Search results for: intelligent manufacturing

Authors: M. Pappa, C. Efstathiou, G. Livanos, P. Xidas, D. Vakondios, E. Maravelakis, M. Zervakis, A. Antoniadis

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The choice of cutting tools in manufacturing processes is an essential parameter on which the required manufacturing time, the consumed energy and the cost effort all depend. If the number of tool changing times could be minimized or even eliminated by using a single convex tool providing multiple profiles, then a significant benefit of time and energy saving, as well as tool cost, would be achieved. A typical machine contains a variety of tools in order to deal with different curvatures and material removal rates. In order to minimize the required cutting tool changes, Actively Deformable micro-Cutters (ADmC) will be developed. The design of the Actively Deformable micro-Cutters will be based on the same cutting technique and mounting method as that in typical cutters.

Keywords: deformable cutters, cutting tool, milling, turning, manufacturing

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Authors: Luís Rocha, Adam Gąska, Enrico Savio, Michael Marxer, Christoph Battaglia

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The changes observed in the last years in the field of manufacturing and production engineering, popularly known as "Fourth Industry Revolution", utilizes the achievements in the different areas of computer sciences, introducing new solutions at almost every stage of the production process, just to mention such concepts as mass customization, cloud computing, knowledge-based engineering, virtual reality, rapid prototyping, or virtual models of measuring systems. To effectively speed up the production process and make it more flexible, it is necessary to tighten the bonds connecting individual stages of the production process and to raise the awareness and knowledge of employees of individual sectors about the nature and specificity of work in other stages. It is important to discover and develop a suitable education method adapted to the specificities of each stage of the production process, becoming an extremely crucial issue to exploit the potential of the fourth industrial revolution properly. Because of it, the project “Train4Dim” (T4D) intends to develop complex training material for digital manufacturing, including content for design, manufacturing, and quality control, with a focus on coordinate metrology and portable measuring systems. In this paper, the authors present an approach to using an active learning methodology for digital manufacturing. T4D main objective is to develop a multi-degree (apprenticeship up to master’s degree studies) and educational approach that can be adapted to different teaching levels. It’s also described the process of creating the underneath methodology. The paper will share the steps to achieve the aims of the project (training model for digital manufacturing): 1) surveying the stakeholders, 2) Defining the learning aims, 3) producing all contents and curriculum, 4) training for tutors, and 5) Pilot courses test and improvements.

Keywords: learning, Industry 4.0, active learning, digital manufacturing

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Authors: Kalyan Deepak Kolla, Ketan Paua, Rajkumar Bhagate

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Transmission dynamics occupy major role in customer perception of the product in both senses of touch and quality of sound. The quantity and quality of sound perceived is more concerned with the whine noise of the gears engaged. Whine noise is tonal in nature and tonal noises cause fatigue and irritation to customers, which in turn affect the quality of the product. Transmission error is the usual suspect for whine noise, which can be caused due to misalignments, tolerances, manufacturing variabilities. In-cabin noise is also more sensitive to the gear design. As the details of the gear tooth design and manufacturing are in microns, anything out of the tolerance zone, either in design or manufacturing, will cause a whine noise. This will also cause high variation in stress and deformation due to change in the load and leads to the fatigue failure of the gears. Hence gear design and development take priority in the transmission development process. This paper aims to study such variability by considering five pairs of helical spur gears and their effect on the transmission error, contact pattern and vibration level on the transmission.

Keywords: gears, whine noise, manufacturing variability, mount vibration variability

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Authors: Amauri Josafat Gomez Aguilar, Jean Pierre Kenné

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This research presents the development of a self-sustainable manufacturing system from a circular economy perspective, structured by a multi-stage serial production system consisting of a series of machines under deterioration in charge of producing a single product and a reverse remanufacturing system constituted by the same productive systems of the first scheme and different tooling, fed by-products collected at the end of their life cycle, and non-conforming elements of the first productive scheme. Since the advanced production manufacturing system is unable to satisfy the customer's quality expectations completely, we propose the development of a mixed integer linear mathematical model focused on the optimal search and assignment of quality stations and preventive maintenance operation to the machines over a time horizon, intending to segregate the correct number of non-conforming parts for reuse in the remanufacturing system and thereby minimizing production, quality, maintenance, and customer non-conformance penalties. Numerical experiments are performed to analyze the solutions found by the model under different scenarios. The results showed that the correct implementation of a closed manufacturing system and allocation of quality inspection and preventive maintenance operations generate better levels of customer satisfaction and an efficient manufacturing system.

