Stefan Wellsandta, Z. Rusák, Santiago Ruiz Arenas, D. Aschenbrenner, K. Hribernik, K. Thoben
Voice-enabled assistants, such as Alexa and Google Assistant, are among the fastest-growing information technology applications. Their technological foundation matured over the last years and reached a point where new application areas in challenging business environments become a certainty. Maintenance in manufacturing is one of these areas. This paper presents expectations, requirements, and a concept for a voice-enabled digital intelligent assistant that supports maintenance activities. We identified process monitoring, task execution, reporting, problem-solving, and maintenance planning as the key functional modules for an assistant. Realizing them depends on basic, utility, and maintenance functions. Our discussion states that all fundamental technologies and tools to realize an assistant for maintenance exist, but they have constraints. For instance, Speech-to-Text mechanisms lack transparent and performant solutions, and natural language understanding must rely on small datasets, which is challenging. We argue that continuous improvement and systematic evaluation of an assistant prototype is important to create high-quality training data. Trial-and-error is common because some technologies still mature, and conversation designers lack design patterns for the maintenance domain. Challenges for system adoption include providing an outstanding user experience, handling factory-specific jargon, and the limited availability of easy-to-use data exchange interfaces for machines and business applications. We conclude that further efforts on interoperability, technology stack management, AI-focused change management, and education programs are necessary. Furthermore, the accountability of AI systems is a cost factor for the assistant’s service providers and the client companies in manufacturing – AI insurance services, human-in-the-loop functions, user training, and professional education are actions to address this issue.
{"title":"Concept of a Voice-Enabled Digital Assistant for Predictive Maintenance in Manufacturing","authors":"Stefan Wellsandta, Z. Rusák, Santiago Ruiz Arenas, D. Aschenbrenner, K. Hribernik, K. Thoben","doi":"10.2139/ssrn.3718008","DOIUrl":"https://doi.org/10.2139/ssrn.3718008","url":null,"abstract":"Voice-enabled assistants, such as Alexa and Google Assistant, are among the fastest-growing information technology applications. Their technological foundation matured over the last years and reached a point where new application areas in challenging business environments become a certainty. Maintenance in manufacturing is one of these areas. This paper presents expectations, requirements, and a concept for a voice-enabled digital intelligent assistant that supports maintenance activities. We identified process monitoring, task execution, reporting, problem-solving, and maintenance planning as the key functional modules for an assistant. Realizing them depends on basic, utility, and maintenance functions. Our discussion states that all fundamental technologies and tools to realize an assistant for maintenance exist, but they have constraints. For instance, Speech-to-Text mechanisms lack transparent and performant solutions, and natural language understanding must rely on small datasets, which is challenging. We argue that continuous improvement and systematic evaluation of an assistant prototype is important to create high-quality training data. Trial-and-error is common because some technologies still mature, and conversation designers lack design patterns for the maintenance domain. Challenges for system adoption include providing an outstanding user experience, handling factory-specific jargon, and the limited availability of easy-to-use data exchange interfaces for machines and business applications. We conclude that further efforts on interoperability, technology stack management, AI-focused change management, and education programs are necessary. Furthermore, the accountability of AI systems is a cost factor for the assistant’s service providers and the client companies in manufacturing – AI insurance services, human-in-the-loop functions, user training, and professional education are actions to address this issue.","PeriodicalId":359104,"journal":{"name":"TESConf 2020: Full Papers","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114248012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polylactic Acid (PLA) is a biodegradable thermoplastic polymer known for its widescale application in bio-tribology. A known problem with PLA is its high level of water adsorption due to its hydrophilic nature which can lead to growth of mold and other fungal derivatives due to water stagnation. In our investigation we developed a re-entrant pattern using a standard fused-deposition modelling (FDM) 3D printer with the aim to achieve surfaces which are superhydrophobic in nature. The layer heights of the re-entrant profiles were systematically modified while keeping all other printing parameters constant to achieve a multi-edge ladder effect. The samples were then investigated for their superhydrophobic characteristics by measurement of Contact and Tilt Angles and thereafter characterized for wear resistance using a ball-on-disc tribometer. A fixed low rpm setting was used to eliminate the effect of temperature. Wear parameters including Wear Depth and Coefficient of Friction were recorded after each cycle using until the sample’s re-entrant structure was damaged and no longer supported water-repellent behavior shown by a considerable decrease in water contact angle. The effect of layer height variation in the re-entrant profiles for effective wear resistance was established as a preliminary study for our subsequent research in the area. It can be concluded that the layer height plays a vital role in achieving superhydrophobicity and has a direct influence on the wear resistant of the surface.
{"title":"Wear Behavior of 3D Printed PLA Surfaces for Superhydrophobic Interaction","authors":"H. Shams, K. Basit, M. Khan, A. Mansoor","doi":"10.2139/ssrn.3717991","DOIUrl":"https://doi.org/10.2139/ssrn.3717991","url":null,"abstract":"Polylactic Acid (PLA) is a biodegradable thermoplastic polymer known for its widescale application in bio-tribology. A known problem with PLA is its high level of water adsorption due to its hydrophilic nature which can lead to growth of mold and other fungal derivatives due to water stagnation. \u0000 \u0000In our investigation we developed a re-entrant pattern using a standard fused-deposition modelling (FDM) 3D printer with the aim to achieve surfaces which are superhydrophobic in nature. The layer heights of the re-entrant profiles were systematically modified while keeping all other printing parameters constant to achieve a multi-edge ladder effect. The samples were then investigated for their superhydrophobic characteristics by measurement of Contact and Tilt Angles and thereafter characterized for wear resistance using a ball-on-disc tribometer. A fixed low rpm setting was used to eliminate the effect of temperature. Wear parameters including Wear Depth and Coefficient of Friction were recorded after each cycle using until the sample’s re-entrant structure was damaged and no longer supported water-repellent behavior shown by a considerable decrease in water contact angle. \u0000 \u0000The effect of layer height variation in the re-entrant profiles for effective wear resistance was established as a preliminary study for our subsequent research in the area. It can be concluded that the layer height plays a vital role in achieving superhydrophobicity and has a direct influence on the wear resistant of the surface.","PeriodicalId":359104,"journal":{"name":"TESConf 2020: Full Papers","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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