Application of the Fourth Industrial Revolution for High Volume Production in the Rail Car Industry

Some recent technological advances in line with the fourth industrial revolution (4IR) are rapidly transforming the industrial sector. This work explores the prospect of robotic and additive manufacturing solutions for mass production in the rail industry. It proposes a dual arm, 12-axis welding robot with advance sensors, camera, and algorithm as well as intelligent control system. The computer-aided design (CAD) of the robotic system was done in the Solidworks 2017 environment and simulated using the adaptive neuro-fuzzy interference system (ANFIS) in order to determine the kinematic motion of the robotic arm and the angles of joint. The simulation results showed the smooth motion of the robot and its suitability to carry out the welding operations for mass production of components during rail car manufacturing. In addition, the ability to fabricate several physical models directly from digital data through additive manufacturing (AM) is a key factor to ensuring rapid product development cycle. Given that AM is embedded in a digitally connected environment, flow of information as well as data processing and transmission in real time will be useful for massive turnout during mass production.

[1]  Matthias Klumpp,et al.  Artificial Divide: The New Challenge of Human-Artificial Performance in Logistics , 2017 .

[2]  Christian Diedrich,et al.  Cyber-physical systems alter automation architectures , 2014, Annu. Rev. Control..

[3]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[4]  Borja Bordel,et al.  Cyber-physical systems: Extending pervasive sensing from control theory to the Internet of Things , 2017, Pervasive Mob. Comput..

[5]  K. Osakada,et al.  Rapid Manufacturing of Metal Components by Laser Forming , 2006 .

[6]  Matthias Klumpp,et al.  Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements , 2018 .

[7]  Juliana Parreira,et al.  An interactive simulation model of human drivers to study autonomous haulage trucks , 2011, Complex Adaptive Systems.

[8]  Enzo Morosini Frazzon,et al.  Towards Socio-Cyber-Physical Systems in Production Networks , 2013 .

[9]  Andrej Kosir,et al.  Emotion Elicitation in a Socially Intelligent Service: The Typing Tutor , 2017, Comput..

[10]  Natasha Merat,et al.  Human factors implications of vehicle automation: current understanding and future directions , 2014 .

[11]  A. Sohal,et al.  Planning processes for advanced manufacturing technology by large American manufacturers , 1998 .

[12]  F. Musharavati.  RECONFIGURABLE MANUFACTURING SYSTEMS , 2010 .

[13]  David Z. Zhang,et al.  Additive manufacturing: A framework for implementation , 2014 .

[14]  Nick Bostrom,et al.  Racing to the precipice: a model of artificial intelligence development , 2016, AI & SOCIETY.

[15]  Shuzhu Zhang,et al.  Swarm intelligence applied in green logistics: A literature review , 2015, Eng. Appl. Artif. Intell..

[16]  Angappa Gunasekaran,et al.  Expert systems and artificial intelligence in the 21st century logistics and supply chain management , 2014, Expert Syst. Appl..

[17]  Douglas S. Thomas.  Economics of Additive Manufacturing , 2017 .

[18]  Martin Mikusz,et al.  Towards an Understanding of Cyber-physical Systems as Industrial Software-Product-Service Systems , 2014 .

[19]  H. Proff,et al.  Innovative Produkte und Dienstleistungen in der Mobilität , 2017 .

[20]  Jack M. Walker,et al.  Handbook of manufacturing engineering , 1996 .

[21]  Anton J.M. Schoot Uiterkamp,et al.  A global sustainability perspective on 3D printing technologies , 2014 .

[22]  Eleonora Atzeni,et al.  Economics of additive manufacturing for end-usable metal parts , 2012 .

[23]  Jian Zhang,et al.  Review of job shop scheduling research and its new perspectives under Industry 4.0 , 2017, Journal of Intelligent Manufacturing.

[24]  Benoît Montreuil,et al.  Toward a Physical Internet: meeting the global logistics sustainability grand challenge , 2011, Logist. Res..

[25]  N. Zulkifli,et al.  Effective factors on advanced manufacturing technology implementation performance: a review , 2010 .