In endoscopic surgery, the surgical navigation needs to calculate the internal deformation of the soft tissue by biomechanical model which needs to determine the elastic properties and boundary conditions. However, these information cannot be obtained accurately in a real operation scenario. For example, only a limited portion of a liver surface can be observed in a hepatic surgery under endoscope while its elastic properties remain unknown. In addition, simple boundary conditions such as fixed constraints and free-force constraints are not physically adequate to simulate the elastic effect of ligaments attached to the liver. Biomechanical models of the soft tissue have been thoroughly studied in recent years. In these studies, boundary conditions play an important role in identification of elastic properties for mechanical model based methods. But they rarely combine these unknown conditions together to construct the model, and instead set boundary conditions or elastic properties as known for simplification. In this paper, we present a novel method to identify the Young’s modulus and equivalent spring constraint boundary conditions of a partially observed soft object with incomplete boundary conditions. The spring constraint boundary condition is applied to alternate the conventional displacement boundary conditions (e.g. free constraint and fixed constraint) and an inverse algorithm based on the standard finite element method (FEM) and Gauss-Newton (GN) method is developed, which takes external forces and displacements of observable nodes as inputs. A series of numerical simulation experiments are implemented and the analysis of simulation results show the feasibility of the proposed method.

Needle-tissue interaction model plays an important role in virtual surgery training, pre-intervention planning and intra-intervention guidance. Traditionally, finite element methods (FEM) had been a primary and popular way for simulation modeling. However, FEM, as a mesh-based numerical calculations, is likely to encounter numerical difficulties due to mesh distortion as needle insertion process includes damage, fracture and penetration. In this work, a novel material point method (MPM) based needle-tissue interaction model is proposed, which combines the advantages of Lagrangian and Euler methods. A simplified contact algorithm and friction model are integrated to calculate contact forces as well as the resultant tissue deformations. Both preliminary contact forces and deformation results are compared with test experiments, which show that the maximum and mean displacement errors are 0.9768mm and 0.5134mm, respectively while the mean relative errors of force is 14%. This preliminary result demonstrates that MPM has great potential in needle-tissue interaction modeling.

Needle insertion physical experiments are used as the ground truth for model validation and parameter estimation by measuring the needle defection and tissue deformation during the needle-tissue interactions. Hence parameter uncertainties can contribute experiment errors. To improve the repeatability and accuracy of such experiments, one-at-a-time (OAT) sensitivity analysis is used to study the impacts of the factors, such as stirring temperature, frozen time, thawing time during the process of making hydrogels as well as repeated path insertion and different puncture plane in the planer needle insertion experiments. The results show that the puncture plane has the greatest effect on the repeatability of needle insertion physic experiments, followed by repeated path insertion, while other factors have the least effect. The results serve to guide future experiment design for greater repeatability and accuracy.

For multi-stage manufacturing system, considerable amount of energy may be wasted and energy efficiency may be hindered due to idleness or constraints from the interactions between the machines and buffers. In this paper, a real-time system performance diagnostic method is developed to identify energy waste due to machine and buffer interactions, and system resilience against random disruption events. Furthermore, by utilizing the real-time system diagnostic information, a novel real-time resilient feedback control policy is proposed to improve system energy efficiency and deliver resilient performance against random disruption events. A case study is presented to demonstrate the effectiveness of the proposed control policy.

It is attractive to reduce the total cost of a manufacture system with real-time control of the production. The total cost mainly consists of the production cost, the penalty of the permanent production loss, and the Work-In-Process (WIP) inventory level cost. However, it is difficult to derive an analytical model of manufacture system due to the complexity of starved and blocked phenomena, the random failure and maintenance processes. Therefore, finding a real-time control policy for the manufacture system without exact analytical model is dearly needed. In this paper, a novel reinforcement learning based control decision policy is proposed based on the action of switching the machines on or off at the start of each time slot. Firstly, a simulation model is developed with MTBF and MTTR evaluated from the history data to collect samples. Then, a reinforcement learning method, specifically, Least-Square-Policy-Iteration method, is applied to obtain a sub-optimal policy. The simulation results show that the proposed method performs well in reducing the total cost.

The productivity and efficiency of production systems are greatly influenced by their configuration and complex dynamics subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. In this paper, we develop a mathematical model of production systems with general structure (tandem line, parallel, and etc.) to estimate the status of the system (production counts and processing speeds of the stations, buffer levels and production loss) by using sensor data of disruption events. Real-time production system performance such as effective disruption events, opportunity window, and permanent production loss are identified, which is very useful in real-time control to increase overall system efficiency.