Keywords: closed loop, mixed integer linear programming, preventive maintenance, quality inspection

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Authors: Sastry Y. Kandukuri, Isak Andersen

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Despite recent advances in the manufacturing sector, quality issues remain a frequent occurrence, and can result in fatal accidents, equipment downtime, and loss of life. Adequate quality is of high importance in high-risk industries such as sea-going vessels and offshore installations in which third party quality assurance and product control play an important essential role in ensuring manufacturing quality of critical components. Classification societies play a vital role in mitigating risk in these industries by making sure that all the stakeholders i.e. manufacturers, builders, and end users are provided with adequate rules and standards that effectively ensures components produced at a high level of quality based on the area of application and risk of its failure. Quality issues have also been linked to the lack of competence or negligence of stakeholders in supply value chain. However, continued actions and regulatory reforms through modernization of rules and requirements has provided additional tools for purchasers and manufacturers to confront these issues. Included among these tools are updated ‘approval of manufacturer class programs’ aimed at developing and implementing a set of standardized manufacturing quality metrics for use by the manufacturer and verified by the classification society. The establishment and collection of manufacturing and testing requirements described in these programs could provide various stakeholders – from industry to vessel owners – with greater insight into the state of quality at a given manufacturing facility, and allow stakeholders to anticipate better and address quality issues while simultaneously reducing unnecessary failures that are costly to the industry. The publication introduces, explains and discusses critical manufacturing and testing requirements set in a leading class society’s approval of manufacturer regime and its rationale and some case studies.

Keywords: classification society, manufacturing, materials processing, materials testing, quality control

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Authors: Raed El-Khalil, Hussein Zeaiter

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Managing and improving efficiency in the current highly competitive global automotive industry demands that companies adopt leaner and more flexible systems. During the past 20 years the domestic automotive industry in North America has been focusing on establishing new management strategies in order to meet market demands. 98The lean management process also known as Toyota Manufacturing Process (TPS) or lean manufacturing encompasses tools and techniques that were established in order to provide the best quality product with the fastest lead time at the lowest cost. The following paper presents a study that focused on improving labor efficiency at one of the Big Three (Ford, GM, Chrysler LLC) domestic automotive facility in North America. The objective of the study was to utilize several lean management tools in order to optimize the efficiency and utilization levels at the “Pre-Marriage” chassis area in a truck manufacturing and assembly facility. Utilizing three different lean tools (i.e. Standardization of work, 7 Wastes, and 5S) this research was able to improve efficiency by 51%, utilization by 246%, and reduce operations by 14%. The return on investment calculated based on the improvements made was 284%.

Keywords: lean manufacturing, standardized work, operation efficiency, utilization

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Authors: A. Boumedine, K. Benfriha, S. Lecheb

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Manufacturing methods for products and structures made of composite materials reduce the number of parts and integrate technical functions, this advantage of composite materials leads to a lot of innovation but also to a reduction of costs and a gain in quality. A material has attributes: its density, it’s resistance, it’s cost, it’s resistance to corrosion. For the design of a product, a certain profile of these attributes is required: low density, resistance removed, low cost. The problem is then to identify this attribute profile and to compare it with those of the materials, in order to find the one that comes closest. The aim of this work is to demonstrate the feasibility of characterizing a mini turbine made of 3D printed fiber-filled composite material by the process of additive manufacturing, then compare the performance of the alloy turbine with the composite turbine according to the results of the simulation by Abaqus software.

Keywords: additive manufacturing, composite materials, design, 3D printer, turbine

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Chih-Jer Lin, Jian-Hong Hou

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Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

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Authors: Jovian Cheung, Thomas Kwok, Victor Wong

