Sequence Planning Research Articles

Disassembly sequence planning using a Flatworm algorithm

Disassembly Sequence Planning (DSP) refers to a disassembly sequence based on the disassembly properties and restrictions of the product parts that meets the benefit goal. This study aims to reduce the number of changes in disassembly direction and disassembly tools so as to reduce the disassembly time. This study proposes a novel Flatworm algorithm that evolves through the regenerative properties of the flatworm. It is similar to the evolutionary concept of genetic algorithms, with evolution as the main idea, but without crossover, mutation or replication mechanisms in the evolutionary processes. Instead, it is based upon the characteristics of the growth, fracture and regeneration mechanisms of the flatworm. The Flatworm algorithm features a variety of disassembly combinations and excellent mechanisms to avoid the local optimal solution. In particular, it has the advantage of keeping a good disassembly combination from being destroyed. In this study, it is compared with two genetic algorithms and two ant colony algorithms and tested in three examples of different complexity: a ceiling fan, a printer, and 150 simulated parts. The solution searching ability and execution time are compared upon the same evaluation standard. The test results demonstrate that the novel Flatworm algorithm proposed in this study is superior to the two genetic algorithms and ant colony algorithms in solution quality.

Critical joint identification for efficient sequencing

Identifying the optimal sequence of joining is an exhaustive combinatorial optimization problem. On each assembly, there is a specific number of weld points that determine the geometrical deviation of the assembly after joining. The number and sequence of such weld points play a crucial role both for sequencing and assembly planning. While there are studies on identifying the complete sequence of welding, identifying such joints are not addressed. In this paper, based on the principles of machine intelligence, black-box models of the assembly sequences are built using the support vector machines (SVM). To identify the number of the critical weld points, principle component analysis is performed on a proposed data set, evaluated using the SVM models. The approach has been applied to three assemblies of different sizes, and has successfully identified the corresponding critical weld points. It has been shown that a small fraction of the weld points of the assembly can reduce more than 60% of the variability in the assembly deviation after joining.

A heuristic method with a novel stability concept to perform parallel assembly sequence planning by subassembly detection

PurposeParallel assembly sequence planning (PASP) reduces the overall assembly effort and time at the product development stage. Methodological difficulties at framework development and computational issues at their implementation made the PASP complex to achieve. This paper aims to propose a novel stability concept for subassembly detection to minimize the complexities in PASP.Design/methodology/approachIn this research, a heuristic method is developed to identify, represent and implement the stability predicate to perform subassembly detection and assembly sequence planning (ASP) at the further stages. Stability is organized into static, dynamic, enriched and no stability between the mating assembly parts. The combination of parts that possesses higher fitness is promoted to formulate the final solution about PASP.FindingsThe results obtained by applying the proposed concept on complex configurations revealed that stability predicate plays a dominant role in valid subassembly detection and final sequence generation further.Originality/valueThe value of the presented study lies in the three types of stability conditions and effective integration to existed ASP method. Unlike the existed heuristics in subassembly detection, the proposed concept identifies the parallel subassemblies during ASP.

Optimal Sequence Planning for Robotic Sorting of Recyclables From Source-Segregated Municipal Solid Waste

Abstract Sorting of recyclables from source-segregated municipal solid waste (MSW) stream is an essential step in the recycling chain in a material recovery facility (MRF) for waste management. Manual sorting of recyclables in an MRF is a highly hazardous operation for human health as well as time-consuming. Application of robotics for automated waste sorting can alleviate these problems to a large extent. The total sorting time depends upon the pick-and-place (PAP) sequence used in a robotic sorting system. In this context, the generation of optimal PAP sequence plan is a key challenge considering that it cannot be solved by an exhaustive search due to the combinatorial explosion of the search space. This paper reports an approach for generating optimal PAP sequence plan for robotic sorting of recyclables from source-segregated MSW stream in a system equipped with thermal-imaging technique. The PAP sequence generation is formulated as an optimization problem wherein the objective is to minimize the total sorting time. The formulated problem has been solved using a genetic algorithm (GA)-based approach. Numerical simulations as well as physical experiments using a 6 degrees-of-freedom (DOF) articulated manipulator have been performed to test and validate the developed optimal sequence generation algorithm. Results revealed an improvement of up to 4.28% speedup in total sorting time over that of randomly generated sequences. It is envisaged that the developed approach can substantially improve the sorting performance in an MRF.

Intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge

PurposeThe implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.Design/methodology/approachA spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.FindingsThe proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.Practical implicationsThe assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.Originality/valueThe spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

Disassembly Sequence Planning (DSP) Applied to a Gear Box: Comparison between Two Literature Studies

This work aims to analyze the characteristics and importance that design techniques for disassembly assume in the modern design phase of a mechanism. To this end, the study begins by considering a three-dimensional model of a gear motor, taken from the components of which the overall drawings are arranged and from the relief of those not available. Once the mechanism has been digitally reconstructed, the activity focuses on the study of the optimal disassembly sequence by comparing different methodologies, according to two evaluation criteria—minimizing the time taken and minimizing the number of tool changes necessary to complete the sequence. The main results of the work are (1) defining a standard methodology to improve disassembly sequence planning, (2) finding the best disassembly sequence for the specific component among the literature and eventually new methods, and (3) offering to the industrial world a way to optimize maintenance operations in mechanical products. Referring to the limitation of the present works, it can be affirmed that the results are limited to the literature explored and to the case study examined.

Algorithms for solving assembly sequence planning problems

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems.

A Novel Parallel Assembly Sequence Planning Method for Complex Products Based on PSOBC

Parallel assembly sequence planning (PASP) greatly impacts on efficiency of assembly process. In traditional methods, large scale of matrix calculation still limits efficiency of PASP for complex products. A novel PASP method is proposed to address this issue. To avoid matrix calculation, the synchronized assembly Petri net (SAPN) is firstly established to describe the precedence relationships. Associated with the SAPN model, the PASP process can be implemented via particle swarm optimization based on bacterial chemotaxis (PSOBC). Characterized by an attraction-repulsion phase, PSOBC not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than standard particle swarm optimization (PSO) algorithm. Finally, feasibility and effectiveness of the proposed method are verified via a case study. With different assembly parallelism degrees, optimization results show that assembly efficiency of the solution calculated by PSOBC method is 9.0%, 4.2%, and 3.1% better than the standard PSO process.

La escritura de docentes practicantes: sobre la enseñanza de Lengua y Literatura

Se describen prácticas de escritura desarrolladas por docentes practicantes durante el dictado de la asignatura Planificación y Práctica Docente del Profesorado en Enseñanza de la Lengua y la Literatura –ciclo–, de la Universidad Nacional de Río Negro. Los textos documentan decisiones en torno al registro de observaciones en campo, planificaciones de secuencias, métodos de análisis de prácticas, formas de reflexión sobre el trabajo docente. La sistematización de las prácticas, su registro y análisis permite reconocer una trama de significados negociados en las interacciones cotidianas, compartida en un aula virtual donde convergen múltiples escenarios del sistema educativo. El análisis de los textos revela un problema común: la dificultad de articulación conceptual y metodológica de objetos de enseñanza presentados de manera disgregada en los diseños curriculares vigentes que trazan la política lingüístico-educativa para nivel secundario en Argentina.

4R Program results: What is the impact of 4R care delivery model on patient self-management and which patients benefited?

e19211 Background: We previously proposed a 4R model of teamwork and patient self-management (pSM) in cancer care (NCI ASCO Teams Project, Trosman JOP 2016). 4R (Right Info / Care / Patient / Time) enables patient and care team to manage complex care with an innovative 4R Care Sequence plan, including a novel visual feature describing timing and sequence of care. We report final results of a program which tested 4R at 5 safety net and 5 non safety net US centers from 2016 to 2019. Methods: Patients with stage 0-III breast cancer received 4R plans (4R Cohort). We surveyed 4R cohort and a historical control cohort of patients receiving care at same centers pre-4R. We assessed the usefulness of 4R to the 4R cohort and the impact on pSM in 4R cohort compared to historical cohort. Results: Survey response rates: 63%, 422/670 (4R cohort); 47%, 466/992 (control). 4R significantly increased the composite pSM score and 5 of 7 pSM metrics vs control (Table). The increase was not influenced by patient age, stage or whether treated at safety net site. pSM scores increased in 4R vs control cohort to a similar extent for patients < 65 years old (74% vs 51%, p = .0001) as for patients ≥ 65 years old (78% vs 57%, p = .0002). pSM improved similarly for patients with stage 0 or I breast cancer (77% vs 56%, p = .0001) as for patients with stage II or III breast cancer (72% vs 54%, p = .0001). Safety net patients showed pSM increase (77% vs 51%, p = .0001) similar to non safety net patients (74% vs 58%, p = .0002). Within the 4R cohort, 80% found 4R useful for organizing care and 70% found the novel visual feature useful to manage care timing and sequence. Usefulness was similar for age groups and stages, but higher for safety net than non safety net patients (88% vs 74%, p = .0008). Conclusions: 4R markedly improved patient self-management in early breast cancer across age groups and cancer stages, but further enhancements are needed to benefit as many patients as possible. 4R benefits in safety net setting indicate that 4R may reduce disparities. [Table: see text]

