Abstract: Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.
Abstract: The rapid progress of technology in today's competitive conditions has also accelerated companies' technology development activities. As a result, companies are paying more attention to R&D studies and are beginning to allocate a larger share to R&D projects. A more systematic, comprehensive, target-oriented implementation of R&D studies is crucial for the company to achieve successful results. As a consequence, Technology Roadmap (TRM) is gaining importance as a management tool. It has critical prospects for achieving medium and long term success as it contains decisions about past business, future plans, technological infrastructure. When studies on TRM are examined, projects to be placed on the roadmap are selected by many different methods. Generally preferred methods are based on multi-criteria decision making methods. Management of selected projects becomes an important point after the selection phase of the projects. At this stage, TRM are used. TRM can be created in many different ways so that each institution can prepare its own Technology Roadmap according to their strategic plan. Depending on the intended use, there can be TRM with different layers at different sizes. In the evaluation phase of the R&D projects and in the creation of the TRM, HAVELSAN, Turkey's largest defense company in the software field, carries out this process with great care and diligence. At the beginning, suggested R&D projects are evaluated by the Technology Management Board (TMB) of HAVELSAN in accordance with the company's resources, objectives, and targets. These projects are presented to the TMB periodically for evaluation within the framework of certain criteria by board members. After the necessary steps have been passed, the approved projects are added to the time-based TRM, which is composed of four layers as market, product, project and technology. The use of a four-layered roadmap provides a clearer understanding and visualization of company strategy and objectives. This study demonstrates the benefits of using TRM, four-layered Technology Roadmapping and the possibilities for the institutions in the defense industry.
Abstract: A novel Active Flap System (AFS) has been developed
at DTU Wind Energy, as a result of a 3-year R&D project following
almost 10 years of innovative research in this field. The full scale AFS
comprises an active deformable trailing edge has been tested at the
unique rotating test facility at the Risø Campus of DTU Wind Energy
in Denmark. The design and instrumentation of the wing section and
the AFS are described. The general description and objectives of the
rotating test rig at the Risø campus of DTU are presented, along
with an overview of sensors on the setup and the test cases. The
post-processing of data is discussed and results of steady, flap step
and azimuth control flap cases are presented.
Abstract: The aspiration of this research article is to target and
focus the gains of university-Industry (U-I) collaborations and
exploring those hurdles which are the obstacles for attaining these
gains. University-Industry collaborations have attained great
importance since 1980 in USA due to its application in all fields of
life. U-I collaboration is a bilateral process where academia is a
proactive member to make such alliances. Universities want to
ameliorate their academic-base with the technicalities of technobabbles.
U-I collaboration is becoming an essential lane for achieving
innovative goals in this century. Many developed nations have set
successful examples to prove this phenomenon as a catalyst to reduce
costs, efforts and personnel for R&D projects. This study is exploits
amplitudes of UI collaboration incentives in the light of success
stories of developed countries. Many universities in USA, UK,
Canada and various European Countries have been engaged with
enterprises for numerous collaborative agreements. A long list of
strategic and short term R&D projects has been executed in
developed countries to accomplish their intended purposes. Due to
the lack of intentions, genuine research and research-oriented
environment, the mentioned field could not grow very well in
developing countries. During last decade, a new wave of research
has induced the institutes of developing countries to promote R&D
culture especially in Pakistan. Higher Education Commission (HEC)
has initiated many projects and funding supports for universities
which have collaborative intentions with industry.
Findings show that rapid innovation, overwhelm the technological
complexities and articulated intellectual-base are major incentives
which steer both partners to establish faculty-industry alliances. Everchanging
technologies, concerned about intellectual property,
different research environment and culture, research relevancy (Basic
or applied), exposure differences and diversity of knowledge
(bookish or practical) are main barriers to establish and retain joint
ventures. Findings also concluded that, it is dire need to support and
enhance cooperation among academia and industry to promote highly
coordinated research behaviors. Author has proposed a roadmap for
developing countries to promote R&D clusters among faculty and
industry to deal the technological challenges and innovation
complexities. Based on our research findings, Model for R&D
Collaboration for developing countries also have been proposed to
promote articulated R&D environment. If developing countries
follow this phenomenon, rapid innovations can be achieved with
limited R&D budget heads.
Abstract: Extensive information is required within a R&D environment,
and a considerable amount of time and efforts are being
spent on finding the necessary information. An adaptive information
providing system would be beneficial to the environment, and a
conceptual model of the resources, people and context is mandatory
for developing such applications. In this paper, an information model
on various contexts and resources is proposed which provides the
possibility of effective applications for use in adaptive information
systems within a R&D project and meeting environment.
Abstract: The purpose of this article is to identify the practical strategies of R&D (research and development) entities for developing converging technology in organizational context. Based on the multi-assignation technological domains of patents derived from entire government-supported R&D projects for 13 years, we find that technology convergence is likely to occur when a university solely develops technology or when university develops technology as one of the collaborators. These results reflect the important role of universities in developing converging technology
Abstract: Chemical industry project management involves
complex decision making situations that require discerning abilities
and methods to make sound decisions. Project managers are faced
with decision environments and problems in projects that are
complex. In this work, case study is Research and Development
(R&D) project selection. R&D is an ongoing process for forward
thinking technology-based chemical industries. R&D project
selection is an important task for organizations with R&D project
management. It is a multi-criteria problem which includes both
tangible and intangible factors. The ability to make sound decisions
is very important to success of R&D projects. Multiple-criteria
decision making (MCDM) approaches are major parts of decision
theory and analysis. This paper presents all of MCDM approaches
for use in R&D project selection. It is hoped that this work will
provide a ready reference on MCDM and this will encourage the
application of the MCDM by chemical engineering management.