Knowledge Discovery Approach for Automated Process Planning

Schuh, Guenther; Prote, Jan-Philipp; Luckert, Melanie; Hünnekes, Philipp

doi:10.1016/j.procir.2017.03.092

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…The primary method of process mining involves the discovery of pattern with the aid of event logs. A process discovery approach takes an event log as input for knowledge retrieval and generates a scratch model stripped of any prior knowledge/information [31], [32].…”

Section: Process Mining With Event Logsmentioning

confidence: 99%

Challenges and Use Cases of Process Discovery in Process Mining

Faizan¹

2020

IJATCSE

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To date, the amount of data collected are significant and the growth is exponential in various fields over the last decades. Most companies typically address the issue of storage system capacities and therefore such situation creates the issue handling "big data." However, most companies sometimes may find it a challenge to obtain useful knowledge from a large amount of information. As such, numerous organizations are expected to tackle the data-related difficulties. In contrast, there is a necessity to improve and support business processes in progressively changing conditions. Generally, it is possible to analyses the available data and propose improvements because some tools and methods exist to facilitate such cause. Process mining may offer effective approaches to complement the business process. Process mining allows organizations to take advantage of the information within the systems, but can also be used to check process conformance, predict execution challenges, and identify bottlenecks. For instance, healthcare has rapidly changed over decades of research and development. Through scientific discovery, more diseases can be treated. However, it also affects the feasibility of health institutions to accommodate more complicated health treatment processes and systems effectively. Indeed, many solutions exist to cure a particular disease, since machines are becoming further complex, requiring medical staff to be equipped with necessary training. Also, it significantly increases the cost of health care. This paper presents the insights of process mining, highlighting the possible approaches used to gather and analyses the data using feasible method in process mining including real discovery processes. The paper also discusses the challenges of process mining.

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Section: Process Mining With Event Logsmentioning

confidence: 99%

Challenges and Use Cases of Process Discovery in Process Mining

Faizan¹

2020

IJATCSE

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“…The activities can be statistically identified via planning algorithms while the level of accuracy and comprehensiveness is increased. The use of data mining, fuzzy logic and decision support enables automated generation of the process plan [3]. But this kind of approach is still to be further developed.…”

Section: Literature Reviewmentioning

confidence: 99%

Workpiece Classification Criteria in Automated Process Planning

Trstenjak

Ćosić

Antolić³

2019

Teh. vjesn.

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The classification of products in automated process planning has been researched for more than twenty years now. Automated process planning is a crucial part of computer-aided process planning (CAPP) which enables the user to generate a manufacturing process plan automatically. It is one step towards autonomous process planning required by the fourth industrial (r)evolution. The paper presents process-based software developed for classifying technological properties of a workpiece based on a CAD 3D model. The accessibility of data will be discussed as well as the classifying and working principles of the software that enables both automatic creation of the required manufacturing processes and operation sequencing. The special classification/workpiece coding system will be presented and explained, which is defined by the data from the 3D model that can be reachable or with limited reachability.

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“…For the implementation of CAPP, it is important to define the components and their characteristics geometrically, such that they can be conveniently read by the proposed system (Schmidt, 2015). For this purpose, component information should be represented by features, which comprise the geometric description of the component elements and their spatial relations (Schuh et al, 2017). Feature extraction and feature recognition systems are used to identify part features and their attributes from the CAD file (Kamrani et al, 2015).…”

Section: Proposed Computer-aided Process Planning Framework For Hybrid Processmentioning

confidence: 99%

Novel dynamic CAPP system for hybrid additive–subtractive–inspection process

Abdulhameed

Al-Ahmari

Ameen

et al. 2018

RPJ

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Purpose Hybrid manufacturing technologies combining individual processes can be recognized as one of the most cogent developments in recent times. As a result of integrating additive, subtractive and inspection processes within a single system, the relative benefits of each process can be exploited. This collaboration uses the strength of the individual processes, while decreasing the shortcomings and broadening the application areas. Notwithstanding its numerous advantages, the implementation of hybrid technology is typically affected by the limited process planning methods. The process planning methods proficient at effectively using manufacturing sources for hybridization are notably restrictive. Hence, this paper aims to propose a computer-aided process planning system for hybrid additive, subtractive and inspection processes. A dynamic process plan has been developed, wherein an online process control with intelligent and autonomous characteristics, as well as the feedback from the inspection, is utilized. Design/methodology/approach In this research, a computer-aided process planning system for hybrid additive, subtractive and inspection process has been proposed. A framework based on the integration of three phases has been designed and implemented. The first phase has been developed for the generation of alternative plans or different scenarios depending on machining parameters, the amount of material to be added and removed in additive and subtractive manufacturing, etc. The primary objective in this phase has been to conduct set-up planning, process selection, process sequencing, selection of machine parameters, etc. The second phase is aimed at the identification of the optimum scenario or plan. Findings To accomplish this goal, economic models for additive and subtractive manufacturing were used. The objective of the third phase was to generate a dynamic process plan depending on the inspection feedback. For this purpose, a multi-agent system has been used. The multi-agent system has been used to achieve intelligence and autonomy of different phases. Practical implications A case study has been developed to test and validate the proposed algorithm and establish the performance of the proposed system. Originality/value The major contribution of this work is the novel dynamic computer-aided process planning system for the hybrid process. This hybrid process is not limited by the shortcomings of the constituent processes in terms of tool accessibility and support volume. It has been established that the hybrid process together with an appropriate computer-aided process plan provides an effective solution to accurately fabricate a variety of complex parts.

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Knowledge Discovery Approach for Automated Process Planning

Cited by 25 publications

References 25 publications

Challenges and Use Cases of Process Discovery in Process Mining

Challenges and Use Cases of Process Discovery in Process Mining

Workpiece Classification Criteria in Automated Process Planning

Novel dynamic CAPP system for hybrid additive–subtractive–inspection process

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