Modern Manufacturing Environments Research Articles

Process Parameter Optimization for Minimizing Vibrations and Surface Roughness During Turning EN19 Steel Using Coated Carbide Tool

In modern manufacturing environments, the quality assurance of machined parts has attracted great attention from manufacturers. Surface finish and vibrations during turning play an important role during this process. Comparatively less work is done in the field of process parameter optimization. EN19 is a hard steel which finds its application in shafts, gears, spindles, oil and gas industries. Due to the hardness of the material, 223-277 BHN, turning this material is a difficult task. Thus, optimization of various cutting parameters is required in this situation. In the current work, an attempt has been made to optimize machining parameters when turning EN19 with coated carbide inserts (Titanium Nitride coated CCMT060204-HM YBC251 inserts) on conventional lathe machine. During this process, three factors, namely, MQL Condition (2 levels), Depth of cut (3 levels) and Spindle Speed (3 levels) are considered. Mixed level optimization is performed using Taguchi analysis with L18 orthogonal array. The considered factors/parameters are optimized for Surface Roughness and Vibrations with the objective functions being to minimize the same

THE IMPACT OF THE USE OF TARGET COSTING IN LIGHT OF MODERN MANUFACTURING ENVIRONMENT ON PERFORMANCE EVALUATION STANDARDS (A FIELD STUDY ON THE CEMENT SECTOR)

The study seeks to identify the extent to which employees are aware of the target cost method and its application as a modern cost management tool in the modern manufacturing environment in the cement sector, The extent to which performance appraisal metrics are used and used, Changes in the manufacturing environment and their impact on performance measures, And test the relationship between the use of the target cost method and performance appraisal metrics, and testing the relationship between changes in the modern manufacturing environment and performance appraisal metrics, The questionnaire was distributed to a sample of (57). senior, middle and technical officials at Kuwait Cement Factory in Kuwait. The results showed that there is a significant effect on the use of target costs on performance evaluation measures in the modern manufacturing environment. , In terms of the components and dimensions of each, which leads us to conclude that the target cost and modern manufacturing environment have an impact on the evaluation criteria. The study recommended: - Follow scientific methods and programs in cost management, especially methods and programs of operations research, which determine the optimal cost of production under the constraints of available resources. - The need to raise the efficiency of workers in cost accounting through the holding of training courses to introduce modern methods of cost management. - The need to integrate financial and non-financial performance indicators with a strategic dimension in performance evaluation. - The duty to develop measures of financial performance to keep pace with the rapid changes in the modern business environment.

Opportunities and Challenges for Statisticians in Advanced Pharmaceutical Manufacturing

Advanced pharmaceutical manufacturing technologies have the potential to make substantial contributions toward reducing waste, cost, and cycle time. However, the steady introduction of advanced manufacturing systems such as continuous manufacturing processes, automated microbioreactors, robotics, etc. create both challenges and opportunities for nonclinical statisticians who support chemistry and manufacturing control (CMC) activities in the pharmaceutical industry. Not only will closer collaboration be essential for optimal utilization of statistical tools, but CMC statisticians may need to acquire new engineering statistical skills as well as some knowledge of how to deal with “big data” issues. It is also expected that the confluence of advanced manufacturing technologies and statistical methods will require input from nonclinical statisticians on some regulatory issues. The purpose of this article is not only to alert CMC statisticians to these opportunities and challenges, but to provide a call-to-action for the industry and academia to better prepare statisticians for careers in modern manufacturing environments.

Perceived stress in human-machine interaction in modern manufacturing environments-Results of a qualitative interview study.

Rapid advances in digitization technologies are changing modern working conditions especially in industrial settings. Consequently, employees are confronted with new forms of human-machine interaction. Whether changes in working conditions in general, and the increasing relevance of human-machine interaction in particular, affect psychosocial working conditions, and employee's health is currently matter of debate, but empirical data are lacking. Therefore, we conducted semistructured interviews with 36 employees working in five different companies. The interviews were aimed at identifying potential stressors associated with the introduction and use of modern technologies in the manufacturing industry. The results show that stressors linked to human-machine interaction are technical problems, poor usability, low situation awareness, and increased requirements on employees' qualification. For example, technical problems such as breakdowns or slowdowns were described as a main stressor when employees were not qualified to handle these problems on their own, thus decelerating work flows and causing additional time pressure. Overall, the results show that problems in human-machine interaction, which have been observed in laboratory and nonindustrial settings, also apply to industrial work places with highly automated working conditions and are a potential source of stress. These factors should be considered in psychosocial risk assessment of work-related stressors.

