finite element (FE) simulation. The simulation results reveal that an increment of the post-stretching elongation leads to smaller shape error and springback but larger cross-sectional distortion and spatial twist. The forming defects index, which is based on normalizing and weighting four forming defects, was put forward to evaluate comprehensive forming effects. Furthermore, the backtracking method was used to obtain the optimal post-stretching elongation. Experimental validation was conducted on stretch-wrap bending machine. The experiment part meets the processing requirements, thereby validating the reasonability of the optimization method. Measured results showed consistency with numerical analysis which verifies the feasibility of using FE simulation to guide the experiment. [ABSTRACT FROM AUTHOR]

In the present work, experiments and analyses have been made to investigate the influence of machining parameters on vibration and surface roughness in traverse cut cylindrical grinding of stainless steel material. The experiments have been conducted as per Box-Behnken design matrix with input parameters as infeed, longitudinal feed, and work speed. Mathematical modeling has been done by response surface methodology (RSM) to develop relationships between process parameters and output response(s). The adequacy of the developed models has been tested with analysis of variance. The contour and surface plots for vibration and surface roughness have been made to reveal how output responses vary with change in the machining parameters. Finally, multiobjective genetic algorithm (MOGA) has been applied to optimize vibration and surface roughness simultaneously. And then, predicted parametric condition has been validated by confirmatory experiments. The proposed optimization methodology (RSM cum MOGA) seems to be useful for analyzing and optimizing any manufacturing process where two or more input parameters influence more than one important output responses. [ABSTRACT FROM AUTHOR]

A procedure to find a unique solution for multi-response optimization problems based on indexing is presented. The procedure utilizes principal component analysis to map the original data to a new vector of component scores, transforming the original response variables into uncorrelated principal components. This process involves loadings that are the elements of the eigenvectors corresponding to the eigenvalues of response variables in the correlation matrix. It is shown that for a given eigenvalue λ, its corresponding eigenvectors are not unique, which could lead to different 'optimal' parametric (factor-level) settings and will further mislead the process or product improvement strategy. The proposed indexing method will determine a unique optimal solution in the presence of (2)(p!) combinations of eigenvectors. [ABSTRACT FROM AUTHOR]

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining. [ABSTRACT FROM AUTHOR]

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product. [ABSTRACT FROM AUTHOR]

Multi-objective optimization of oblique turning operations while machining AISI H13 tool steel has been carried out using developed finite element (FE) model and multi-objective genetic algorithm (MOGA-II). The turning operation is optimized in terms of cutting force and temperature with constraints on required material removal rate and cutting power. The developed FE model is capable to simulate cutting forces, temperature and stress distributions, and chip morphology. The tool is modeled as a rigid body, whereas the workpiece is considered as elastic-thermoplastic with strain rate sensitivity and thermal softening effect. The effects of cutting speed, feed rate, rake angle, and inclination angle are modeled and compared with experimental findings. FE model is run with different parameters with central composite design used to develop a response surface model (RSM). The developed RSM is used as a solver for the MOGA-II. The optimal processing parameters are validated using FE model and experiments. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer's inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time. [ABSTRACT FROM AUTHOR]

Global competition forces the process industries to continuously optimize plant operation. One of the latest trends in efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model- or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed. [ABSTRACT FROM AUTHOR]

The economic and competitive pressures have made it imperative for organizations to focus on third party or outsourcing to reduce costs and improve operating efficiencies. The thrust of global economy drives the organizations to outsource process, parts, and labor, virtually anywhere in the world and get the desired combination of low cost and high quality. However, ineffective utilization of shipment practices in supply chain prevents to achieve the anticipated outsourcing benefits. In this research, a three-stage inventory model is developed to address the outsourcing issues with different shipment policies between manufacturer, exporter, and assembly point for any manufacturing industry. The model provides a measure for establishing an optimal balance between two conflicting objectives viz. net costs involved and the transportation discounts with consideration of holding cost at all the points in the supply chain. Owing to inherent computational complexities of the problem with higher dimensions, various deterministic approaches practically fail. Proposed work, therefore, utilizes a nature-inspired evolutionary algorithm, namely particle swarm optimization, to solve the problem. This paper applies enhanced particle swarm optimization, a variant of particle swarm optimization for solution purpose. Furthermore, the practical benefits and implications of the proposed model are demonstrated. The results obtained delineate efficacy to handle the fluctuations in the possible shipment options and simultaneously deciding the optimal shipment policies. [ABSTRACT FROM AUTHOR]

