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Abstract The placement of SMD components is usually performed with Cartesian type robots, a task known as pick-and-place (P&P). Small Selective Compliance Articulated Robot Arm (SCARA) robots are also growing in popularity for this use because of their quick and accurate performance. This paper describes the use of the Lean Robotic Micromanufacturing (LRM) framework applied on a large, 10kg payload, industrial SCARA robot for PCB assembly. The LRM framework guided the precision evaluation of the PCB assembly process and provided a prediction of the placement precision and yield. We experimentally evaluated the repeatability of the system, as well as the resulting collective errors during the assembly. Results confirm that the P&P task can achieve the required assembly tolerance of 200 microns without employing closed-loop visual servoing, therefore considerably decreasing the system complexity and assembly time. 

Abstract Enhancing physical human-robot interaction requires the improvement in the tactile perception of physical touch. Robot skin sensors exhibiting piezoresistive behavior can be used in conjunction with collaborative robots. In past work, fabrication of these tactile arrays was done using cleanroom techniques such as spin coating, photolithography, sputtering, wet and dry etching onto flexible polymers. In this paper, we present an addictive, non-cleanroom improved process of depositing PEDOT: PSS, which is the organic polymer responsible for the piezoresistive phenomenon of the robot skin sensor arrays. This publication details the patterning of the robot skin sensor structures and the adaptation of the inkjet printing technology to the fabrication process. This increases the possibility of scaling the production output while reducing the cleanroom fabrication cost and time from an approximately five-hour PEDOT: PSS deposition process to five minutes. Furthermore, the testing of these skin sensor arrays is carried out on a testing station equipped with a force plunger and an integrated circuit designed to provide perception feedback on various force load profiles controlled in an automated process. The results show uniform deposition of the PEDOT: PSS, consistent resistance measurement, and appropriate tactile response across an array of 16 sensors. 

Abstract Direct write Inkjet Printing is a versatile additive manufacturing technology that allows for the fabrication of multiscale structures with dimensions spanning from nano to cm scale. This is made possible due to the development of novel dispensing tools, enabling controlled and precise deposition of fluid with a wide range of viscosities (1 – 50 000 mPas) in nanoliter volumes. As a result, Inkjet printing has been recognized as a potential low-cost alternative for several established manufacturing methods, including cleanroom fabrication. In this paper, we present a characterization study of PEDOT: PSS polymer ink deposition printing process realized with the help of an automated, custom Direct Write Inkjet system. PEDOT: PSS is a highly conductive ink that possesses good film forming capabilities. Applications thus include printing thin films on flexible substrates for tactile (touch) sensors. We applied the Taguchi Design of Experiment (DOE) method to produce the optimal set of PEDOT:PSS ink dispensing parameters, to study their influence on the resulting ink droplet diameter. We experimentally determined that the desired outcome of a printed thin film with minimum thickness is directly related to 1) the minimum volume of dispensed fluid and 2) the presence of a preprocessing step, namely air plasma treatment of the Kapton substrate. Results show that an ink deposit with a minimum diameter of 482 μm, and a thin film with approximately 300 nm thickness were produced with good repeatability. 

Abstract Robot-assisted healthcare could help alleviate the shortage of nursing staff in hospitals and is a potential solution to assist with safe patient handling and mobility. In an attempt to off-load some of the physically-demanding tasks and automate mundane duties of overburdened nurses, we have developed the Adaptive Robotic Nursing Assistant (ARNA), which is a custom-built omnidirectional mobile platform with a 6-DoF robotic manipulator and a force sensitive walking handlebar. In this paper, we present a robot-specific neuroadaptive controller (NAC) for ARNA’s mobile base that employs online learning to estimate the robot’s unknown dynamic model and nonlinearities. This control scheme relies on an inner-loop torque controller and features convergence with Lyapunov stability guarantees. The NAC forces the robot to emulate a mechanical system with prescribed admittance characteristics during patient walking exercises and bed moving tasks. The proposed admittance controller is implemented on a model of the robot in a Gazebo-ROS simulation environment, and its effectiveness is investigated in terms of online learning of robot dynamics as well as sensitivity to payload variations. 

Abstract This paper describes the fabrication of cicada-wing-inspired antimicrobial surfaces using Glancing Angle Deposition (GLAD). From the study of an annual cicada ( Neotibicen Canicularis , also known as dog-day cicada) in North America, it is found that the cicada wing surfaces are composed of unique three-dimensional (3D) nanofeature arrays, which grant them extraordinary properties including antimicrobial (antifouling) and antireflective. However, the morphology of these 3D nanostructures imposes challenges in artificially synthesizing the structures by utilizing and scaling up the template area from nature. From the perspective of circumventing the difficulties of creating 3D nanofeature arrays with top-down nanofabrication techniques, this paper introduces a nanofabrication process that combines bottom-up steps: self-assembled nanospheres are used as the bases of the features, while sub-100 nm pillars are grown on top of the bases by GLAD. Scanning electron micrographs show the resemblance of the synthesized cicada wing mimicry samples to the actual cicada wings, both quantitatively and qualitatively. The synthetic mimicry samples are hydrophobic with a water contact angle of 125˚. Finally, the antimicrobial properties of the mimicries are validated by showing flat growth curves of Escherichia coli ( E. coli ) and by direct observation under scanning electron microscopy (SEM). The process is potentially suitable for large-area antimicrobial applications in food and biomedical industries. 

Multi-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to build the ABC classes. However, existing ABC classification methods operate as black-box AI processes that only provide assignments of the items to the different ABC classes without providing further managerial explanations. The multi-criteria nature of the inventory classification problem makes the utilization and the interpretation of item classes difficult, without further information. Decision makers usually need additional information regarding important characteristics that were crucial in determining the managerial classes of the items because such information can help managers better understand the inventory groups and make inventory management decisions more transparent. To address this issue, we propose a two-phased explainable approach based on eXplainable Artificial Intelligence (XAI) capabilities. The proposed approach provides both local and global explanations of the built ABC classes at the item and class levels, respectively. Application of the proposed approach in inventory classification of a firm, specialized in retail sales, demonstrated its effectiveness in generating accurate and interpretable ABC classes. Assignments of the items to the different ABC classes were well-explained based on the item’s criteria. The results in this particular application have shown a significant impact of the sales, profit, and customer priority as criteria that had an impact on determining the item classes.