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Adoption of artificial intelligence (AI) has risen sharply in recent years but many firms are not successful in realising the expected benefits or even terminate projects before completion. While there are a number of previous studies that highlight challenges in AI projects, critical factors that lead to project failure are mostly unknown. The aim of this study is therefore to identify distinct factors that are critical for failure of AI projects. To address this, interviews with experts in the field of AI from different industries are conducted and the results are analyzed using qualitative analysis methods. The results show that both, organizational and technological issues can cause project failure. Our study contributes to knowledge by reviewing previously identified challenges in terms of their criticality for project failure based on new empirical data, as well as, by identifying previously unknown factors.
A closed-loop control for a cooperative innovation culture in interorganizational R&D projects
(2022)
Since project managers only have a limited authority in interorganizational R&D projects a cooperative innovation culture is essential for team cohesion and thus for achieving project scope in time and cost. For its development different factors depending on underlying values are essential. These factors must be learned iteratively by the project members so that they are living the values of a cooperative innovation culture. Hence, this paper raises the following research question: “How to control living the values of a cooperative innovation culture in interorganizational R&D projects?” To answer this question, a closed-loop control for a cooperative innovation culture is developed. The developed closed-loop control system includes several different functional units which show essential roles and several different variables which show what to consider and design in the control system. In addition, the developed closed-loop control system is generalized for other types of projects such as intraorganizational projects.
Allyls
(2022)
This chapter addresses the importance and usage of the commercially low-volume thermoset plastics group known as allyls. The three significant subelements of this group are poly(diallylphthalates), poly(diallylisophthalates), and poly(allyldiglycol carbonate). Chemistry, processing, and properties are also described. Allyl polymers are synthesized by radical polymerizations of allyl monomers that usually do not produce high-molecular-mass macromolecules. Therefore only a few specific monomers can produce thermosetting materials. Diallyldiglycolcarbonate (CR-39) and diallylphthalates are the most significant examples that have considerably improved our everyday life.
Unsaturated polyester resins (UPR) and vinyl ester resins (VER) are among the most commercially important thermosetting matrix materials for composites. Although comparatively low cost, their technological performance is suitable for a wide range of applications, such as fiber-reinforced plastics, artificial marble or onyx, polymer concrete, or gel coats. The main areas of UPR consumption include the wind energy, marine, pipe and tank, transportation, and construction industries.
This chapter discusses basic UPR and VER chemistry and technology of manufacturing, and consequent applications. Some important properties and performance characteristics are discussed, such as shrinkage behavior, flame retardance, and property modification by nanoparticles. Also briefly introduced and described are the practical aspects of UPR and VER processing, with special emphasis on the most widely used technological approaches, such as hand and spray layup, resin infusion, resin transfer molding, sheet and bulk molding, pultrusion, winding, and centrifugal casting.
Cross-linked thermoplastics
(2022)
Cross-linked thermoplastics represent an important class of materials for numerous applications such as heat-shrinkable tubing, rotational molded parts, and polyolefin foams. By cross-linking olefins, their mechanical performance can be significantly enhanced. This chapter covers the three main methods for the cross-linking of thermoplastics: radiation cross-linking, chemical cross-linking with organic peroxides, and cross-linking using silane-grafting agents. It also considers the major effects of the cross-linking procedure on the performance of the thermoplastic materials discussed.
Silicones
(2022)
Silicones are found in a variety of applications with requirements that range from long life at elevated temperatures to fluidity at low temperatures. This chapter first considers silicone elastomers and their application in room temperature vulcanizing (RTV) and heat curing systems (HTV). Also, new technologies for UV curing are introduced. Coverage of RTVs includes both one-component and two-component systems and the different cure chemistries of each and is followed by a separate discussion of silicone laminates. Due to the high importance of silicone fluids, they are also discussed. Fluids include polishes, release agents, surfactants, and dielectric fluids.
Cyanate ester resins
(2022)
Cyanate ester resins are an important class of thermosetting compounds that experience an ever-increasing interest as matrix systems for advanced polymer composite materials, which among other application fields are especially suitable for highly demanding applications in the aerospace or microelectronics industries. Other names for cyanate ester resins are cyanate resins, cyanic esters, or triazine resins. The various types of cyanate ester monomers share the –OCN functional group that trimerizes in the course of resin formation to yield a highly branched heterocyclic polymeric network based on the substituted triazine core structure.
Process analysis and process control have attracted increasing interest in recent years. The development and application of process analytical methods are a prerequisite for the knowledge-based manufacturing of industrial goods and allow for the production of high-value products of defined, constantly good quality. Discussed in this chapter are the measurement principle and some relevant aspects and illustrative examples of online monitoring tools as the basis for process control in the manufacturing and processing of thermosetting resins. Optical spectroscopy is featured as one of the main process analytical methods applicable to, among other applications, online monitoring of resin synthesis. In combination with chemometric methods for multivariate data analysis, powerful process models can be generated within the framework of feedback and feed-forward control concepts. Other analytical methods covered in this chapter are those frequently used to control further processing of thermosets to the final parts, including dielectric analysis, ultrasonics, fiber optics, and Fiber Bragg Grating sensors.
Self-healing thermosets
(2022)
This chapter discusses the basic extrinsic, intrinsic, and combined extrinsic/intrinsic strategies for equipping thermosetting polymers with self-healing properties. The main focus will be on the presentation of a holistic optimization of thermosetting materials, that is, on a simultaneous optimization of both self-healing and other, specialized material properties. Due to their very rigid, highly cross-linked three-dimensional structure, thermosetting polymers require special chemical strategies to achieve self-healing properties. The main chemical strategies available for this will be briefly outlined. The examples given illustrate interesting and/or typical procedures and serve as an inspiration to find solutions for your own applications. They summarize important recent development in research and technology aiming toward multifunctional truly smart self-healing thermosetting materials. An important aspect in this topic area is also how precisely the self-healing effects are analytically checked, quantified, and evaluated. A range of measuring methods is available for this purpose. In this chapter, the most important analytical tools for testing self-healing properties are briefly introduced and highlighted with some illustrative examples.
Today, many industrial tasks are not automated and still require human intervention. One of these tasks is the unloading of oversea containers. After the end of transportation to the sorting center, the containers must be unloaded manually for further sending the parcels to the recipients. A robot-based automatic unloading of containers was therefore researched. However, the promising results of the system developed in these projects could not be commercialized due to problems with its reliability. Mechanical, algorithmic or other limitations are possible causes of the observed errors. To analyze errors, it is necessary to evaluate the results of the robot’s work without complicating the existing system by adding new sensors to it. This paper presents a reference system based on machine learning to evaluate the robotics grasps of parcels. It analyzes two states of the container: before and after picking up one box. The states are represented as a point cloud received from a laser scanner. The proposed system evaluates the success of transferring a box from an overseas container to the sorting line by supervised learning using convolutional neural networks (CNN) and manual labeling of the data. The process of obtaining a working model using a hyperband model search with a maximum classification error of 3.9 % is also described.