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Buildings are entwined with smart city developments. Building performance relies heavily on electrical and mechanical (E&M) systems and services accounting for about 40 percent of global energy use. By cohering the advancement of technology as well as energy and operation-efficient initiatives into the buildings, people are enabled to raise building performance and enhance the sustainability of the built environment in their daily lives. Digital transformation in the buildings is the profound development of the city to leverage the changes and opportunities of digital technologies To optimize the building performance, intelligent power quality and energy management system is developed for transforming data into actions. The system is formed by interfacing and integrating legacy metering and internet of things technologies in the building and applying big data techniques. It provides operation and energy profile and actionable insights of a building, which enables to optimize the building performance through raising people awareness on E&M services and energy consumption, predicting the operation of E&M systems, benchmarking the building performance, and prioritizing assets and energy management opportunities. The intelligent power quality and energy management system comprises four elements, namely the Integrated Building Performance Map, Building Performance Dashboard, Power Quality Analysis, and Energy Performance Analysis. It provides predictive operation sequence of E&M systems response to the built environment and building activities. The system collects the live operating conditions of E&M systems over time to identify abnormal system performance, predict failure trends and alert users before anticipating system failure. The actionable insights collected can also be used for system design enhancement in future. This paper will illustrate how intelligent power quality and energy management system provides operation and energy profile to optimize the building performance and actionable insights to revitalize an existing building into a smart building. The system is driving building performance optimization and supporting in developing Hong Kong into a suitable smart city to be admired.

Keywords: intelligent buildings, internet of things technologies, big data analytics, predictive operation and maintenance, building performance

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Authors: Rohana Abdullah, Md Nizam Abd Rahman, Seri Rahayu Kamat

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Operator efficiency aspect is gaining importance in ensuring optimized usage of resources especially in the semi-automated manufacturing environment. This paper addresses a case study done to solve operator efficiency and line balancing issue at a semiconductor assembly and test manufacturing. A Man-to-Machine (M2M) work study technique is used to study operator current utilization and determine the optimum allocation of the operators to the machines. Critical factors such as operator activity, activity frequency and operator competency level are considered to gain insight on the parameters that affects the operator utilization. Equipment standard time and overall equipment efficiency (OEE) information are also gathered and analyzed to achieve a balanced and optimized production.

Keywords: operator efficiency, optimized production, line balancing, industrial and manufacturing engineering

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Authors: Seyedebrahim Afkhami, Meisam Abdi, Reza Baserinia

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Additive manufacturing is revolutionising production in different industries, including pharmaceuticals. This case study explores the influence of surface area on the dissolution of additively manufactured polyvinyl alcohol parts as a polymer candidate. Specimens of different geometries and constant mass were fabricated using a Fused Deposition Modelling 3D printer. The dissolution behaviour of these samples was compared with respect to their surface area. Improved and accelerated dissolution was observed for samples with a larger surface area. This study highlights the capabilities of additive manufacturing to produce samples of complex geometries that cannot be manufactured otherwise to control the dissolution behaviour for pharmaceutical and biopharmaceutical applications.

Keywords: additive manufacturing, polymer dissolution, fused deposition modelling, geometry optimization

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Authors: Diana Flores Galindo, Richard Gil Herrera

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Nowadays, Lean Manufacturing and Industry 4.0 are very important in every country. One of the main benefits is continued market presence. It has been identified that there is a need to change existing educational programs, as well as update the knowledge and skills of existing employees. It should be borne in mind that behind each technological improvement, there is a human being. Human talent cannot be neglected. The main objectives of this article are to review the link between Lean Manufacturing, the incorporation of Industry 4.0 and the steps to follow to implement it; analyze the current situation and study the implications and benefits of this new trend, with a particular focus on Mexico. Lean Manufacturing and Industry 4.0 implementation waves must always take care of the most important capital – intellectual capital. The methodology used in this article comprised the following steps: reviewing the reality of the fourth industrial revolution, reviewing employees’ skills on the journey to become world-class, and analyzing the situation in Mexico. Lean Manufacturing and Industry 4.0 were studied not as exclusive concepts, but as complementary ones. The methodological framework used is focused on motivating companies’ collaborators to guarantee common results, innovate, and remain in the market in the face of new requirements from company stakeholders. The key findings were that both trends emphasize the need to improve communication across the entire company and incorporate new technologies into everyday work, from the shop floor to administrative staff, to help improve processes. Taking care of people, activities and processes will bring a company success. In the specific case of Mexico, companies in all sectors need to be aware of and implement technological improvements according to their specific needs. Low-cost labor represents one of the most typical barriers. In conclusion, companies must build a roadmap according to their strategy and needs to achieve their short, medium- and long-term goals.