Linked Data Architecture for Assistance and Traceability in Smart Manufacturing

Traceability systems and digital assistance solutions are becoming increasingly vital parts of modern manufacturing environments. They help tracking quality-related information throughout the production process and support workers and maintenance personnel to cope with the increasing complexity of manufacturing technologies. In order to support these use cases, the integration of information from different data sources is required to create the necessary insights into processes, equipment and production quality. Common challenges for such integration scenarios are the various data formats, encodings and software interfaces that are involved in the acquisition, transmission, management and retrieval of relevant product and process data. This paper proposes a Linked Data based system architecture for modular and decoupled assistance software. Its web-oriented approach allows to connect two usually disparate data sets: semantic descriptions of complex production systems on the one hand and high-volume and high-velocity production data on the other hand. The proposed concept is illustrated with a typical example from the manufacturing domain. The described End-of-Line quality assessment on forming machines is used for traceability and product monitoring.

Engineering Technology Education Based on the Reconfigurable Manufacturing Paradigm: A Case Study

Engineering technology deals with the development and implementation of engineering and technology. Engineering technology education focuses on providing more specialized and applied engineering education, as opposed to theoretical knowledge. Consequently, institutions providing engineering technology education curricula need to develop applied courses that take advantage of recent advancements in automation, information and communication technologies to show integration, interoperability, modularity and flexibility in modern manufacturing environments. Since the late 1990s, the reconfigurable manufacturing paradigm has been introduced to extend the flexibilities and capabilities of modern manufacturing systems. Unfortunately, in scientific literature, there is still a lack of works that describe success stories related to the application of the reconfigurable manufacturing paradigm in engineering technology education. This article provides a case study that takes advantage of the reconfigurable manufacturing paradigm to introduce students to the complex tasks of design and operation of reconfigurable manufacturing systems (RMS). The article describes how a course is designed based on project management methodology in order to get students faced with the complexity of designing, implementing, assessing the performance and operating an RMS.

A Case Study of 3D Printed PLA and Its Mechanical Properties

In this modern manufacturing scenario cost effectiveness and environment friendly design of a production process plays a vital role. This article focuses towards the newer manufacturing technique 3D Printing (3DP) and its feasibility to implementation towards biomedical components. 3DP is technology which can create complex geometry into a solid model. This solid model is made up Fused Deposition Modeling (FDM) method which is a type of rapid prototyping method. In this study Poly Lactic Acid (PLA) is considered as an alternate to the existing material. PLA is a biodegradable polymer which possesses good strength and biodegradable property which is highly needed in production of newer components through 3DP. The study begins with creation of solid 3D model based on the ASTM standards to test the strength of the material using micro tensile testing and biodegradable test of component using landfill method. The results clearly shows that the strength of the test specimen is strongly satisfies which can be further used as any biomedical component as well as the land fill test of the specimen over a period of 30 days to 60 days shows good amount of weight reduction which is also a fine outcome of this research work. Another way to degrade PLA by using bacterial degradation such as Amycolatopsis coloradensis are able to degrade PLA. A purified protease from amycolatopsis sp., PLA depolymerise, can also degrade PLA.

On the control chart with estimated parameters

Statistical monitoring of multivariate processes is becoming increasingly important in modern manufacturing environments. Typical equipment may have multiple key variables to be measured continuously. Hotelling’s chart was originally applied for monitoring the mean vector of multivariate quality measurements. In practical problems, estimated parameters are needed and their use will modify the properties of control charts. The Average Run Length (ARL), an indicator of the performance of the control charts, will be larger when the estimated parameters are used. As one contribution of the paper, we provide a rigorous proof of this phenomenon which has been reported in several empirical studies. Furthermore, in order to design an efficient chart with estimated parameters, it is necessary to have a method to calculate or approximate the ARL function. An existing approach in the literature is based on extensive Monte-Carlo simulations. In this paper, we propose a novel approach by providing an analytic approximation of the ARL function in the however limited case of univariate observations.