When machining complex geometries on five-axis machining centres, the orientation and positioning of the workpiece in the machine workspace are generally chosen arbitrarily by the operator from the Computer-Aided Manufacturing software. Nevertheless, these two factors have considerable influence on the machining time. The present article firstly studies the choice of workpiece orientation. Relying on analysis of the machine's kinematic behaviour, orientations of the workpiece in the machine workspace are proposed minimising the overall distance travelled by the rotary axes. Secondly, choice of workpiece positioning in translation is studied. To this purpose, the work volume in five-axis machining is identified so as to avoid overshooting the machine travels when the program is executed. The optimum positioning is chosen to minimise the overall distance covered by the machine's axes of translation. Finally, the proposed method provides for a workpiece setup to be adopted that minimises the distances covered by the machine axes. This leads to reduced machining time with concomitant gains in productivity and greater respect for the cutter/workpiece relative feed rate for enhanced quality. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is one of the advanced methods of machining. Most publications on the EDM process are directed towards non-rotational tools. But rotation of the tool provides a good flushing in the machining zone. In this study, the optimal setting of the process parameters on rotary EDM was determined. A total of three variables of peak current, pulse on time, and rotational speed of the tool with three types of electrode were considered as machining parameters. Then some experiments have been performed by using Taguchi's method to evaluate the effects of input parameters on material removal rate, electrode wear rate, surface roughness, and overcut. Moreover, the optimal setting of the parameters was determined through experiments planned, conducted, and analyzed using the Taguchi method. Results indicate that the model has an acceptable performance to optimize the rotary EDM process. [ABSTRACT FROM AUTHOR]

Computer-aided process planning is an important interface between computer-aided design and computer-aided manufacturing in computer-integrated manufacturing environments. In this paper, the complicated process planning is modeled as a combinatorial optimization problem with constraints, and a hybrid graph and genetic algorithm (GA) approach has been developed. The approach deals with process planning problems in a concurrent manner by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the global optimal objective. Graph theory accompanied with matrix theory, as the basic mathematical tool for operation sequencing, is embedded into the main frame of GA. The precedence constraints between operations are formulated in an operation precedence graph (OPG). The initial population composed of all feasible solutions is generated by an elaborately designed topologic sort algorithm to the OPG. A modified crossover operator guaranteeing only feasible offspring generated is used, two types of mutation strategies are adopted, and a heuristic algorithm is applied to adjust the infeasible plan generated by the mutation operator to the feasible domain. A case study has been carried out to demonstrate the feasibility and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed ( V), feed rate ( f), diameter of cutting equipment, and depth of cut ( a) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance ( R), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness. [ABSTRACT FROM AUTHOR]

This paper illustrates the development and implementation of a parameter optimization methodology to improve impact harshness (IH) of road vehicles. A full ADAMS model of a small commercial vehicle is used as the IH test vehicle. The methodology involves the use of design of experiments methods together with response surface methodology. Significant design parameters affecting IH of the vehicle are first determined by the screening experiments. Once the critical parameters are identified, they are optimized to achieve improvement in the IH by constructing response surface. The optimization results indicate that the selected suspension parameters are capable of improving IH performance of the full vehicle ADAMS model by minimizing longitudinal and vertical acceleration responses. The results also show that considerable improvement can be obtained by using the proposed parameter optimization methodology. [ABSTRACT FROM AUTHOR]