Keywords: lean management, lean manufacturing, industry 4.0, motivation, SWOT analysis, Hoshin Kanri

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Annie Ramanbhai Mecwan

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Cleaning validation in a pharmaceutical manufacturing facility is documented evidence that a cleaning process has effectively removed contaminants, residues from previous drug products and cleaning agents below a pre-defined threshold from the reusable tools and parts of equipment. In shared manufacturing facilities more than one drug product is prepared. After cleaning of reusable tools and parts of equipment after one drug product manufacturing, there are chances that some residues of drug substance from previously manufactured drug products may be retained on the equipment and can carried forward to the next drug product and thus cause cross-contamination. Health-based limits through the derivation of a safe threshold value called permitted daily exposure (PDE) for the residues of drug substances should be employed to identify the risks posed at these manufacturing facilities. The PDE represents a substance-specific dose that is unlikely to cause an adverse effect if an individual is exposed to or below this dose every day for a lifetime. There are different practices to calculate PDE. Data for all APIs in the public domain are considered to calculate PDE value though, company to company may vary the final PDE value based on different toxicologist’s perspective or their subjective evaluation. Hence, Regulatory agencies should take responsibility for publishing PDE values for all APIs as it is done for elemental PDEs. This will harmonize the PDE values all over the world and prevent the unnecessary load on manufacturers for cleaning validation

Keywords: active pharmaceutical ingredient, good manufacturing practice, NOAEL, no observed adverse effect level, permitted daily exposure

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Authors: Safwat Mohamed, Rasha Metawi, Hadeel Khalil, Hussein Kamal

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This paper discusses and reveals the manufacturing techniques of the leather scale armour of Tutankhamun. This armour was in critical condition and went under many conservation procedures as it suffered from some serious deterioration aspects including fragmentation. In addition, its original shape was lost, the leather scales were found scattered in the box and separated from the linen basis, and hence its outlines were blurred and incomprehensible. In view of this, the leather scale armour of Tutankhamun was desperate for urgent conservation and reconstruction interventions. Documentation measures were done before conservation. Several re-treatable conservation procedures were applied seeking for stabilizing the armour and reaching sustainable condition. The conservation treatments included many investigations and analyses that helped in revealing materials and techniques of making the armour. The leather scale armour of Tutankhamun consisted of leather scales attached to a linen support. This linen support consisted of several layers. Howard Carter assumed that the linen support consisted of 6 layers. The undertaken conservation treatments helped in revealing the actual number of layers of the linen support as well as in reaching the most sustainable condition. This paper views the importance of the conservation procedures, which were recently carried out on Tutankhamun’s leather scale armour, in identifying and revealing all materials and techniques used in its manufacturing. The collected data about manufacturing techniques were used in making a replica of the leather scale armour with the same methods and materials.

Keywords: leather scales armours, conservation, manufacturing techniques, Tutankhamun, producing a replica

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Authors: Maciej Zaręba, Sławomir Lasota

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The monitoring of manufacturing processes is an important issue in nowadays ERP systems. The identification and analysis of appropriate data for the units that take part in the production process are ones of the most crucial problems. In this paper, the authors introduce a new approach towards modelling the relation between production units, signals, and factors possible to obtain from the production system. The main idea for the system is based on the ontology of production units.

Keywords: manufacturing operation management, OWL, ontology implementation, ontology modeling

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Authors: Suraj Gururaj, Sumantha Udupa U.

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Browsing the Web has emerged as the de facto activity performed on the Internet. Although browsing gets tracked, the manageability aspect of Web browsing history is very poor. In this paper, we have a workable solution implemented by using machine learning and natural language processing techniques for efficient manageability of user’s browsing history. The significance of adding such a capability to a Web browser is that it ensures efficient and quick information retrieval from browsing history, which currently is very challenging. Our solution guarantees that any important websites visited in the past can be easily accessible because of the intelligent and automatic classification. In a nutshell, our solution-based paper provides an implementation as a browser extension by intelligently classifying the browsing history into most relevant category automatically without any user’s intervention. This guarantees no information is lost and increases productivity by saving time spent revisiting websites that were of much importance.