A New Heuristic for Modern Manufacturing Environment Scheduling Problems

A New Heuristic for Modern Manufacturing Environment Scheduling Problems

Monitoring multivariate process variability with individual observations via penalised likelihood estimation

Excessive variation in a manufacturing process is one of the major causes of a high defect rate and poor product quality. Therefore, quick detection of changes, especially increases in process variability, is essential for quality control. In modern manufacturing environments, most of the quality characteristics that have to be closely monitored are multivariate by the nature of the applications. In these multivariate settings, the monitoring of process variability is considerably more difficult than monitoring a univariate variance, especially if the manufacturing environment only allows for the collection of individual observations. Some recent charts, such as the MaxMEWMV chart, the MEWMS chart and the MEWMC chart, have been proposed to monitor process variability specifically when the subgroup size is equal to 1. However, these methods do not take into account the engineering and operational understanding of how the process works. That is, when the process variability goes out of control, it is often the case that changes only occur in a small number of elements of the covariance matrix or the precision matrix. In this work, we propose a control charting mechanism that enhances the existing methods via penalised likelihood estimation of the precision matrix when only individual observations are available for monitoring the process variability. The average run length of the proposed chart is compared with that of the MaxMEWMV, MEWMS and MEWMC charts. A real example is also presented in which the proposed chart and the existing charts are applied and compared.

Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

Investigations on surface characteristics over an Aluminium alloy in CNC turning operation

It is universally acknowledged that the performance of any machining process is usually evaluated in terms of its productivity and surface quality. Each of these controlling criteria are affected directly and in different way by the tool edge wear, fracture, chatter, surface roughness, cutting force etc., among these responses from machining operation, surface roughness is always considered as one of the most reliable measures for tool wear monitoring and breakage detection. This paper focuses on developing an effective, inexpensive surface recognition system which could be implemented in the modern manufacturing environments. Experiments have been conducted on Aluminium 6061 T6 and for measuring surface roughness based on Design of Experiments (DOE). A statistical multiple regression model has been developed for correlating the values obtained. For the purpose of correlation, DOE software has been used and ANOVA analyses have been carried out to identify the significant factors affecting surface roughness. The experimental values are analyzed in the optimizer technique which uses the Design of Experiment (DOE) principles to generate parameters depending on the surface roughness needed in the manufacturing environment. Thus in case of mass production, time for inspection could be reduced and hence the surface recognition system results in better performance.

Measurement of bivariate attributes using a novel statistical model

Reducing process variability is essential to many organisations. According to the pertinent literature, a quality system that utilizes quality techniques to reduce process variability is necessary. Quality programs that respond to measurement precision are central to quality systems, and the most common method of assessing the precision of a measurement system is repeatability and reproducibility (R&R). Few studies have investigated R&R using attribute data. In modern manufacturing environments, automated manufacturing is becoming increasingly common; however, a measurement resolution problem exists in automatic inspection equipment, resulting in clusters and product defects. It is vital to monitor effectively these bivariate quality characteristics. This study presents a novel model for calculating R&R for bivariate attribute data. An alloy manufacturing case is utilized to illustrate the process and potential of the proposed model. Findings can be employed to evaluate and improve measurement systems with bivariate attribute data.

Random fuzzy location-scale family with linear fuzzy location function

Coexistence of fuzziness and the randomness has become an intrinsic character in modern manufacturing environments. Accordingly, it is necessary to introduce a new random fuzzy reliability concept and regression models to facilitate the random and fuzzy uncertainties. In this article, we propose the average chance reliability as a measure of quality index under random fuzzy uncertainty, which can be regarded as a counterpart of conventional reliability under probability theory. Furthermore, we extend the probabilistic location-scale family, which is actually a non-normal regression model, into a random fuzzy location-scale family, which is developed for the facilitation of the covariate modeling under random fuzzy environments. We will particularly focus our development on a random fuzzy location-scale family, in which the location function is a fuzzy regression model with each fuzzy exploratory variable being piecewise linear credibility distributed.