4R Program results in breast cancer: the impact of 4R Care Delivery Model on timing and sequence of guideline recommended care.

573 Background: We previously proposed a 4R care delivery model which enables patients and care teams to manage timing and sequence of interdependent, time sensitive care with a novel multimodality 4R Care Sequence plan (NCI ASCO Teams Project; Trosman, JOP 2016). We report final results of a program which tested 4R at 10 US centers (4 academic and 6 non-academic) from 2016 to 2019. Methods: 4R Sequences were provided to stage 0-III breast cancer patients (4R cohort, N = 422) at participating centers. Analyses of clinical and patient-reported data compared the 4R cohort to a historical control cohort of patients who received care pre-4R at the same centers (N = 466). Results: We significantly improved 5 guideline recommended referral metrics and 4 referral completion metrics indicating receipt of care by patients who were referred (Table). Although significantly increased, referrals to dental visit and smoking cessation before treatment remained low (< 20% and < 10% respectively). Patient survey comments indicated that insufficient lead time to quit smoking or obtain fertility consult before cancer treatment was a key barrier for completing these referrals. Conclusions: 4R markedly improved referral and receipt of interdependent guideline recommended breast cancer care for most metrics. Future 4R program should optimize the timing of referrals within the 4R Care Sequence to allow sufficient time for smoking cessation and fertility care before treatment initiation. [Table: see text]

Asynchronous parallel disassembly sequence planning for multi-manipulator based on improved shuffled frog leaping algorithm

To solve the problem of low disassembly efficiency of complex products, an asynchronous parallel disassembly sequence planning method of multi-manipulator based on improved shuffled frog leaping algorithm was proposed. Depending on the characteristics of this method, the disassembly manipulators are taken as the research object, and the minimum completion time of disassembly is taken as the optimization objective. Based on the basic disassembly time of parts, the disassembly preparation time caused by the change of disassembly tools and directions is considered, so that the objective function evaluation model is more realistic. Then, the mathematical model of asynchronous parallel disassembly sequence planning is constructed and the disassembly information modeling method is optimized. We proposed a new method based on the fastener constraint matrix and the part constraint matrix to build the information model of product disassembly. Based on this model, the detachable conditions are derived and the feasible disassembly sequence is obtained. In order to improve the performance of the proposed algorithm, a double-link encoding method and an efficient decoding operator is proposed. The adjustment orders which based on adjustment position and the substitution formulas are constructed to carry out the new segmental local evolution of frog individuals, and the update formula of frog position is reconstructed. Furthermore, the two-point mutation operator is added to the global information exchange strategy to enhance the search ability of the algorithm. The proposed algorithm is applied to a screw lifting mechanism and four products of varying complexity, and the results are compared with two algorithms. The results show that the proposed method can solve disassembly problems of different scales and is especially effective in solving large-scale disassembly problems.

Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation

Previous neural models on open-domain conversation generation have no effective mechanisms to manage chatting topics, and tend to produce less coherent dialogs. Inspired by the strategies in human-human dialogs, we divide the task of multi-turn open-domain conversation generation into two sub-tasks: explicit goal (chatting about a topic) sequence planning and goal completion by topic elaboration. To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). Specifically, for the first sub-task, the upper-layer policy learns to traverse a knowledge graph (KG) in order to plan a high-level goal sequence towards a good balance between dialog coherence and topic consistency with user interests. For the second sub-task, the middle-layer policy and the lower-layer one work together to produce an in-depth multi-turn conversation about a single topic with a goal-driven generation mechanism. The capability of goal-sequence planning enables chatbots to conduct proactive open-domain conversations towards recommended topics, which has many practical applications. Experiments demonstrate that our model outperforms state of the art baselines in terms of user-interest consistency, dialog coherence, and knowledge accuracy.

Investigation into the Metabolic Limitations of Alpha Conotoxins as Drugs in the Gastrointestinal Tract

Peptides isolated from cone snails, known as conotoxins, have potential in treating neuropathic pain. Peptides often lack efficacy as drugs due to poor pharmacokinetic profiles despite displaying high potency and selectivity. Conotoxins present significant sequence diversity, but little information exists about their pharmacokinetic properties. We hypothesize that this diversity will correlate with dynamic pharmacokinetic profiles, with some peptides exhibiting increased stability. In this study, we aimed to gain a basic understanding of the stability of the conotoxins α‐IMI and α‐GI in the stomach and intestines. Both have different primary sequences despite serving as antagonists of the acetylcholine receptor, which is a potential target in cancer, chemotherapy‐induced allodynia and possibly pain associated with Crohn’s disease. In order to study these peptides, novel mass spectrometry (LC‐MS/MS) methods were developed incorporating multiple reaction monitoring scans for each peptide in simulated gastric and intestinal fluids. The quantitation range of our method was 0.5–5 μg/mL, which is 20 times more sensitive than traditional methods. To compare gastrointestinal metabolic stability, each peptide was incubated in simulated gastric fluid and intestinal fluid and their stability was measured using our LC‐MS/MS method at times ranging from 0–90 minutes. Both peptides displayed a high amount of stability in gastric fluid, as no significant loss of either peptide was determined after 90 minutes. In intestinal fluid IMI degraded rapidly (t1/2= ~6 minutes), whereas α‐GI displayed far more stability in the same environment (t1/2= ~257 minutes). α‐GI incorporates a prominent helix in its structure which is likely responsible for its increased stabilization in comparison to IMI. Considering the differences in the metabolic stability of these peptides, further study of conotoxin sequences may result in the identification of stable molecular scaffolds. In the future, we plan to pinpoint what particular features are responsible for metabolic stability or susceptibility across more conotoxins, such as differences in sequence, structure and rigidity and plan to use this information to produce analogs with increased stability within the gastrointestinal tract.Support or Funding InformationSource of Research Support: This work was supported by the Pacific University Research Incentive Grant Program and an ASPET Summer Undergraduate Research Fellowship (SURF) award at Pacific University.

A decision support methodology for integrated machining process and operation plans for sustainability and productivity assessment

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost.

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data trans-mission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Di-minishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy effi-ciency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collabo-rations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

MRI of female genital tract congenital anomalies: European Society of Urogenital Radiology (ESUR) guidelines

ObjectiveTo develop imaging guidelines for the MR work-up of female genital tract congenital anomalies (FGTCA).MethodsThese guidelines were prepared based on a questionnaire sent to all members of the European Society of Urogenital Radiology (ESUR) Female Pelvic Imaging Working Group (FPI-WG), critical review of the literature and expert consensus decision.ResultsThe returned questionnaires from 17 different institutions have shown reasonable homogeneity of practice. Recommendations with focus on patient preparation and MR protocol are proposed, as these are key to optimised examinations. Details on MR sequences and planning of uterus-orientated sequences are provided.ConclusionsThe multiplanar capabilities and soft tissue resolution of MRI provide superb characterisation of the wide spectrum of findings in FGTCA. A standardised imaging protocol and method of reporting ensures that the salient features are recognised, contributing to a correct diagnosis and classification of FGTCA, associated anomalies and complications. These imaging guidelines are based on current practice among expert radiologists in the field and incorporate up to date information regarding MR protocols and essentials of recently published classification systems.Key Points• MRI allows comprehensive evaluation of female genital tract congenital anomalies, in a single examination.• A dedicated MRI protocol comprises uterus-orientated sequences and vaginal and renal evaluation.• Integration of classification systems and structured reporting helps in successful communication of the imaging findings.