Keywords: adhoc retrieval, Chrome extension, supervised learning, tile, Web personalization

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Authors: Shipra Sharma, Jagat Jerath

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Human resource training is an essential element of talent management in the current era of global competitiveness and dynamic trade in the manufacturing industry. Globally, India is behind only China as the largest textile manufacturer. The major challenges faced by the Indian textile manufacturing Industry are low technology levels, growing skill gaps, unorganized structure, lower efficiencies, etc. indicating the need for constant talent up-gradation. Assessment of training needs from a strategic perspective is an essential step for the formulation of effective training. The paper established the significance of training in the Indian textile industry and to determine the training needs on various parameters as presented. 40 HR personnel/s working in the textile and apparel companies based in the industrial region of Punjab, India, were the respondents for the study. The research tool used in this case was a structured questionnaire as per five-point Likert scale. Statistical analysis through descriptive statistics and chi-square test indicated the increased need for training whenever there were technical changes in the organizations. As per the data presented in this study, most of the HR personnel/s agreed that the variables associated with organizational analysis, task analysis, and individual analysis have a statistically significant role to play in determining the need for training in an organization.

Keywords: Indian textile manufacturing industry, significance of training, training needs analysis, parameters for training needs assessment

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Authors: Veerpal Kaur

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The present paper aims at investigating the role of dynamic externalities of agglomeration in the regional productivity growth of manufacturing sector in India and China. Taking 2-digit level manufacturing sector data of states and provinces of India and China respectively for the period of 1998-99 to 2011-12, this paper examines the effect of dynamic externalities namely – Marshall-Arrow-Romer (MAR) specialization externalities, Jacobs’s diversity externalities, and Porter’s competition externalities on regional total factor productivity growth (TFPG) of manufacturing sector in both economies. Regressions have been carried on pooled data for all 2-digit manufacturing industries for India and China separately. The estimation of Panel has been based on a fixed effect by sector model. The results of econometric exercise show that labour-intensive industries in Indian regional manufacturing benefit from diversity externalities and capital intensive industries gain more from specialization in terms of TFPG. In China, diversity externalities and competition externalities hold better prospectus for regional TFPG in both labour intensive and capital intensive industries. But if we look at results for coastal and non-coastal region separately, specialization tends to assert a positive effect on TFPG in coastal regions whereas it has a negative effect on TFPG of coastal regions. Competition externalities put a negative effect on TFPG of non-coastal regions whereas it has a positive effect on TFPG of coastal regions. Diversity externalities made a positive contribution to TFPG in both coastal and non-coastal regions. So the results of the study postulate that the importance of dynamic externalities should not be examined by pooling all industries and all regions together. This could hold differential implications for region specific and industry-specific policy formulation. Other important variables explaining regional level TFPG in both India and China have been the availability of infrastructure, level of competitiveness, foreign direct investment, exports and geographical location of the region (especially in China).

Keywords: China, dynamic externalities, India, manufacturing, productivity

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Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

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Authors: Wahab Ali, Oyebade K. Oyedotun, Adnan Khashman

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Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer.

Keywords: breast cancer, intelligent classification, neural networks, mammography

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Authors: Axel Thallemer, Martin Danzer, Dominik Diensthuber, Aleksandar Kostadinov, Bernhard Rogler

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Nature has always inspired the creative mind, to a lesser or greater extent. Introduced around the 1950s, Biomimetics served as a systematic method to treat the natural world as a ‘pattern book’ for technical solutions with the aim to create innovative products. Unfortunately, this technique is prone to failure when performed as a mere reverse engineering of a natural system or appearance. Contrary to that, a solution which looks at the principles of a natural design, promises a better outcome. One such example is the here presented case study, which shows the design process of three distinctive grippers. The devices have biomimetic properties on two levels. Firstly, they use a kinematic chain found in beaks and secondly, they have a biomimetic structural geometry, which was realized using additive manufacturing. In a next step, the manufacturing method was evaluated to estimate its efficiency for commercial production. The results show that the fabrication procedure is still in its early stage and thus it is not able to guarantee satisfactory results. To summarize the study, we claim that a novel solution can be derived using principles from nature, however, for the solution to be actualized successfully, there are parameters which are beyond reach for designers. Nonetheless, industrial designers can contribute to product innovation using biomimetics.