The world's manufacturing community has been questing for ways to face the onslaught of competition. One of those ways is the adoption of agile manufacturing (AM) paradigm. AM paradigm enables a company to quickly respond to the customers' dynamic demands. In order to implement AM paradigm, a model named 'model for enhancing total agility level' (METAL) is proposed in this paper. METAL enunciates the assessing of the total agility level (TAL), identifying the weak AM criteria and subsequently strengthening them. The practicality of METAL has been explored in an air dryer manufacturing company. During this case study, refrigeration air dryer was considered as AM capable product. After assessment, three weak AM criteria were identified. Proposals were drawn to strengthen these three weak AM criteria. These proposals envisage the strengthening of the weak AM criteria through the removal of nonvalue adding activities, utilization of mathematical models, and creation of web portal. The reassessment has indicated the possibility of enhancing the TAL value in the above company through the implementation of these proposals. The experience of carrying out this research has revealed that the deployment of METAL would facilitate the contemporary companies to systematically infuse AM paradigm and enhance their TAL values. [ABSTRACT FROM AUTHOR]

Facility location decisions play a vital role in the strategic design of the global supply chain. Global supply chains are vulnerable towards different types of risks factor that causes disturbance in the allocation of certain goods at the required place and time, and with the required quality and quantity. This paper presents a model of the multi-stage global supply chain network problem incorporating a set of risk factors (such as: late shipment, exchange rates, quality problems, logistics and transportation breakdown, and production risks), their expected values and probability of their occurrence, and associated additional cost. Different scenarios are considered to demonstrate the applicability of the model. Optimal decisions regarding the facility locations and inter-echelon quantity flows in the global supply chain are based on initial information for the risk factors. Further, if any change in the expected value of risk factor has occurred, the inter-echelons shifting take place to minimize the overall cost of the supply chain. [ABSTRACT FROM AUTHOR]

The problem of grouping has attracted the attention of researchers and practitioners both. They have proposed number of schemes for classification of part-types into part-families and machines into machine-cells based on machine-requirement data. Real issue lies in judging objectively the relative merit of the varying grouping solutions that are obtained from their schemes. To this end, many measures have already been suggested by several researchers. In the present paper, these measures have been summarised, and their suitability is discussed. In particular, the measure due to Chandrasekharan and Rajagopalan (Int J Prod Res 24(2):451-464, ) has been analysed in details. Identifying the weakness of their measure, two new efficiency measures have been proposed in the present paper that relate grouping efficiency not only with exceptional elements and voids but also with the number of groups formed and size of the grouping problem. It has been shown by taking several examples from literature that the proposed measures have much better characteristics of discriminating good groups from bad ones. [ABSTRACT FROM AUTHOR]

In organizations with a centralized decision making environment, production usually involves the participation of more than one individual unit, each contributing in part to the total production. The planning problem involves determining the number of products to be produced by all individual units in the next season when demand changes can be forecasted. This paper introduces a data envelopment analysis approach to making future production plans in a centralized decision making environment when demand changes can be forecasted in the next production season. The approach proposed in this paper takes the size of operational units into account so that the planned production for each unit becomes proportionate to the ability of the units. A real world data set is used to illustrate the applicability of the approach. [ABSTRACT FROM AUTHOR]

In the drilling of stacked materials, burrs produced on both the surface layer and the interlayer have some undesirable characters with regard to assembly quality, and deburring is a time consuming and costly operation. This paper presented an experimental study on the dry drilling of Ti-6Al-4V titanium alloy and 7075-T6 aluminum alloy stacked materials, which was performed by using uncoated cemented carbide drills. The burr size was evaluated at various spindle speeds, feed rates, stacking sequences, and clamp forces. After which, the burr morphology was observed and analyzed. Finally, a new multi-objective optimization algorithm, which was derived from the game theory, was used to select optimum process parameters to minimize burr size. The best process parameters recorded in this paper were at the spindle speed of 2,000 r/min, the feed rate of 0.075 mm/r, the pressure of 0.3 MPa, and the stacking sequence of the Ti-6Al-4V titanium alloy on top of the 7075-T6 aluminum alloy. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to develop a new type of thermal friction drill made of sintered carbide. In addition, to optimize the machining process of the thermal friction drilling using Taguchi method is explored. The experiments were conducted on a 30 × 30 × 2 mm SUS 304 stainless steel plate. The effects of friction angles (FA), friction contact area ratio (FCAR), feed rate (FR), and spindle speed (SS) on the two quality characteristics, surface roughness (SR) and bushing length (BL), were also investigated. After conducting all the experimental trials, the analysis procedure followed. Firstly, the significance of the four parameters on the two quality characteristics was examined by ANOVA. Then the optimal combination levels of parameters for SR and BL were determined based on the S/N ratios. Finally, confirmation experiments were conducted to verify the experimental findings. Results showed that FA and SS were the significant machining parameters that most intensively affect SR while FCAR was the only significant parameter for BL. Specially, the SR and BL were greatly improved when used in the optimized parameters settings. More importantly, the performance of the friction drill was conducted 60 runs. The thermal friction drill demonstrated a smoother, mirror-like surface and showed less wear. This proved that thermal friction drilling provided the better machining performance and longer tool life. [ABSTRACT FROM AUTHOR]