An interference discrimination method for assembly sequence planning and assembly simulation

PurposeThe purpose of this paper is to discriminate fake interference caused by polygonal approximation so as to achieve accurate assembly sequence planning and assembly simulation.Design/methodology/approachAn approximation zone model is proposed to formulate polygonal approximation. Fake interference is discriminated from hard interference by evaluating if polygonal models intersect within corresponding approximation zones. To reduce the computation, the surface-surface, surface-end face and end face-end face intersection test methods have been developed to evaluate the intersection and obtain collision data. An updated collision detection algorithm with this method is presented, which is implemented by a system named AutoAssem.FindingsThis method has been applied to a set of products such as a valve for assembly interference matrix generation, static and dynamic collision detection. The results show that it ensures the accuracy of assembly sequence planning and assembly simulation for polygonal models.Practical implicationsThis method facilitates assembly design in the virtual environment with polygonal models. It can also be applied to computer aided design systems to achieve quick and accurate collision detection.Originality/valueFake interference between polygonal models may result in serious errors in assembly sequence planning and assembly simulation. Assembly zone model and novel polygon intersection verification methods have been proposed to effectively tackle this problem. Compared to current methods, this method considers valid penetration direction and approximation difference, does not need to process complicated auxiliary data and can be easily integrated with current collision detection methods.

Distinct cortical activity patterns in Parkinson\u2019s disease and essential tremor during a bimanual tapping task

BackgroundParkinson’s disease (PD) and essential tremor (ET) are neurodegenerative diseases characterized by movement deficits. Especially in PD, maintaining cyclic movement can be significantly disturbed due to pathological changes in the basal ganglia and the cerebellum. Providing external cues improves timing of these movements in PD and also affects ET. The aim of this study is to determine differences in cortical activation patterns in PD and ET patients during externally and internally cued movements.MethodsEleven PD patients, twelve ET patients, OFF tremor suppressing medication, and nineteen age-matched healthy controls (HC) were included and asked to perform a bimanual tapping task at two predefined cue frequencies. The auditory cue, a metronome sound presented at 2 or 4 Hz, was alternately switched on and off every 30 s. Tapping at two different frequencies were used since it is expected that different brain networks are involved at different frequencies as has been shown in previous studies. Cortical activity was recorded using a 64-channel EEG cap. To establish the cortical activation pattern in each group, the task related power (TRP) was calculated for each subject. For inter-groups analysis, EEG electrodes for divided into 5 different areas.ResultsInter-group analysis revealed significant differences in areas responsible for motor planning, organization and regulation and involved in initiation, maintenance, coordination and planning of complex sequences of movements. Within the area of the primary motor cortex the ET group showed a significantly lower TRP than the HC group. In the area responsible for combining somatosensory, auditory and visual information both patient groups had a higher TRP than the HC group.ConclusionsDifferent neurological networks are involved during cued and non-cued movements in ET, PD and HC. Distinct cortical activation patterns were revealed using task related power calculations. Different activation patterns were revealed during the 2 and 4 Hz tapping task indicating different strategies to execute movements at these rates. The results suggest that a including a cued/non-cued tapping task during clinical decision making could be a valuable tool in an objective diagnostic protocol.

Geometry-based Bend Feasibility Matrix for bend sequence planning of sheet metal parts

ABSTRACTProcess planning for sheet metal bending involves the determination of a near-optimal bend sequence for a given part. The problem is complex since the search space of possible solutions is factorial with respect to the number of bends. In this paper, a two-stage algorithm is described that allows for the quick identification of a near-optimal bend sequence for a given part and set of tools. In the first stage, a Bend Feasibility Matrix is constructed to map the entire search space by taking a geometric approach to the problem. The matrix helps to quickly establish whether the part can be manufactured using the given set of tools. The second stage uses best-first search (graph) algorithm to identify the bend sequence. During search, infeasible sequences are never evaluated and expensive collision tests are not done since the necessary computations are already done in the first stage. Performance of the proposed algorithm is compared with that of genetic algorithm and it is demonstrated that the best-first search algorithm is better than genetic algorithm (GA) to solve the bend sequencing problem.