Nowadays, industrial robot applications are required to customize the manufacturing of diverse products to reduce both downtime and standoff variability. Two methods for robot programming are regularly implemented to carry out that goal. The first one, online programming, requires a specialized operator to guide the robot through desired poses, and the quality of the result is directly limited by his skill level. On the other side, off-line programming uses software packaging to simulate robot applications before their implementation. It reduces downtime with respect to online programming but requires additional calibration steps. In this paper, a novel procedure is presented to obtain accurate surface approximations by combining linear interpolations generated during online programming with a triangulated surface reconstruction of a workpiece surface representation. The method uses a point cloud instead of a predefined mesh to reduce the standoff variability between the robotic tool center point and the surface. Additionally, a technique based on a penalized least squares method was implemented to smooth the trajectory, including position and orientation. The proposed methodology was validated with three well-known case studies involving real trajectories, with simulations in Matlab® and RobotStudio®, as well as by experimentation with an industrial ABB robot. The quality of the results demonstrates a great efficiency of this method for path generation based on surface reconstruction.

Background: 3D-printing is a potential manufacturing process for optimizing the design and manufacture of ankle foot orthosis (AFOs). The feasibility of an AFO with interchangeable strut that is suitable for 3D-printing is created and evaluated. Objective: A segmented AFO with 3D-printed custom footplate and calf shell connected by a custom-made strut is studied. Study design: The duration of a healthy subject wearing the 3D-printed segmented AFO in daily activities is used to evaluate the feasibility and durability to integrate 3D-printed AFOs into orthotics practice. Technique: The 3D-scanning of a patient's leg is first conducted. The scanned 3D surface is modified by creating the clearance around bony prominences and trimlines for the footplate and calf shell. The footplate has a custom-shaped inside to match with the foot and a standard shape outside at the top to match and connect with the strut. For the calf shell, the inside shape is custom fit with the shank and the outside shape is standard to connect with the strut. Material extrusion is the 3D-printing process selected. Tree-like support structures are used to avoid the use of soluble support material and to eliminate the risk of residual chemical solvent in the orthosis. Results: The segmented AFO with material extrusion footplate and calf shell was tested in a healthy subject with an active lifestyle, offering comfort, and stability for over 4 months without breakage. Conclusions: This segmented AFO is durable, requires short 3D-printing time, and enables the quick adjustment of bending stiffness via an interchangeable strut design.

This study presents an edge detection and speckle tracking (EDST) based algorithm to calculate distensibility as percentage of change of vessel diameter during cardiac cycles. Canny edge detector, Vandermonde matrix representation, Kanade Lucas Tomasi algorithm with pyramidal segmentation, and penalized least squares technique identifies the vessel lumen edge, track the vessel diameter, detrend the signal and find peaks and valleys when the vessel is fully distended or contracted. An upper extremity artery from 10 patients underwent an ultrasound examination as part of preoperative evaluation before arteriovenous fistula surgery. Three studies were performed to evaluate EDST with automatic peak and valley selection versus manual speckle selection of expert users using manual peak and valley selection. Results demonstrate the effectiveness of the proposed methodology, to obtain comparable results as those obtained by expert-users, and considerably reducing the variability associated with external factors such as excessive motion, fluctuations in stroke volume, beat-to-beat blood pressure changes, breathing cycles, and arm-transducer pressure.

Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic end-effector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton’s method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.

This study presents a novel algorithm implementation that optimizes manually recorded toolpaths with the use of a 3D-workpiece model to reduce manual error induced. The novel algorithm has three steps: workpiece declaration, manual toolpath declaration, and toolpath optimization using steepest descent algorithm. Steepest descent finds the surface route wherein the manually recorded toolpaths traverse over a 3D-workpiece surface. The optimized toolpaths were simulated and tested with an industrial robot showing minimal error compared to the desired optimized toolpaths. The results obtained from the presented implementation on three different trajectories demonstrate that the proposed methodology can reduce the manual error induced using as a reference the CAD-workpiece surface.

Operation Sequencing (OS) is one of the most critical tasks in a CAPP system. This process could be modelled as a combinatorial problem where finding a suitable solution within a reasonable time interval is difficult. This work implements a novel Discrete Differential Evolution Algorithm (DDEA) to solve the OS problem, focusing on parts of up to 76 machining operations; the relationships among operations are represented as a directed graph; the contributions of the DDEA are as follows: (1) operates with a discrete representation in the space of feasible solutions; (2) employs mutation and crossover operators to update solutions and to reduce machining and setup costs, (3) possess a local search strategy to achieve better solutions, and (4) integrates a statistical method based on quantiles to measure the quality and likelihood for an achieving a solution. To demonstrate the efficiency and robustness of the DDEA, five prismatic parts with different numbers of machining operations as benchmarks to address the OS problem were selected. The results generated the same OS for parts with a few machining operations (up to 23 machining operations). Conversely, for parts with more machining operations, the DDEA needs more runs to achieve the best solution.