Integration of failure mode, effects, and criticality analysis with multi-criteria decision-making in engineering applications: Part I – Manufacturing industry

Dabous, Saleh Abu; Ibrahim, Fakhariya; Feroz, Sainab; Alsyouf, Imad

doi:10.1016/j.engfailanal.2021.105264

Cited by 24 publications

(13 citation statements)

References 70 publications

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“…Our study was about providing additional practical measures to enhance the safety procedures of a smart manufacturing plant. Safety in a manufacturing plant remains a priority for every organization to ensure a smooth run of their production activities [ 58 ]. In an era of smart manufacturing and digitalization, the word safety is no longer limited to employees or operators working in the plant but to machine interacting and collaborating with them daily, such as collaborative robots (cobots) and autonomous robots.…”

Section: Resultsmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

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Section: Resultsmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Our study was about providing additional practical measures to enhance the safety procedures of a smart manufacturing plant. Safety in a manufacturing plant remains a priority for every organization to ensure a smooth run of their production activities [66]. In an era of smart manufacturing and digitalization, the word safety is no longer limited to employees or operators working in the plant but to machine interacting and collaborating with them daily, such as collaborative robots (cobots) and autonomous robots.…”

Section: Results Summary and Discussionmentioning

confidence: 99%

A Safety Response Mechanism for an Autonomous Moving Robot in a Small Manufacturing Environment using Q-learning Algorithm and Speech Recognition

Kiangala

Wang

2021

Preprint

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The industrial manufacturing sector is currently undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer a static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. Operators are subject to frequent safety inductions to react in emergencies but very little is done for robots. Our research proposes a safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from emergency stops (ESTOPs) button. The factory emergency signal can be given by an ESTOP or a voice command sent directly to the factory central controller: an S7-1200 Siemens programmable logic controller (PLC) in this experiment. We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations.

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“…Decision makers in complex situations to solve difficult problems, can use MCDM methods to make the right decision (Haseli & Sheikh, 2022). MCDM methods help to rank alternatives based on different criteria and due to its advantages, it has been used in engineering (Dabous et al, 2021, health care (Chen & Lin, 2022), economics (Seker & Kahraman, 2021), supply chain (Deepu & Ravi, 2021), waste management , Pamučar et al, 2021, Haseli & Jafarzadeh Ghoushchi, 2022, etc. Only some of research studies have illustrated the use of MCDM methods within the framework of green logistics.…”

Section: Mcdm Methodsmentioning

confidence: 99%

Risk Assessment of Green Logistics in Renewable Energy Transition under Spherical Fuzzy Environment

Bonab

Ghiaci

Ghoushchi

2023

Preprint

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Ensuring the sustainability of renewable energy transition logistics relies on the successful management of various risk factors. In this study, the evaluation of risk factors related to the implementation of green logistics (GL) in renewable energy transition is carried out. To address the limitations of conventional risk priority number (RPN), a modified Failure Mode and Effects Analysis (FMEA) approach that incorporates decision-makers' (DMs) perspectives DMs using a Spherical Fuzzy Set (SFs) is proposed. The use of SFs helps to mitigate the vagueness and imprecision inherent in DMs' opinions. The proposed framework leverages the Spherical Fuzzy Best-Worst Method (SF-BWM) to determine criteria weights and combines SFs with the Combinative Distance-Based Assessment (SF-CODAS) method to rank the recognized risk factors. The resulting critical risk factors provide valuable insights for effective risk management in GL implementation. The effectiveness of the proposed approach was validated through a comparison of its results with those obtained from other MCDM methods, as well as through conducting sensitivity analysis. The outcomes of this evaluation provide strong evidence of the efficacy of our proposed method.

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Integration of failure mode, effects, and criticality analysis with multi-criteria decision-making in engineering applications: Part I – Manufacturing industry

Cited by 24 publications

References 70 publications

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

A Safety Response Mechanism for an Autonomous Moving Robot in a Small Manufacturing Environment using Q-learning Algorithm and Speech Recognition

Risk Assessment of Green Logistics in Renewable Energy Transition under Spherical Fuzzy Environment

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