Steerable flexible needles with bevel-tip are designed for many medical diagnoses and treatments. In this paper we present a new kinematic model for the bevel-tip flexible needle. Based on the analysis of needle deflection, the procedure of needle insertion can be decomposed of n sub-procedures, which are independent on the depth of insertion. In each sub-procedure, the tip is steered though the base to generate three motions with respect to the body-frame Ox i y i z i : the rotation about z i , the translation along z i and the rotation about y i . The kinematics of flexible needle with bevel-tip is presented using the Denavit-Hartenberg method. The inverse kinematics of needle insertion is derived from the kinematic equation of flexible needle. The solution sets are discussed for the insertion consisting of consecutive rotation and insertion control actions. Furthermore, how to select suitable insertion position and orientation is discussed.

Timely performance of preventive maintenance (PM) tasks is a critical element of manufacturing systems. The current PM at most manufacturing plants is to conduct maintenance tasks during non-production shifts, breaks, or other scheduled downtime. This practice may introduce unnecessary extra labor and overhead costs. Therefore, there is need to look for hidden maintenance opportunities to perform PM tasks during normal production time without impacting throughput. Additional benefit of the hidden opportunity window is that it can be translated to hidden energy saving opportunity during which machines can be strategically shut down or turned to energy saving mode while PM can be performed. Since production schedules are always made beforehand, the development of opportunity window and the downtime schedule need future prediction about the production system. In this paper, a stochastic model of the downtime opportunity on serial production systems is developed. Based on the stochastic model, revised opportunity window and recovery time is defined, which can help on production control on deciding when and where to insert the downtime events and the duration of the downtime events for the incoming process.

This paper presents a new simulation model for flexible needle insertion into soft tissue using modified local constraint method. In this work, we assume deformation of the needle is relatively small, therefore, the soft tissue deformation in the radial direction is far less than it is in the axial direction. Under the assumptions above, the spring model in the radial direction of the needle is established and applied in the puncture procedure to obtain the equilibrium position of the needle-tissue system. The FEM model for the simulation includes three iterative steps: 1. Distribute the axial force on the needle into tissue nodes, and add radial constraint on tissue nodes that are connected with needle’s shaft, then calculate the whole displacement of the tissue nodes; 2. Based on the whole displacement of all nodal points and the effective stiffness matrix, we get the radial force on the tissue, then we use the spring model in the radial direction to get the equilibrium position; 3. Compound the radial and axial force on the tissue nodal points to obtain the actual displacement. Simulation results are presented and discussed.

Forging is an essential metal-working process in manufacturing. Forging tonnage signals, which are originally used to monitor the structural deformation of the forging machines for safety purpose, had been increasingly used to monitor the performance of the forging processes. Despite recent advances on forging process monitoring, little attention had been paid to expedite the preparation of the data labeling procedure for different fault classes of the forging tonnage signals. Currently, existing data are mostly classified and labeled by experts, which is extremely time consuming and error prone. In this paper, a new forging tonnage signal clustering method is proposed to label the multiple-operation forging tonnage signals automatically. Due to different workload setups of the forging machines, a Finite State Machine (FSM) modeling approach is developed to utilize the domain knowledge of the multiple-operation forging processes. An adaptive iterative learning scheme is deployed to optimize the clustering results based on minimum classification error of the tree-structure classifier based on FSM. Real-world forging tonnage signals are used in the case study to demonstrate the proposed method.

Needle insertion is widely used in many clinical procedures such as biopsy, brachytherapy, and anesthesia. The success of these procedures highly depends on accurate guidance of the needle tip to a target area. Tissue deformation and needle deflection are the major factors that affect the accuracy and precision of needle insertion. In this paper, a new finite element model approach that couples both the tissue deformation and needle deflection is proposed based on the stick-slip theory. A method that adapts both the tissue mesh and needle mesh is developed. The interactive model of the needle and soft tissue is established and performed in MATLAB. The amounts of needle deflection and tissue deformation for both homogeneous and inhomogeneous tissue materials are obtained from the simulations. The model can easily be extended to 3 dimensional cases, and integrated into the real-time medical training simulation systems.

A new surface nano-patterning technique, the so-called UTAM nano-patterning approach, is reported here in this paper. Using the UTAM nano-patterning technique, large-scale arrays of highly ordered nanostructures (nanoparticles and nanoholes) in the range of square centimeters have been fabricated on substrates in a massive parallel way. The resulting nanostructures are characterized by highly defined and controllable size, shape, composition, and spacing of the nanostructures. By changing the structural parameters of the nanoparticles, the properties of the nanoparticle arrays can be tuned. This non-lithographic surface nano-patterning approach provides an efficient and low-cost alternative in fabricating large-scale ordered arrays of surface nanostructures.