Modeling of Dimensional Errors in Slender Bar Turning Process Using Artificial Neural Networks

Accurate predictive modeling is an essential prerequisite for optimization and control of production in modern manufacturing environments. For slender bar turning operations, dimensional deviation is one of the most important product quality characteristics due to the low stiffness of part. In this study, radial basis function neural network is employed to investigate dimensional errors in slender bar turning. The relationship between cutting parameters and dimensional errors is firstly described by the proposed model. Simulation is provided to investigate the effects of cutting parameters on dimensional errors. Further, real-time predictive model based on radial basis function neural network is developed to perform the dimensional error monitoring during slender bar turning process. Experiments verify that the proposed in-process predictive system has the ability to monitor efficiently dimensional errors within the range that they have been trained.

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.

The significance of reducing setup times/setup costs

Scheduling with setup times or setup costs plays a crucial role in todays modern manufacturing and service environments where reliable products/services are to be delivered on time. Scheduling activities profoundly depend on the times/costs required to prepare the facility for performing the activities. However, the vast majority of existing scheduling literature ignores this fact. We define and emphasize the importance, applications, and benefits of explicitly considering setup times/costs in scheduling research. Moreover, a review of the latest research on scheduling problems with setup times/costs is provided.

Effective Supplier Selection and Management Issues in Modern Manufacturing and Marketing Service Environments

Production lot-sizing techniques are very important inputs into MRP/JIT systems to help explain how quantities are calculated, lead times are offset and order released, and receipts are established in modern manufacturing environments. The purpose of this paper is to examine and evaluate different theories of supplier selection and management with regard to their affect on the supply chain. The first section is dedicated to supplier selection. Multiple versus single supplier selection is examined and models and methodologies for supplier selection are presented. The second section deals with supplier management. Several theories of supplier partnering and arms-length management are chronicled and discussed in light of modern manufacturing environmental and best-of-breed concerns.

Grey Predictive Process Control Charts

It has been stated by Pan (2002) that “modern manufacturing environments where the life cycle of products has decreased rapidly and customized short-run manufacturing process become quite common for achieving custom satisfactions”. In classical statistical control theory, it is assumed that for a stable production process if the product measurements were within control, the intrinsic variability poses no impacts to product quality. In this article, we will investigate a small-sample oriented grey predictive control scheme which is developed in terms of grey differential equation theory. This new quality control methodology does not impose the stability constraint to the production process and easy to be implemented for constant monitoring product quality. As illustrative examples, grey predictive Shewhart chart and CUSUM control chart are discussed. A grey fuzzy predictive control scheme is proposed and investigated as well. Finally, we discuss the usage of grey systematic equation system for process control predictive charts.

On-line surface roughness recognition system using artificial neural networks system in turning operations

In modern manufacturing environments, the quality assurance of machined parts has attracted great attention from manufacturers. The surface roughness of a workpiece is one of the most important factors to consider. The need for developing a surface recognition system that is able to replace stylus-style surface measuring systems has increased to improve the efficiency of production. In this research an on-line surface recognition system was developed based on artificial neural networks (OSRR-ANN) using a sensing technique to monitor the effect of vibration produced by the motions of the cutting tool and workpiece during the cutting process. Different combinations of cutting conditions were conducted to develop an OSRR system for a lathe. In order to determine the direction of the vibration which most significantly affects surface roughness, a triaxial accelerometer was employed. Three directional vibrations which were detected simultaneously by the accelerometer were analyzed using a statistical method. The radial direction vibration was found to be the most significant vibration in turning operations. The accuracy of the developed systems showed that the developed system could predict surface roughness efficiently. The developed system not only proposes a surface recognition system which is alternative to that using a traditional measurement instrument, but also provides an on-line surface recognition system for turning operations.