Keywords: biomimetics, innovation, design process, additive manufacturing

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: T. Häfele, J. Kaspar, M. Vielhaber, W. Calles, J. Griebsch

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The demand for an increasing diversification of the product spectrum associated with the current huge customization desire and subsequently the decreasing unit quantities of each production lot is gaining more and more importance within a great variety of industrial branches, e.g. automotive industry. Nevertheless, traditional product development and production processes (molding, extrusion) are already reaching their limits or fail to address these trends of a flexible and digitized production in view of a product variability up to lot size one. Thus, upcoming innovative production concepts like the additive manufacturing technology basically create new opportunities with regard to extensive potentials in product development (constructive optimization) and manufacturing (economic individualization), but mostly suffer from insufficient strength regarding structural components. Therefore, this contribution presents an innovative technological and procedural conception of a hybrid additive manufacturing process (fiber-reinforced sandwich structures based on selective laser sintering technology) to overcome these current structural weaknesses, and consequently support the design of complex lightweight components.

Keywords: additive manufacturing, fiber-reinforced plastics (FRP), hybrid design, lightweight design

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Authors: Yuan-Jye Tseng, Yi-Shiuan Chen

Abstract:

In this paper, a new concept of closed-loop design model is presented. The closed-loop design model is developed by integrating forward design and reverse design. Based on this new concept, a closed-loop design model for sustainable manufacturing by integrated evaluation of forward design, reverse design, and green manufacturing using a fuzzy analytic network process is developed. In the design stage of a product, with a given product requirement and objective, there can be different ways to design the detailed components and specifications. Therefore, there can be different design cases to achieve the same product requirement and objective. Thus, in the design evaluation stage, it is required to analyze and evaluate the different design cases. The purpose of this research is to develop a model for evaluating the design cases by integrated evaluation of forward design, reverse design, and green manufacturing models. A fuzzy analytic network process model is presented for integrated evaluation of the criteria in the three models. The comparison matrices for evaluating the criteria in the three groups are established. The total relational values among the three groups represent the total relational effects. In application, a super matrix can be created and the total relational values can be used to evaluate the design cases for decision-making to select the final design case. An example product is demonstrated in this presentation. It shows that the model is useful for integrated evaluation of forward design, reverse design, and green manufacturing to achieve a closed-loop design for sustainable manufacturing objective.

Keywords: design evaluation, forward design, reverse design, closed-loop design, supply chain management, closed-loop supply chain, fuzzy analytic network process

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Authors: Ilham Benali, Nasser Hajji, Nawfal Acha

Abstract:

Given the fact that the pharmaceutical industry is a commonly studied sector in the context of innovation, the majority of innovation research is devoted to the developed markets known by high research and development (R&D) assets and intensive innovation. In contrast, in developing countries where R&D assets are very low, there is relatively little research to mention in the area of pharmaceutical sector innovation, characterized mainly by two principal elements which are the presence of foreign-owned firms and licensed manufacturing agreements between local firms and multinationals. With the scarcity of research in this field, this paper attempts to study the effect of these two elements on the firms’ innovation tendencies. Other traditional factors that influence innovation, which are the age and the size of the firm, the R&D activities and the market structure, revealed in the literature review, will be included in the study in order to try to make this work more exhaustive. The study starts by examining innovation tendency in pharmaceutical firms located in developing countries before analyzing the effect of foreign-owned firms and licensed manufacturing agreements between local firms and multinationals on technological, organizational and marketing innovation. Based on the related work and on the theoretical framework developed, there is a probability that foreign-owned firms and licensed manufacturing agreements between local firms and multinationals have a negative influence on technological innovation. The opposite effect is possible in the case of organizational and marketing innovation.

Keywords: developing countries, foreign owned firms, innovation, licensed manufacturing agreements, pharmaceutical industry

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Authors: Dawei Xia, Benyan Jiang, Yong Li

Abstract:

Split-type air conditioners is widely used for indoor temperature regulation in urban residential buildings in summer in China. The energy-related occupant behavior has a great impact on building energy consumption. Obtaining the energy-related occupant behavior data of air conditioners is the research basis for the energy consumption prediction and simulation. Relying on the development of sensing and control technology, this paper selects Zigbee intelligent electronic equipment to monitor the energy-related occupant behavior of 20 households for 3 months in summer. Through analysis of data, it is found that people of different ages in the region have significant difference in the time, duration, frequency, and energy consumption of air conditioners, and form a data model of three basic energy-related occupant behavior patterns to provide an accurate simulation of energy.

Keywords: occupant behavior, Zigbee, split air conditioner, energy simulation

Procedia PDF Downloads 172