In distributed manufacturing environments, the real competitive edge of an enterprise is directly related to the optimization level of its supply chain deployment in general, and, in particular, to how it allocates diverse manufacturing resources optimally. This is faced with increasing challenges caused by the conflicting objectives in manufacturing integration over distributed manufacturing resources. This paper presents a new manufacturing resource allocation method using extended genetic algorithm (GA) to support the multi-objective decision-making optimization for supply chain deployment. A new multi-objective decision-making mathematical model is proposed to evaluate, select, and sequence the candidate manufacturing resources allocated to sub-tasks composing the supply chain, by dealing with the trade-offs among multiple objectives including similarity, time, cost, quality, and service. An extended GA approach with problem-specific two-dimensional representation scheme, selection operator, crossover operator, and mutation operator is proposed to solve the mathematical model optimally by designing a chromosome containing two kinds of information, i.e., resource selection and resource sequencing. A case study is carried out to demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Ultrasonic machining (USM) process has several important performance measures (responses), some of which are correlated. For example, material removal rate and tool wear rate are highly correlated. Although in the recent past several methods have been proposed in the literature to resolve the multi-response optimization problems, only a few of them take care of the possible correlation between the responses. All these methods primarily make use of principal component analysis (PCA) to consider the possible correlation between the responses. Process engineers may face the difficulty of selecting the appropriate method because the relative optimization performances of these methods are unknown. In this paper, two sets of past experimental data on USM process are analysed using three methods dealing with the multiple correlated responses, and the optimization performances of these three methods are subsequently compared. It is observed that both the weighted principal component (WPC) and PCA-based TOPSIS methods result in a better optimization performance than the PCA-based grey relational analysis method. However, the WPC method is preferable because of its simpler computational procedure. [ABSTRACT FROM AUTHOR]

Scheduling of casting processes is the problem of determining the number of products that will be manufactured in each casting shift so as to optimize a predetermined objective function. In this paper, we propose a linear programming (LP) model that maximizes the average efficiency of process time for casting in real foundries. The considered casting process is pressure die casting, the most prevalent permanent mold casting process, equipped with an automated die exchange device. In permanent mold casting, the use of a mold or die installed on a casting machine requires a significant process time for casting. The objective function of the proposed LP model is defined as the average ratio of the actual process time to the pre-assigned operating time in a shift. A solution that minimizes the objective function will provide the maximum surplus time, hence optimizing the efficiency of casting operations in terms of process time. Numerical examples are presented to illustrate the applicability and reliability of the proposed model. The results demonstrate that this model is an effective and conveniently implemented tool for solving real casting sequences in a simple and practical framework. [ABSTRACT FROM AUTHOR]

Corrective machining is fundamental to obtain higher precision than the machine tool based on error inspection and calculation techniques. Generally, the error to be corrected is calculated by minimizing the material removal volume. However, there are often constraints for corrective machining, such as nonnegative error and allowance requirements, making it a constrained minimization problem. The basic algorithm for error calculation combines the alternating optimization method and the successive linearization method. Whereas, the sequence of objective function values is not guaranteed monotonously decreasing because of the local validity of linearization. Therefore, in the improved algorithm, the problem is reformulated as an inequality-constrained least square problem. Bound constraints are imposed on the optimization variables to keep local validity. Then, the line search and Palacios-type adjustment strategy are incorporated in each iteration in order to find a better point reducing the value of objective function. This point is chosen as the start of the next iteration. Finally, better convergence of the improved algorithm is verified by numerical simulations and a case of application in optical machining. [ABSTRACT FROM AUTHOR]